Exhibition

Meals

Walk the exhibition space to grab your quick breakfast before general session.

Registration Desk Open

General Session

As the Transportation industry evolves and innovates to deliver upon the goals of clean and safe mobility, efficient engineering processes are critical.   A key enabler in shortening and making the engineering processes more efficient is simulation. Visionary automotive companies know how their world-changing ideas will perform well before prototypes and production through the use of simulation and model-based systems engineering.  No longer relegated to CAE departments, simulation has changed tremendously over the past few years, empowering every engineer to innovate faster than ever before. Learn how the latest technologies in AI/ML, physic-based numerics and cloud services have transformed workflows to deliver the latest innovations in electrification, ADAS/AD, SDV, and vehicle attributes.

General Session

The use of numerical tools and virtual analysis plays a critical role in the design and optimization of Formula 1 cars.  To deliver race-winning performance on the track, Oracle Red Bull Racing relies on virtualized design and development to reproduce real-world scenarios and re-create the complex physical environment off the track. This continuous and rapid process ensures the car is optimized for maximum performance at each racetrack. 

Gennaro will highlight how Oracle Red Bull Racing leverages Ansys technologies for aerodynamic development and thermal management to the virtual testing of impact structures. Through the accuracy and fidelity in reproducing virtual testing environments, Ansys enables the Team to reduce costs and time necessary for physical prototypes, enabling quicker iterations and innovations in the fast-paced world of Formula 1. The Innovation Partnership with Ansys is crucial for Oracle Red Bull Racing to maintain the competitive edge in Formula 1 and to deliver exceptional performance on and off the track. 

General Session

General Session

Meals

Electrification and ADAS

Electronics and Lighting

Safety and Systems

Digital Engineering

Crash I

Seats play a crucial role in ensuring the safety of occupants during crashes. Seat integrity is a key factor in minimizing occupant injuries. In this study, the third-row seat of a full-size SUV was occu-pied by three Humanetics Hybrid III 50th percentile dummies in a high-fidelity model. The front crash acceleration of FMVSS208 (56KPH rigid barrier crash) was applied to assess the seat's integrity. Additionally, to account for the impact of parameter variation, a comprehensive set of variations was applied using Ls-Opt. This aimed to investigate the robustness of seat integrity performance considering parameters such as H-point, material properties, part thickness, and acceleration variation.

Forming I

Stamping simulation includes: 1) forming simulation – modeling large plastic deformation during in-die forming operation and predict failures, such as wrinkling, fracture, and springback; 2) out of die handling simulation – modeling handling operation and predict dynamic effect caused surface and dimensional defects. Using rich application programming interface (API), scripts have been developed to streamline the modeling and failure evaluation of stamping simulation in LS-PrePost. The developments will be demonstrated through two case studies: 1) realization a damage-based failure index tool in hot stamping fracture prediction; 2) Graphic user interface (GUI) for parameter inputs and automatic modeling drop test. In addition to more than 90% efficiency improvement, the API supported scripts also democratize complex simulation modeling and reduce human errors.

Occupant and Pedestrian Safety I

Virtual testing applications are rapidly growing in the ever-changing landscape of automotive mobility applications. The key drivers to the transformation in mobility are technological changes for the future of mobility such as ADAS (Advanced Driver Assistance System), AV (Autonomous Vehicle). These technological evolutions will require assessment of a variety of safety scenarios with virtual assessment tools and would not be practical with physical testing only. One such virtual assessment tool in roadmap by consumer testing agencies like Euro NCAP and C-NCAP and other regulatory bodies is the Finite Element (FE) Human Body Model (HBM). The automotive industry must adopt HBM and other virtual safety tools to meet the requirements set forth by the consumer agencies and regulatory frameworks. The FE HBM representing the human is the most promising virtual assessment tool to support virtual testing in conjunction with anthropometric test devices (ATD) used for virtual and physical testing. HBM needs to fulfil the requirements for virtual testing by demonstrating compliance and should also meet the need of industry in product development applications. This requires HBMs to be omnidirectional biofidelic, user friendly (easy-to-use), robust, computationally efficient (fast), and certifiable by the virtual testing requirements from various agencies. HBM should also be capable of assessing injuries relevant to the accident field data in the most comprehensive manner to elevate the vehicle safety features.  Another requirement is the ease of positioning the FE- HBM in the virtual testing environment in an effective manner. With the Accelerated Positioning Tool (APT-C), this process is streamlined and automated, by employing advanced methodologies such as Optimization and Machine Learning. Through these techniques, precise marker-based positioning of HBM models is achieved which includes model position, seat-squash, and belt routing. Additionally, the tool incorporates spine-posture calculations derived from data obtained in the UMTRI volunteer study, further enhancing its accuracy and applicability. The developed HBM-C and APT-C show promising capabilities to be used for virtual testing and product development aimed to improve vehicle safety applications and grow to other industries like aviation, defense, space, and sports (NFL, NASCAR) applications. This proactive approach to safety assessment is essential in the dynamic landscape of technological advancements and evolving safety regulations.

Simulation Methods I

In vehicle development CAE plays crucial role in arriving at optimum structural design to meet various vehicle performance targets in different domain such as Crash, NVH, Durability etc. Accurate CAE methodology can aid in reducing the number of physical tests & reducing overall vehicle development time. However, there are instances where there are gaps observed between test results and CAE predictions. These gaps get amplified in crash simulations as the event is highly dynamic and non-linear behavior simulation is always challenging. In order to enhance CAE methodology, it was decided to incorporate the effect of manufacturing and testing variations in crash CAE simulations. Manufacturing process accounts for variations due to inherent variation in material properties, spot weld nugget diameter, manufacturing processes such as stamping etc. whereas Physical Testing houses variation in barrier positions, test speed etc. within specified tolerance defined by regulatory bodies. These variations affect structural performance and negating these issues in early design phase will help to arrive at robust structural design. In this paper, the CAE based approach for accommodating manufacturing and testing variation in crash CAE simulation for arriving at robust BIW design is described. In the current work, unsupervised machine learning based CAE approach is used to identify variations in structural performance arising out of manufacturing and testing variations. This paper also describes accuracy verification of this CAE approach based upon its comparison with quasi-static experimental test.

Digital Engineering

Automated physics-based simulation, bolstered by advancements in AI and ML, stands at the forefront of revolutionizing product development for innovative companies. This transformative technology integrates sophisticated algorithms to streamline design studies and optimization processes, significantly enhancing efficiency and accuracy in product development cycles. By leveraging AI's ability to rapidly iterate through vast datasets and simulate complex physical phenomena, companies can expedite prototyping, reduce costs associated with physical testing, and accelerate time-to-market for novel products. This paradigm shift not only fosters greater innovation by enabling engineers to explore unconventional designs with confidence but also cultivates a culture of continuous improvement and agility within organizations. Ultimately, automated physics-based simulation empowered by AI and ML represents a pivotal advancement that redefines how innovative companies conceive, refine, and bring their ideas to fruition in today's competitive landscape.

Ansys Intelligent Engineering Projects, AIEP, is a holistic solution that builds on physics-based simulation capabilities, fully automated and paired with intelligent design study techniques to leverage both classic statistics and modern data science techniques. This allows a quick adjustment of new designs to newly upcoming or changing requirements by leveraging legacy information through AI. To achieve this solution, technical domains and capabilities like physics-based simulation, automation, machine learning and optimization are assembled perfectly deploy a scalable web-based application through a data management platform to ensure data integrity, oversight, and governance.

Electronics and Lighting

The automotive industry is undergoing a transformative shift towards Software-Defined Vehicles (SDVs), where vehicle functionalities are increasingly driven by software. Managing the scalability, flexibility and interoperability of systems across diverse vehicle models is critical for successful deployment. This challenge involves the development of new hardware architectures and electronics as well as new software features. Ansys provides a comprehensive and integrated suite of tools and solutions that address the engineering challenges of software-defined vehicle development. Solutions for model-based system engineering, electronics design from chip to system, software development using real-time plant models and virtual validation of customer features. In this presentation, we will discuss how Ansys solutions enable OEMs and suppliers to partner to design, validate, and deliver the vision of the SDV.

Safety and Systems

Digital Engineering is in the focus of many companies to improve their product development. It is concerned with the application of digital methods and tools covering the complete lifecycle of products, from conception through to operation and maintenance. Ansys is going to provide our customers with a digital engineering experience covering important aspects in key areas like MBSE, model-based software development, model-based compliance, and engineering simulation. In the talk our approach to Digital Engineering is discussed with a focus on safe software and compliance to safety and cybersecurity regulations.

Electrification and ADAS

Ansys ConceptEV is a new innovative cloud-based design and simulation platform for the design of EV powertrains. Engineers can collaborate on a shared system simulation connected to requirements from the start of the design process. ConceptEV provides a model-based approach to optimizing the powertrain system & components with rapid evaluation of different powertrain configurations and component design choices using innovative simulation techniques.

Crash I

Demands for ever increasing efficiency of automotive crash analysis have continued to rise in the drive to reduce automotive development costs. When major design changes are required to satisfy product performance late in the development process, significant cost and time are required to implement them. To alleviate this, automotive manufacturers have adopted the concept of "front-loading" to identify problems early-on in the development process. The earlier issues in the production phase can be identified, the more efficiently and effectively development can be performed. JSOL Corporation has developed a new method for modelling automotive body structures to simulate crash analysis in the early stage of design that employs Hughes-Liu beam elements with arbitrary cross-sectional geometry. Then JSOL Corporation has released a new modelling tool J-SimRapid that can easily create the reduction models. This tool enables front-loading crash safety analysis in the conceptual design phase. Furthermore, it can also reduce large-scale models in the detailed design phase. Consequently, J-SimRapid makes overall automotive design phases more efficient. In this presentation, the new model reduction method will be introduced along with examples of reduced model analysis by J-SimRapid.

Forming I

Achieving a sheet metal part within dimensional tolerance without the need for tool recutting is the ultimate goal of every forming simulation. Several key factors are essential to reach this goal: an accurate forming simulation with precise material descriptions, correct friction values, and an accurate binder model to determine the correct material flow and stress state of the part after forming. Additionally, an accurate springback calculation is required, considering the part clamped in the measurement device and the effect of gravity. With these results the new tool geometry has to be determined to compensate the springback of the sheet metal part.

Occupant and Pedestrian Safety I

For airbag deployment simulation, CPM is widely used due to its usability and cost performance. On the other hand, it has some difficulty in representing the gas flow into the narrow space like curtain side airbags. CPG is a new method which solves the Navier-Stokes equation for a space discretized by particles, and is expected to be able to solve his problem by representing pressure propagation and its effect to the fabric more accurately. This paper shows the preliminary validation results comparing test results and CPM for several airbags. CPG shows some better behaviors compared to conventional methods and possibilities to apply to the airbag deployment simulations.

Simulation Methods I

Accurate simulation of solder reflow processes during Integrated Circuit (IC) packaging process is essential for ensuring the structural reliability and thermal behavior of electronic devices. In this presentation, a new approach utilizing the adaptive Incompressible Smooth Particle Galerkin (ISPG) method in LS-DYNA is presented. This method aims to simulate solder reflow processes during Flip Chip Ball Grid Array (FCBGA) die attachment processes and overall IC assembly. The ISPG method combines the advantages of incompressible fluid simulations and SPG (particle) techniques using a Lagrangian approach. Existing methods such as Surface Evolver or Fluid-Structure Interaction (FSI) techniques often struggle to accurately represent the interactions between solid composites and fluid-like molten solder balls during their reflow process. They also face challenges in maintaining efficiency and computational feasibility when simulating complete packages. This work demonstrates the effectiveness of the new approach called Adaptive ISPG which has been recently implemented in LS-DYNA.  The ISPG technique utilizes a Lagrangian particle-based approach to solve the Navier-Stokes equation, specifically designed for modeling molten solder balls used in IC packaging. This technique seamlessly integrates with the structural physics capabilities within the LS-DYNA solver. The evolution of solder ball shapes is determined by the material's surface tension and the boundary conditions set by the structure. The adaptive feature of ISPG further enhances its capabilities, enabling improved flow behavior in narrow gaps and around sharp corners, which provides more accurate and realistic simulations of the solder. This novel workflow offers significant potential in optimizing new parameters of packaging assembly process through simulation-guided adjustments, enhancing the reliability and performance of IC packages.

Electrification and ADAS

One of the biggest impediments to widespread adoption of electric transportation is battery cost. Performance, safety, and hitting time-to-market all remain challenges for the EV design cycle along with an evolving technological, supply chain and regulatory landscape for batteries. Tight product cycles make it impossible to use the build, test, fix approach against a multitude of battery design choices that exist at the material and component level. Simulation is therefore critical in EV and battery product development. Cell chemistry/format, manufacturing quality, cooling system and mechanical design significantly impact vehicle level attributes such as safety and performance with some tradeoffs involved. All of the components then need to be integrated and system performance validated against real-life usage conditions. On the safety side with new mandates that require passenger warnings around thermal runaway, propagation resistance becomes important whether thermally triggered or mechanically triggered. This presentation will cover an overview of Ansys battery solutions including key challenges in battery manufacturing, safety and how system level performance and what if failure scenarios can be investigated.

Electronics and Lighting

Automotive printed circuit boards (PCBs) are the heart of all modern vehicles as they play a critical role in all advanced electronic features including autonomous driving systems, safety, infotainment, and connectivity solutions. In this presentation we will be covering the latest advancements in electronics, thermal and mechanical simulations of PCBs using Ansys tools. The presentation will highlight newer technologies to create and solve complex PCB assemblies including connectors, flex cables and housing to investigate the performance of the complete sub system and not the components in isolation, which is critical for automotive applications. Full vehicle simulations will also be presented showing virtual electromagnetic compatibility (EMC) of PCBs installed inside the vehicle along with the electrical/electronics architecture.

Safety and Systems

An overview of the new ISO Standard: ISO/PAS 8800 (Road vehicles Safety and artificial intelligence). This standards defines safety-related properties and risk factors impacting the insufficient performance and malfunctioning behavior of Artificial Intelligence (AI) within a road vehicle context. Emphasis on the key elements of this standard such as deviation of safety requirements, data quality and completeness, SW architecture measures for the control and mitigation of failures and the evidence required to support an assurance argument for the overall safety of an automotive system that employes AI. Real life challenges to the implementation of this standard will be also covered for selected design areas. Activities I am involved in at both Aptiv and ISO/SAE/SCC to address these challenges are also highlighted.

Digital Engineering

Capturing design insights earlier in the design process in a critical driver to shift-left initiatives, enabling increased innovation, reduced cycles times, and improved quality through earlier issue detection. Attend this session to learn how Ansys Discovery is leveraging native GPU computing and a next-generation integrated user experience to break down the barrier to upfront simulation across multiple automotive application areas, providing instantaneous design guidance where and when you need it most. Stop waiting for results and start acting on them.

Crash I

Virtual validation activity performed to fullfill all the ECE-R21 / FMVSS 201 standards requirements. Deep investigation on how dashboard stiffness will impact all the parameters to be checked like glass survival, deceleration, reaction force, gap and flush.

Forming I

The development of hot forming functionality in Ansys Forming has been a collaborative effort with BENTELER Automotive as our industrial partner.

Hot forming simulation necessitates accurate computation of both temperature distribution and material deformation of the sheet metal part. Historically, setting up a hot forming simulation in LS-DYNA demanded extensive finite element (FE) expertise, posing a significant barrier for many companies.

To address this, we collaborated with BENTELER to define the requirements for an intuitive and user-friendly graphical user interface (GUI). Our aim was to streamline the simulation setup process so that users only need a comprehensive understanding of the hot forming process, rather than deep FE expertise.

In this presentation, we will demonstrate how we achieved this goal with Ansys Forming. We will cover the defined specifications, underlying assumptions, and the implementation process in both Ansys Forming and LS-DYNA. Additionally, we will showcase a case study from BENTELER, detailing the simulation setup and results. Finally, we will provide an outlook on future development plans for hot forming simulations with Ansys Forming.

Occupant and Pedestrian Safety I

The authors have previously published a paper characterizing a laminated glass pane to simulate the delamination and fragmentation response of glass in a blast event. In this study, a computational model in LS-Dyna was developed and correlated with physical testing of laminated safety glass panes subjected to blast loads. To generate an accurate model, validation of the component parts including Polyvinyl Butyral (PVB), adhesion and glass that make up the laminated safety glass was completed.

In a new study, the authors using this model undertook several pedestrian head impact tests on a windshield in accordance with the Euro New Car Assessment Program (NCAP) and UN ECE R127 to determine its suitability. Building on this model, the glass material was further refined to correlate with previous physical tests completed with an emphasis to correlate against the head injury criterion (HIC) number as well as match the acceleration vs. time history curve. Following refinement, more physical testing was completed which was compared to the model and showed good agreement between the physical testing and model. Furthermore, when compared to the old approach to simulate pedestrian head impact on a windshield, the new approach demonstrated better membrane action following glass cracking which resulted in better correlation to the acceleration vs. time history curve. The approach and results of the study are presented within this paper.

Simulation Methods I

Accurate simulation of solder joint shapes and dimensions during the Flip Chip Ball Grid Array (FCBGA) packaging reflow process is crucial for ensuring signal integrity and structural reliability in electronic devices. This presentation introduces a novel approach utilizing the adaptive Incompressible Smooth Particle Galerkin (ISPG) method in LS-DYNA.   Our methodology involves calibrating the ISPG model using NVIDIA's FCBGA, where a set of optimal parameters was identified. To validate the calibrated model, various FCBGA designs were simulated and compared against experimental solder joint cross-sectional data. The results demonstrated a strong correlation between the calibrated model and the experimental data, confirming the high accuracy of the ISPG method.  This study highlights the significant impact of ISPG material model parameters on solder joint morphology, aiming to improve the design process by identifying key parameters.

Meals

Buffet lunch will be served during:

Advancements in Sustainable HPC Solutions for Ansys Applications

At this lunch presentation AMD and HPE will address the complex simulation challenges faced by our enterprise CAE customers. This session will focus on the deployment of highly efficient high-performance computing (HPC) clusters, power management options, advanced storage systems, and robust data management solutions. With customers relying on advanced modeling and simulation as a key business function, it’s important to choose HPC systems that optimize performance for a wide range of applications. Additionally, as thermal and power management become increasingly critical in next-generation HPC systems, customers need to understand the advantages of liquid cooling technology.

Join us to also hear about the latest performance results of AMD EPYC™ processors running Ansys CFX, Ansys Fluent, Ansys Mechanical, and Ansys LS-DYNA, demonstrating their capabilities in real-world applications.

Buffet lunch will be served during:

Case Studies in Utilizing High Performance Computing (HPC) and AI/ML for Digital Transformation

High-performance computing (HPC) and AI/ML accelerate solving complex simulations at unprecedented speeds to innovate faster.  This talk will focus on case studies of clients utilizing TotalCAE-managed HPC services for clusters and cloud to accelerate their digital transformation using HPC, cloud, and AI/ML for their CAE workflows.

Electronics and Lighting

While simulation has been accepted as a best-practice in evaluating reliability of automotive electronics, the actual activity continues to be constrained. Overwhelmingly, OEM and Tier 1 organizations only evaluate reliability as a final signoff, instead of proactively integrating electronic reliability simulation into the entire product development process. In this talk, we will review current best practices for electronic reliability simulation within the automotive industry and then discuss future trends to better leverage electronic reliability simulation, including performing reliability prediction pre-layout and requiring reliability prediction simulation insight from Tier 1 and Tier 2 suppliers.

Digital Engineering

Simulation has become an indispensable component of the modern product development cycle, enabling faster and more efficient design iterations. A critical aspect of this process is the digitalization and optimization of simulation workflows through the seamless integration of multidisciplinary computer-aided engineering (CAE) tools. In this presentation, we will demonstrate how to maximize the potential of your existing CAE tools by orchestrating and optimizing these workflows with Ansys optiSLang, the cutting-edge process integration and design optimization (PIDO) solution. By leveraging optiSLang, engineers can streamline complex simulations, reduce manual effort, and accelerate time-to-market. This talk will delve into key methodologies, including Sensitivity Analysis, Design Exploration, Optimization, and Robustness and Reliability assessments, showing how these approaches can improve product quality while mitigating risk and uncertainty in real-world applications.

Electrification and ADAS

In this presentation, we will focus on EV battery thermal runaway propagation simulation. In such a simulation, we model the heat generation due to exothermal reactions during thermal runaway and the subsequent heat transfer of the heat to the cooling system. The exothermal reaction models are calibrated from the accelerating rate calorimetry (ARC) data of a battery cell. The heat transfer is modelled using conjugate heat transfer models in computational fluid dynamics (CFD). Modelling of venting and vented gas reaction is also discussed. Several validated examples will be shown in this presentation including module/pack runaway propagation and the associated venting and vented gas reaction.

Safety and Systems

How doing our FMEDAs in Medini instead of Excel: - Improved the quality of our analysis - Resulting in a higher confidence in our design. - Made extracting data on specific failures quicker, easier and more accurate - Made estimating the risk of missing/inoperative Safety Mechanism easier and more accurate Traceability matrix between SFMEA and FMEDA improved confidence in our design.

Aerospace Structure Impact and Dynamics I 

The high strength, toughness, quasi-ductility over monolithic ceramics, and elevated temperature oxidation resistance make carbon-carbon (C/C) ceramic matrix composites (CMCs) excellent candidates for hypersonic vehicle components expected to experience high strain rates and high temperatures in service. However, accurate characterization of the material behavior under such extreme/harsh conditions presents significant challenges. This work will present the results of LS-DYNA computations conducted in support of an ongoing Southwest Research Institute (SwRI) internal research (IR) program focused on the high temperature, high strain rate behavior of C/C composites. The goal of this IR program is to develop and fabricate a system capable of rapidly heating metals and C/C CMC test specimens up to 4000°F to facilitate elevated temperature tensile split-Hopkinson pressure bar (SHPB) testing. This talk will provide an overview of SwRI’s dynamic material testing capabilities, challenges associated with the current effort, and will primarily focus the use of explicit finite element (FE) simulations to support the experimental program. Results of a computational investigation into apparent non-equilibrium behavior exhibited in previous SHPB tension tests conducted on a commercially available C/C composite material will be presented. Said non-equilibrium behavior is suggested by the measured signals on the input and output bars during these tests, particularly the dissimilarity of the transmitted wave and the sum of the incident and reflected waves. In the computations, the entire SHPB set up is represented/meshed. Two different approaches are taken to model the SHPB tension test coupons. The first approach considers the woven C/C CMC as a smeared homogeneous continuum. These continuum simulations resulted in sudden, brittle failure, which leads to wave attenuation/dispersion of the transmitted strain signals (i.e., the transmitter bar strain signals decrease in amplitude with increasing propagation distance). To correlate the simulated and experimental strain amplitude at the location of the transmitter bar strain gage in the tests, it was found that the value of the maximum principal stress at failure in the continuum models needed to be increased to what the authors believe is an unrealistically large value. It was also found that the continuum modeling approach is unable to capture the widening of the transmitter bar signals that is present in the SPHB experimental data. In the second modeling approach, the woven C/C CMC mesostructure (i.e., the tows and matrix) was explicitly modeled. Compared to the continuum simulations, the mesoscale simulations exhibited a more progressive failure, which was found to result in enough additional “ductility” such that the amplitude of the strain and force signals did not decrease with increasing propagation distance along the transmitter bar. Additionally, the mesoscale simulations resulted in transmitted strain signals that were wider those in the continuum simulations and were in good agreement to those in the experiments. Whereas the maximum principal stress at failure used in the continuum models had to be unrealistically increased to correlate the simulated transmitted strain signals to those in the experiment, this was not the case for the failure properties used for the tows and matrix in the mesoscale simulations, highlighting the importance of the mesoscale approach.

Human Body Models

THUMS (Total Human Model for Safety) is a detailed biofidelic finite element model of the human body, which encompass different genders and physiques including detailed anatomical features of the skeletal structure, internal organs, and other soft tissues like skin, flesh, and ligaments. The application of THUMS in numerical simulation offers exciting opportunities in automotive and civil aerospace development in areas such as safety, comfort, and ergonomics. These models will play an increasingly important role in the study of human body kinematics and assessment of injury risks in collision accidents.   The Civil Aviation University of China (CAUC) aims to establish a posture database according to civil aviation standards and perform detailed numerical studies on the biomechanical response of aircraft occupants using THUMS in a variety of simulated impact scenarios. CAUC collaborated with Arup and its software house Oasys LS-DYNA, to help build this posture database and explore the feasibility of using Oasys PRIMER (a world leading LS-DYNA pre-processor) with its comprehensive human body model positioning tools and THUMS positioning metadata to achieve complex emergency braced postures.  This presentation describes the entire positioning workflow used to achieve complex brace postures using the multi-stage positioning method in Oasys PRIMER and the *CONTROL_STAGED_CONSTRUCTION keyword in LS-DYNA. Further investigations were made to optimize simulation run times. Using a generic aircraft seat, a series of standard dynamic load cases are performed to predict occupant kinematics and the extent of injury risk to the aircraft occupant. This research will contribute to the wider application of THUMS in the aviation industry and promote biomechanical research into aircraft occupant safety.

Material and Constitutive Modelling I

Joining is a critical part of any structure for transferring load and maintaining integrity for the product. Ultrasonic weld is one of the popular methods for joining plastic parts in automotive industry. Along with providing a visually demanding finish, the method has been established for tight, strong, and dimensionally accurate joints. With the increase of complexity and integration of electrical and sensing instrumentation in autonomous and electric vehicles, ultrasonic weld provides a necessary means of attaching parts into plastic parts without compromising visual impact. However, the sonic weld performance is yet to be quantified, and the criteria for capturing weld separation, and losing this connected load path during structural vehicle analysis, has not been studied extensively.  Sonic welds, even though it is a very effective joining method, the whole welding tooling process is expensive and time consuming. Ideally, to optimize the welding spot number and develop future cost-effective welding method, it is crucial to understand the actual weld performance under various variables such as material, thickness, temperature, strain rates etc.  In the following study, sonic weld performance has been evaluated in five different material combinations, three different strain rates and three different temperatures. Based on the analysis, a fully characterized FEA sonic weld modeling method has been developed, which captures weld separation, for in-production parts joined with sonic welding method. This method can be applied on full vehicle level analysis, like front and rear low speed, and enables for optimized design by determining an ideal number of sonic welds necessary for this type of structural loading.

SDM

In recent years, the speed required for product development has increased significantly. With the increasing sophistication of requirement levels, data-driven development utilizing past data is gaining attention in the automotive industry. Compared to physical testing, simulation is characterized by the ease of obtaining information through calculation and analysis, but on the other hand, handling huge amounts of data is a challenge. In this paper, we propose a new process to search for similar behavior of the part of interest from dozens of crash simulation results by using the order-reduction technique. Assuming that an irregularity occurred in the behavior of a member, the modeling history database was searched for members with similar irregularities using the shape of the member as a key, and highly similar behaviors were found in the database.

Aerospace Structure Impact & Dynamics I

Impacts at or exceeding speeds of 7 km/s are classified as hypervelocity impacts (HVI). Micro-meteorites and space debris travelling at these speeds pose a present and prevalent threat to the operational safety of satellites. The damage can be measured through loss of operation and the corresponding economic expenses. HVIs against satellite shielding structures need to be understood to improve their design, however, physical testing is usually associated with high economic expenses. A commonly accepted and widely used alternative relies on numerical modelling of HVI shielding structures. More recently, the use of advanced composite materials in space applications, such as carbon fiber reinforced polymers (CFRPs), has increased. This necessitates the development of accurate numerical modelling techniques adept at predicting failure mechanisms and damage under these extreme loading conditions. Generally, numerical techniques employed in HVI modelling rely on the Lagrangian implementation of the finite element method (FEM; well-suited for tracking relative motion between interfaces, useful in modelling delamination) or the meshfree smoothed particles hydrodynamics method (SPH; excellent for replicating extreme deformation and fragmentation). To leverage the benefits of both methods and capture the wide range of failure mechanisms and damage occurring simultaneously, this study employs an adaptive FEM-SPH method. 16-ply laminated composites under HVI by three different types of orbital debris (steel, aluminum and nylon, classified as high, medium and low-density materials by NASA, respectively) are studied. Crater size and delaminated area are used as comparison metrics to gauge the accuracy of the numerical models and variations in the prediction of these features are explored and quantified across different composite material models.

Human Body Models

HBMs have become a much-needed tool for the safety simulations of the automotive industry. Out-of-position load cases for passengers and simulations for other vulnerable road users, like pedestrians and cyclists are increasingly needed, and HBMs can address this need. Euro NCAP in its Vision 2030 plans to replace ATDs with HBMs and the same is expected by other consumer information programs.  Nevertheless positioning, pre-processing and post-processing of these models on a level suitable for industrial use has not been straight-forward until now.  ANSA, offers a novel solution to this complex problem, making the positioning and handling of an HBM, as easy as with an ATD model. Using an advanced integrated MBD solver in parallel with morphing algorithms, engineers are provided with real time articulation and positioning of a HBM within an easy user interface. While the user just articulates the human model with the mouse in a most direct way, the biofidelic joint modelling guarantees realistic model movements and the generation of a ready-to-run model without the need of pre-simulation.  In parallel we are developing tools to tackle the problem of producing variants of an HBM adapting it to different anthropometries, thus better representing the population variability. We are addressing the problem with morphing and remeshing tools along with methods of incorporating anthropometric data into our algorithms.  Vulnerable road users is another area of great interest where HBMs are the only choice. We are conducting our own research projects related to cyclists' postures. Through statistical processing of laboratory scanned data we aim to produce statistical rider models that will be applied on the HBMs. This gives the possibility to the engineers, to simulate crash events of any kind of bicycle for any rider anthropometry.  On the post-processing side, running interactively or in batch mode, the META HBM tool automatically creates PPTX and PDF reports including videos and images of GHBM's kinematics, strain contour plots, elements erosion identification, chest-bands deformations, and injury criteria calculations (Brain CSDM, Abdominal soft tissue organs SED, etc.). Moreover, time history results can be extracted from the Occupant Injury Criteria tool. Injury criteria like HIC, BrIC, Nij, etc. are calculated. The extracted and calculated results can be compared to corresponding results of Anthropomorphic Test Devices (ATDs), while it is also easy to make comparisons between multiple HBMs simulation runs or between results from different solvers.

Material and Constitutive Modeling I

In comparison to Metals, several aspects of Polymers such as Viscoelasticity, Early Necking, Wide range of ductility, poses several challenges for manual calibration that can be accepted for us in full-vehicle crashworthiness. This paper provides recent advancements in d3VIEW to develop an accurate *MAT_024 and *MAT_SAMP models. The automation is powered by d3VIEW's Workflow and powered by its new product, FORA, a generative-AI multi-agent that is tightly integrated with d3VIEW.

SDM

From an industrial or productive standpoint, the scale of simulation   models, the number of involved simulation model components, and the complexity of the utilized processes with a vast amount of data are at a level that is challenging to manage manually. The introduction of virtual testing adds to the complexity of the development process and the quantity of data to be handled. Consequently, the use of an SDM system for this purpose can be advantageous in numerous ways.  The introduction of virtual testing can be accomplished in several steps. The initial step is the automation of data preparation, encompassing both input data and produced result data for both the OEM and the testing authority. Subsequent steps involve the implementation of individual processes and security mechanisms against data manipulation. This paper/presentation primarily addresses the initial step and outlines a methodology for achieving the objective of safeguarding against data manipulation and intellectual property (IP) infringem

Electrification and ADAS

Ansys Electronics continues to demonstrate its multi-decade leadership in computational electromagnetics and multi physics simulations.

In the newest release 2024R2, the unmatched market leading and multiphysics motor design simulation software, Ansys Motor-CAD, now offers capabilities that are further widening the competitive gap. Some of these new capabilities are: three new cooling methods; extending the capability of the adaptive templates; and enhanced electromagnetics.

As an advanced electromagnetic field solver that widely used for electric machine design and analysis, Ansys Maxwell in 2024R2 now has: an improved formulation for the sliding mesh interface called Continuum Air; a new reduced order model(ROM) for brush commutating machines; and a new User Defined Primitive (UDP) for hairpin coil.

Digital Engineering

Ansys is advancing its customer experience, accelerating democratization of simulation, and powering next-generation innovation with new AI capabilities across its simulation portfolio and technical support services. As simulations are being leveraged more in early stage design processes, there is a need to run them faster compared to high fidelity simulations.

AI presents here an opportunity to address key challenges and scale the solution to non-experts. Ansys SimAI, a cloud-enabled physics-neutral platform, empowers users across industries to greatly accelerate innovation and reduce time to market. SimAI allows engineering teams to predict performance of complex problems in minutes, encouraging performance improvement, progress and innovation.

AI Models rely heavily on high-fidelity data with several data points and accuracy. Leveraging the right data becomes important for a good AI Model; this is where Ansys Minerva comes in. Minerva offers traceability, versioning and data driven insights across a centralized, access controlled repository thereby complementing and enriching the SimAI workflow.

Electronics and Lighting

The design complexity of high-performance electronics systems, including chip-package-board and accompanying enclosures and housings and even entire systems such as electric vehicles (EV) has dramatically increased in recent years. With further requirements to integrate high-speed digital functionality for infotainment and safety systems such as HDMI, USB, DDR4, PCIe and UCIe, reaching compliance with EMI/EMC (ElectroMagnetic Interferences and Compatibility) standards has become ever more challenging. In addition, electromagnetic co-existence issues with RF wireless protocols such as AM/FM, Satellite Radio, WiFi, Bluetooth, ZigBee, and 5G/6G can potentially occur, causing signal integrity issues within and across systems resulting in bandwidth reduction and reduced performance. Finally, the push to EV technology with the demands for higher voltages and operating frequencies has introduced some of the most challenging problems in EV development. In the worst cases, addressing EMI/EMC issues requires a significant re-design of critical EV systems resulting in increased cost of design and delayed time to market. This presentation reviews the most recent advancements in electromagnetic simulation methodologies for virtual EMI/EMC testing covering the entire design spectrum from chips to systems on to full EV capacity.

Safety and Systems

Modern vehicles are getting more and more complex and an approach that utilizes industry best practices is needed to prevent the vehicle from behaving in ways that are unwanted and/or dangerous. This presentation describes an approach based on a process model, automotive functional safety and Safety of the Intended Function as well as automotive cyber security to prevent unexpected behavior. We will investigate how the process can be used to ensure that requirements are documented, understood and implemented. Then we will use ISO 26262:2018 to explain how unreasonable risk due to E/E malfunctions can be prevented. After that we will discuss the use of ISO 21448 (SOTIF) to prevent malfunctions caused by technological insufficiency and foreseeable misuse. The final layer of protection is achieved by using ISO 21434 cyber security to identify and analyze hacking attack risks.

Aerospace Structure Impact and Dynamics I 

Due to the anticipated growth in the Advanced Air Mobility (AAM) market, there has been a surge of interest in novel advanced material systems like thermoplastics. This new-age aerospace industry could benefit from novel manufacturing and joining processes to meet the expected high-production volume requirements compared to traditional aircraft. As these materials are projected to be used in primary and secondary structural applications, there is a need to understand their resistance to dynamic impact conditions. The present research evaluated flat thermoplastic composite laminates for bird strike resistance through a series of experimental and numerical investigations. The test article was a flat composite panel fabricated with unidirectional carbon fiber and a thermoplastic resin system, and the impactor was a bird. Before conducting the bird strike experiments, pre-test numerical analysis was conducted to determine the laminate thickness that would achieve two desired outcomes: bird penetration through the laminate and bird rebound. The composite panel was modeled using LS-Dyna *MAT Laminated Composite Fabric, commonly referred to as MAT058. Coupon and sub-component level tests were conducted to develop the material model. Pre-test numerical analysis was used to predict the laminate’s response and determine the impact velocities. Bird strike experiments were then conducted and numerically validated using post-test simulations.

Human Body Models

Current trends and developments in the automotive market have caused changes in the way we evaluate and analyze our vehicles. Reacting to a growing demand for realism in simulations, Hans was born. Hans is the DYNAmore's human model, characterized by its detail and excellent performance. Realistic Human Body Models (HBMs) are one of the major revolutions in the world of vehicle development simulation nowadays. But the greater realism of the models comes together with a greater complexity in their handling. GNS offers its well-known products in response to this new need: Generator4 and Animator4 . Thanks to Generator4, Hans and other HBMs can sit comfortably through a simple GUI that is as flexible as the model itself. An industry that requires high realism in passenger models also expects a high level of accuracy in capturing the effects of changing model position and interaction with its environment, reflecting seat deformations and realistic positioning of anchoring systems.

Material and Constitutive Modeling I

The diverse characteristics of Polymer materials, in comparison with Metals, presents significant challenges to develop accurate material laws for use in Crashworthiness simulations efficiently.

With the advent of decision-making Workflows combined with the power of AI, this paper provides benefits, efficiency and accuracy of HPC and AI powered Workflow-based calibration over traditional methods using several Industrial materials used frequently large deformation and high-velocity applications.  The paper will cover sequential and parallel calibration of brittle and ductile Polymers, using *MAT_024 or *MAT_SAMP_LIGHT, with DIC for Quasi-static hardening curve, strain-rate based failure with damage using *MAT_ADD_GISSMO, and Mesh-regularization with the use of AI. The paper will demonstrate the immediate availability of the AI-powered Workflows as Software-as-as-Service (SaaS) in the Cloud that can consume Test-data in Excel format and generate calibrated material cards that can be applied in real-world applications.

SDM

In recent years, crash and safety simulations have reached a very high level of accuracy in the prediction of the crashworthiness of the vehicle and the probability of injury for occupants and pedestrians under a multitude of loading scenarios. Among several factors, this achievement is also attributed to the fine resolution of finite element models that enables the precise representation of even the smallest parts and geometric features affecting the simulation results and the increase in the number of simulated loadcases. However, accuracy does come with a cost: Model size and variability have considerably increased, together with the number of loading scenarios that need to be simulated on each model variant.  From the definition of the different model variants from the CAD structure and the subdivision of the simulation models into functional sub-assemblies, to the set-up of the numerous different types and flavors of loadcases, there's one thing in common: The modular management of the model at each phase of its lifecycle. At early phases, modules are as small as CAD parts. Later, modules become functional sub-assemblies which, for crash and safety loadcases, are handled as include files. Although the modular way of work is the only reliable method to enable parallel work on different areas of the model, it increases the“administration cost” during the simulation preparation, as the pool of different modules (be they parts or include files) needs to be consumed by higher level structures in a way that facilitates data reuse and enables traceability throughout the complete digital thread of a simulation model.  BETA CAE Systems' Suite of applications addresses these challenges during model build and loadcase set-up with its Modular Model and Run Management solutions, that facilitate the handling of the different model and loadcase variants in a way that maximizes data reuse and enables traceability from part to simulation run, while mitigating the “administration cost” with the extensive use of templates. From the population of CAE subsystems based on the CAD structure, to the definition of different subsystem variants, vehicle configurations and loadcases, templates act as recipes that hold the instructions on which ingredients to use and how, in order to complete each given task.  This work discusses the definition and use of templates during model build and loadcase set-up, focusing on three key phases of the simulation preparation: First, the CAD to CAE structure mapping during Subsystem definition from PDM/PLM structures. Second, the handling of Subsystem variants in the scope of the different vehicle configurations. Third, the handling of parametric include files for the definition of Loadcases. Insights are given on the methods used for the adaptation of the templates to the specifications of each model in hand and how these are finally interpreted into LS-DYNA keywords behind the scenes, by making use of parameters, transformations and ID management techniques.

Electronics and Lighting

EMC is a challenging topic for power electronic applications that are trying to switch more power at higher speeds inside smaller packages. Early system insights are valuable since EMC compliance testing failures are notorious for delaying product release and costing lots of time and money late in the development cycle. In this presentation we will look at ANSYS simulation capabilities for power electronic EMC analysis of electric vehicle drivetrains that predicts switching transients based on real-world packaging parasitics and mutual coupling between traces and components.

Digital Engineering

Honda has been constructing a unique material intelligence digital platform using material database Granta for the purpose of highly efficient, environmentally friendly R&D. We’ve designed a material database structure ’schema’ , which is easy to be utilized for fields of design, simulation (functional and manufacturing CAE, additive manufacturing, materials informatics), additive, procurement, and material development.  Over thousand users in the company accesses to the in-house material database and we’re now tackling to enlarge its application for Honda products not only automotive, but motorcycle, power products, aerospace, and other technical fields. Besides, under VUCA circumstance for future, we suggest a prospective digital utilization on material information toward environmental, regulatory issues, and AI technology progress.

Safety and Systems

Legacy approaches to automotive system design often consider systems engineering and systems safety analysis as critical yet separate engineering activities. This results in specialized but siloed and disconnected processes, ill-equipped to efficiently respond to industry trends of increasing product complexity and reduced development times. To counter these challenges, as in other industries, automotive systems engineering has adopted a Digital Engineering approach centered around a model-based representation of the design intent. However, unlike other industries, model-based work products in automotive are not a direct deliverable to our customers. Why then do we invest significant engineering resources into model-based systems engineering (MBSE)? This presentation will highlight the value proposition of closely coupling automotive systems engineering and safety workstreams via a shared, common SysML model. Discussion will include how the model enables an integrated development process where safety is designed into the system from day one. Additional return on modeling investment will be shown in the form of integrated system simulations, efficiencies in system verification and validation, and accelerated design certification and instantiation.

Electrification and ADAS

The electric powertrain has become a pivotal component in modern electric vehicles (EVs), directly impacting their performance, efficiency, and user experience. Ensuring the durability and reliability of the electric powertrain is crucial for the long-term success of EVs. This study focuses on developing predictive models for the durability of electric powertrains, with particular attention to Noise, Vibration, and Harshness (NVH) characteristics, which are critical to both the perceived quality and mechanical integrity of the system. By integrating advanced simulation techniques, material fatigue analysis, and real-world testing data, the research aims to enhance the accuracy of durability predictions and optimize NVH performance. The findings provide insights into the key factors affecting powertrain longevity and noise reduction, offering valuable guidance for the design and engineering of more robust and quieter electric vehicles.

Aerospace Structure Impact and Dynamics I 

This paper discusses the challenges of simulating a rotating helicopter main rotor blade constructed from heterogeneous materials. A two-step LS-DYNA process is used to pre-load the helicopter blade: first, a centrifugal force is applied with the blade at rest, followed by initializing the stresses for a transient explicit rotation simulation. The blade, modeled with a combination of shell and solid elements, encounters instabilities such as element erosion and checkered stress patterns when the adhesive is modeled with *CONTACT_AUTOMATIC_SURFACE_TO_SURFACE_TIEBREAK. Several strategies are proposed to resolve these instabilities, including replacing TIEBREAK contacts, adjusting contact settings, and employing implicit transient preload simulations. After the implementation of the strategies, the model's stability was verified over a full revolution simulation.

Human Body Models

Hans - Human Body Model: Enhansments  Over the last months, our Hans - Human Body Model has successfully been introduced to a few selected pilot customers. These customers tested the model in their environments, evaluated application load cases, and kindly shared their feedback with us. Coming this fall, an update of our Hans – Human Body Model will be released based on this valuable customer feedback. The model has further improved in several aspects like the overall response, geometrical level of detail, robustness, etc.   In terms of usability, more output definitions are now available as well as the occupant and pedestrian postures are fine-tuned to better fulfill the requirements of certification load cases. Regarding the model performance, several load cases have been added to the correlation repository of Hans.  To evaluate potential injuries, a stand-alone tool will be released with the upcoming update of Hans. This tool encompasses the calculation of injury risks based on the output of a Hans analysis.

Material and Constitutive Modelling I

This contribution will deal with the latest developments in the field of material models in LS-DYNA. This includes completely new methods as well as extensions and improvements to existing models. The range of materials concerned includes metals, foams, composites, plastics, honeycombs, glass, adhesives, damage and failure models, and others. The new developments are aimed at improving prediction quality, robustness, performance, and user-friendliness. Examples will be used to illustrate the new features.

SDM

Due to the continuously increasing demand in Computer Aided Engineering (CAE), it is essential for high efficiency and transparency to automate and standardize processes. In many cases, Simulation Data Management (SDM) software is used for this purpose.   To achieve all mechanical target values of a product, there are several standard disciplines in the field of CAE, such as crash, Noise Vibration Harshness (NVH) or fatigue. Assembly, solving and postprocessing for these disciplines can differ greatly from one another. For this reason, it is best practice in many companies to carry out the optimization of a model in each discipline separately and to compare the results and structural adjustments with other disciplines at regular intervals. This approach can lead to two disadvantages:   Firstly, this results in redundant work. Model adjustments successful for one discipline must be redone in other disciplines later on. Secondly, deterioration in other disciplines may be discovered late: Model modifications that produce positive results for one discipline can have a negative impact on the results in other disciplines. This can lead to short-term changes of plans, postponements, or additional costs for optimizations.   With the help of a base model and SDM, several disciplines can be covered based on a single source of truth. The basic approach is not to work directly on the solver specific files, but on the base model itself. All discipline and solver specific files are generated from this file via SDM automatically. As an example, the two disciplines crash and NVH are considered in this paper. LS-Dyna is used as solver for both disciplines.   There are several criteria that must be met to successfully carry out multidisciplinary variant creation and result evaluation:   A complete database for all discipline-specific information, such as load case definitions or connection configuration is essential. Furthermore, a flexible and simple selection of the load cases is needed. It is also crucial to have an automatic process flow from the creation of the include to the finished report. This report must be designed according to the discipline-specific load case. A clear overview of all created variants is as important as a dynamic variant comparison of the results for each discipline.   The SDM software SCALE.sdm fulfills all these points, which is why it is used as the basis for this paper. So far SCALE.sdm is used by users in the industry like in the best practice example mentioned above: For each discipline there is a separate variant tree that is considered independently. By customizing specific key features such as an automatic creation of the include- and inter-include-connections, it is now possible to display all disciplines together with a single variant tree.   This approach makes it possible to avoid the above-mentioned disadvantages of the separate discipline approach: All the desired disciplines can now be covered by a single model modification, allowing the user to work effectively on structural optimizations while minimizing resources.

Electronics and Lighting

Lighting technology is significantly evolving. Displays are becoming larger, brighter, and multi-dimensional. Lighting systems are more customizable, flexible, adaptable and complex as ever. Imaging sensors are getting smaller, while image quality is getting higher. For many of these optical systems, challenges are being solved at the micro-level, but system-level integration and performance are equally as complex. With the Optics portfolio at Ansys, we can bridge the gap between microstructure optimization and system level performance. Many advances in our Optics products have enabled us to keep up with the evolving lighting & sensing technologies, such as improved GPU acceleration, stray light analysis, and full optical product workflows. Join to discover the Ansys Optics solution and its recent innovations.

Electrification and ADAS

Modern vehicles put new demands on electronic devices and systems for the control and conversion of electrical power. Electrification promises more efficient powertrains with increased range, faster charging, and lower weight but they also introduce new sources of electromagnetic interference (EMI). The design and optimization of these systems are essential to achieving greater power density, efficiency, and reliability. This presentation will introduce the extensive simulation capabilities used to design and fine-tune power electronics, predict their behavior under both normal and faulty conditions, and proactively address potential issues – from analysis and sizing of power converter topologies to reduced-order modeling of electromagnetic components and electronics reliability.

Safety and Systems

The complexity of an open pit mining Fleet Management System (FMS) presents challenges for Medini Analyze FFMEA tools. The FMS is structured into four main architectural layersSite Operations, FMS, Autonomy, and Drive-By-Wire each containing multiple internal layers and many functions. The AIAG-VDA 2019 FMEA Handbook reflected in Medini does not directly provide a concept of system nesting or module reuse found in the design. This session explores an example of an open pit FMS and an approach using Medini to address its complexity. The approach is a work in progress, building on lessons from previous efforts and enhanced through collaboration with the Medini Analyze Expert Team.

Digital Engineering

Automotive external aerodynamics is a key factor in vehicle design, impacting aspects such as performance, fuel efficiency, stability, noise, and comfort. In electric vehicles (EVs), aerodynamic performance is especially critical as it significantly affects battery range and longevity. Moreover, EVs generate significantly less engine noise compared to traditional combustion engines, making aerodynamic designs crucial for achieving acoustic comfort. Another crucial area for the automotive industry is thermal management. Efficient thermal management systems help maintain the right temperature for the engine, battery, and other critical parts, ensuring optimal performance and longevity of vehicle components. This presentation will showcase the latest Ansys solutions in the field of aerodynamics and thermal management. We will also demonstrate how our Native Multi-GPU solver can accelerate simulations by an order of magnitude, enhancing the accuracy of aerodynamic predictions and enabling faster design iterations and better optimization. Lastly, we are thrilled to introduce our latest AI innovation, Ansys SimAI, which promises to revolutionize data-driven design.

Battery/Electric Vehicle

The rapid advancement of battery technology has driven the demand for advanced tools for a comprehensive understanding of battery cell behavior. This contribution proposes a holistic approach to generate digital twins of battery cells via coupled electro-thermo-mechanical modeling techniques within LS-DYNA. These models serve as precise representations of real-world battery cells, contributing to the development of efficient and secure battery systems.

Machine Learning

Topology optimization plays a crucial role in generating initial design concepts during the early stages of vehicle development. It is a Finite Element Analysis (FEA) based technique that helps to optimize the shape and distribution of the material in a desired packaging space. This paper explores the application of LS-TaSC and LS-DYNA for topology optimization of giga casting in a vehicle’s underbody. 

This paper touches on the importance of topology optimization in the design development process and elaborates how the LS-TaSC tool can be used to get directional guidance before initiating detailed 

design (CAD) work. BIW (Body-in-White) global static bending and torsional stiffness load cases were considered while setting up the optimization model. Various optimization setting parameters, constraints, and post-processing tools available in LS-TaSC were explored and have been elaborated in the paper. 

The use of LS-TaSC and LS-DYNA in this project enabled the generation of an initial giga casting design concept, indicating the critical areas where material is needed or can be removed. These designconcepts were further refined by the design team using CAD tools, considering more realistic manufacturing and performance constraints.

Multiphysics Modeling

The sloshing phenomena is defined as the movement of a free fluid in a space or tank. Sloshing of fuel in a tank of an aircraft can cause dynamic disturbances that can affect the functionality of the operational system. This work examines two industrial real-life sloshing problems. The first example is the sloshing of fuel in a satellite tank. This can arise from the operational maneuvers of the satellite, which in turn can cause significant inertial forces that may lead to the decrease in performance of the satellite even up to the point of control loss. Another example concerns the slag liquids that may accumulate during the combustion process and acceleration phase of the rocket. These liquids can undergo sloshing which may narrow the nozzle opening and affect the motor performance and mission success. This work presents the attempt to model these sloshing phenomena using the most appropriate LS-DYNA solver. An overview of the available FSI solvers are presented and test cases are done in order to define the most appropriate way to model these real industrial sloshing problems.

Simulation Methods II

In recent years the fidelity of finite element models in structural mechanics has reached a remarkable quality and level of predictiveness. Hence, the question arises, if these models could even be more than the digital twin during product development: Could they be also used for homologation and certification of the corresponding products through the respective authorities or regulators as well? This idea is often referred to Certification by Analysis (CbA) or Virtual Testing (VT). It should be noted that in different industries various steps towards these goals are already taken.   The respective virtual testing procedures (including certification and homologation by regulators) require additional attention: Method development for solver enhancements needs to ensure data as well as IP protection of all stakeholders and provide measures to prevent data manipulation possibilities before homologation and market launch of the product. There are various technical as well as legal aspects to the whole process. But it needs to be emphasized that one key ingredient will be the ability of the solver to protect and safeguard input and output data while at the same time allow for traceability and transparency through fingerprinting technology when it comes to data sources like i.e. material properties, geometrical data, solver settings like version, solution method and contact parameters. Clearly, these requirements can only be met by new solver enhancements.   The present talk will target the big picture of CbA, show the route to achieve some of the most pressing issues and showcase a proposal for a first step into new solve features. This approach will be exemplified by the new Euro NCAP Virtual Far Side Simulation & Assessment regulation for occupant safety.

Meals

Electronics and Lighting

The design of automotive lighting requires a deep understanding of optical, thermal, electronics, and structural domains to comply with stringent legal requirements. The complexity of automotive lighting systems and the integration of their subsystems necessitate feedback across these different physics domains. This presentation will address several key aspects, including the optical design considerations of automotive lighting, which involve optical design principles, light source creation, materials definition, and the integration of data across various software platforms. It will also explore the capabilities of automotive lighting in validation and optimization, aiming to enhance product workflows while reducing costs and time to market. Furthermore, we will illustrate how high-fidelity computer simulations can be utilized to understand the effects of these physics domains and their interactions on automotive lighting design, which is crucial for achieving operational success, refining design decisions, and accelerating the product lifecycle.

Digital Engineering

Structural simulation and Durability prediction is paramount to next generation vehicle validation which ensures the reliability, safety, and performance of vehicles under various conditions like different loading conditions, environments, and usage scenarios. Electrification, rapid technology advancements and shorter product development cycles demands rapid advancements in simulation best practices in a form of new modeling & meshing practices, different connection types, innovative simulation workflows, solver accuracy, performance, high performance computing, incorporation of manufacturing aspects into simulation, test correlation at different stages and ability in running end to end durability workflows to automate process of durability performance improvement. In this presentation, we will show how Ansys structural and durability solutions will help deliver next-generation vehicles that provide a safer, more comfortable passenger experience. We will cover different approaches customers are adopting both solver and workflow perspectives for component, system and vehicle level simulations.

Safety and Systems

Advanced software engineering tools can be used to generate test cases and test case sets of inputs and expected results.  Leveraging generative AI as well as traditional advanced software engineering tools enables engineers to develop test and certify safety critical software in new and more efficient ways.  This presentation will go through a journey of the tools and techniques of developing test cases of Natural language processing and software testing automation focusing on efficient methods and commercial software tools.

Electrification and ADAS

Join us for an exclusive presentation where Ansys unveils the future of highly automated driving. Discover how we tackle the engineering challenge of ensuring safety across diverse scenarios through model-based systems engineering. Our approach to safety encompasses design integration, verification and validation, and incremental safety case development, all supported by advanced simulation and analysis. You will also gain insights into Ansys's commitment to safety and simulation at scale, highlighted by an exclusive testimonial from OEM customers introducing the 'Personal Pilot L3' in the new BMW 7 Series.

During the presentation, you will learn how to effectively integrate safety into design and verification processes, navigate system limits with a focus on safety and SOTIF (Safety of the Intended Functionality) during development and validation, and utilize synthetic sensor data for perception machine learning and AI training and validation. Additionally, you will explore how to leverage simulation at scale to validate autonomous driving features, including sensitivity and reliability analysis in scenario-based simulations.

Keywords include simulation, virtual testing, toolchain, sensor, functional safety, SOTIF, safety case, MBSE (Model-Based Systems Engineering), synthetic data, machine learning, AI, radar, camera, lidar, ultrasonic, sensor fusion, and cloud.

Battery/Electric Vehicle

With the increasing popularity of electric automotive vehicles, electric vertical take-off and landing (eVTOL) aircraft, and commercial drones, there is a subsequent increase in the need to better understand and predict the mechanical behavior of batteries during crash and impact events. A plasticity model of the cylindrical metal casing of commercially available batteries is developed through mechanical testing. The plasticity model is then validated by simulating the mechanical tests with LS-DYNA, which is then used to simulate crush tests on full batteries to determine the mechanical response of the inner battery components. Future work includes using LS-DYNA to accurately predict the coupled mechanical, electrical, and thermal response of batteries to prevent unwanted battery ignition during vehicle and aircraft crashes.

Machine Learning

The process of developing a new vehicle in the automobile industry typically involves a comprehensive timeline of 3 to 4 years. This duration is necessary to conduct a wide array of tests and adhere to various regulations across different domains, particularly focusing on aspects such as Crashworthiness and Safety standards. In an alternative ‘mid-cycle refresh’ design and development process, the industry also considers introducing a new vehicle by making ‘delta’ mechanical/electrical and technological changes to an existing vehicle design. Either of the vehicle development processes require extensive virtual validation through CAE simulations of Crashworthiness load cases.

The purpose of the Crashworthiness analysis is to assess how well a vehicle's structure can protect its occupants during a collision. This study involves transforming the vehicle Crash Model Partially into a Lumped Parameter representation using DEP MeshWorks’ - Reduced Order Modelling (ROM) technology. The ROM model shows an impressive 85-95% correlation with the original Detailed Finite Element model. The complex process of converting the Detailed Finite element model into lumped parameter representation is automated through the ROM approach. The ROM model is further parametrized with a broad ranging category of parameters involving a) shape, b) gage, c) material, d) spot welds, e) adhesives, f) seam welds, g) crush-initiators, h) reinforcements, i) darts, j) bulk-heads, k) slots/holes, l) laser welded blanking, m) ribbing and o) composite lay-up to convert it to an intelligent ‘Parametric ROM Dyna model’ using DEP MeshWorks.  This parametric ROM model is integrated with a DOE (Design of Experiments) based Optimization scheme to obtain an optimized design that maximizes Crashworthiness performance and minimizes weight & hence cost. Thanks to the significantly reduced number of nodes/elements in the parametric ROM model, the entire optimization process can be completed in less than 50% of the time that detailed models would require.

Mass & Intertia representation, condensed stiffness & damping and other related techniques form the foundation of the ROM technology available in MeshWorks. The combining of two significant technologies – ROM & Parametric CAE provides users the advantages of optimization executed in a drastically reduced timeframe.

Multiphysics Modelling 

Parachutes for aerospace application is a new research area in the current era of space science. The scope of our project includes parachute design and inflation techniques. The current research project focuses on the following application areas: ● Parachutes for Re-entry Capsule ● RLV Parachutes Parachutes are used as aerodynamic decelerators in airdrop systems, so inflation is a significant fluid-structure interaction (FSI) phenomenon. New patterns of parachutes are constantly being developed and tested for airdrop systems but this research into parachute inflation is heavily reliant on historical experimental data. Till now, no parachute inflation model that is not based on this experimental data was developed. Material and instrumentation have changed significantly since the early experimental testing, yet the methods to develop the parachutes can still be traced to the same techniques used over ninety years ago. Rapid development of computational technology and modern computational mechanics combined with numerical simulation techniques have become more widespread in parachute research field and would enable us to develop the parachutes that are more optimized. Simulating the landing of a vehicle on water using LS-DYNA is a complex task that involves the interaction between the fluid (water) and the structure of the vehicle. This type of simulation is crucial for vehicles designed to land on water. The process typically involves several steps and requires specialized techniques within LS-DYNA.

Simulation Methods II

FMVSS301 mandates fuel system integrity for vehicles post-crash, so a detailed assessment of the fuel system is needed to validate its integrity. Modeling the fuel tank assembly for vehicle crashworthiness is very challenging due to the fuel slosh phenomenon which occurs in the dynamic crash event. The fuel slosh behavior affects the overall dynamics of the fuel tank and its interaction with surrounding vehicle structures. Extensive studies can be found in the literature to improve fuel tank modeling. However, there is limited information related to modeling fluid’s free surface and the pressure profile imparted by the fluid on the tank during the crash event. A fully coupled Fluid Structure Interaction (FSI) simulation of this event, using an explicit mechanical solver and an incompressible fluid solver (ICFD), is computationally expensive. Smooth Particle Hydrodynamics (SPH) has long been the choice for this application. This study uses the weakly-incompressible SPH formulation (ELFORM 15) to model the fluid behavior. In Phase-I of this two phase detailed calibration effort, the model is calibrated against a series of rigid tank slosh tests at two different speeds and various volume fills with and without baffles. The study shows good correlation of both the pressure time histories and the slosh pattern (fluid free surface) between the experiment and simulation.

Electronics and Lighting

Vehicle vision ergonomics are becoming more and more relevant in todays automotive market. The comfort and safety of the passengers in a vehicle might be affected by different natural and artificial light sources. Interior lighting, sun reflections, displays and external lamps must be taken into consideration during the development of an optimum cabin. The human factors are no longer a subjective topic, and thanks to Ansys Speos and its virtual human vision capabilities, an infinite number of scenarios can be assessed, and a robust design can be achieved.

Electrification and ADAS

ADAS/Autonomous vehicle Software is very complex as SAE level 5 autonomous vehicle probably will have 1B SLOC vs 14 M SLOC in Boeing Dreamliner (As per Bloomberg energy report). Testing of such complex software is challenging and also time consuming. Product quality and maturity for such high complex ADAS/AV is function of testing at various levels like SW testing, integration testing, System Qualification Testing and vehicle testing. With conventional approach ADAS features get tested only at fag end of the project as it requires availability of vehicle with fully equipped sensors and actuators needed. This is extremely risky for meeting the stringent quality standard of safety critical systems, and extremely expensive. With development and maturation of artificial scenario and sensors simulation, Virtual Validation is gaining prominence as it address all demits of conventional development and testing approaches and gives edge by left shifting features testing along side of SW development. With increasing fidelity of simulation and realistic visual orchestrations, feature function testing gets left shifted and performed at bench level and use vehicle to measure Key Performance Indicators. With developments on physics based Radar model from companies like Ansys (AVx) enable configuration of antenna properties and give out ADC signal level data Virtual RWUP will gain prominence and minimize expensive Real World user Profile tests (RWUP) in real vehicle, and also enhance coverage of testing towards Safety of Intended Functionality (SOTIF) standards. Smart tradeoff between tests segregation between vehicle and simulation, makes projects execution faster, and cost effective.

Digital Engineering

NVIDIA Modulus is a state-of-the-art, open-source scientific machine learning platform that enables research, development and deployment of surrogate ML models for a wide range of engineering applications, such as external aerodynamics, cabin cooling, electro thermal cooling simulations etc. The platform offers training pipelines that enable development of optimized and scalable end-to-end workflows for ingesting large simulation datasets, training models in a distributed manner on multiple GPUs, physics-based validation and ease-of-deployment. It offers unique tools for embedding physical constraints derived from governing equations into ML models to increase their robustness and accuracy state-of-the-art model architectures. It also offers specialized ML model architectures that cater to specific engineering applications. In this talk, we will demonstrate these capabilities of the NVIDIA Modulus platform to Develop end-to-end training pipeline for building surrogate models Show the benchmarking workflow to evaluate and validate different model architectures This will be demonstrated in the context of two use cases external aerodynamics and structural analysis of cars.

Safety and Systems

Among aircraft systems, the Fly-by-Wire (FBW) system is one of the most safety critical and it matures throughout the flight test campaign. During the campaign, many software loads are released, and several software verification activities must be satisfied before each of those software loads are allowed to be flown. At EMBRAER, SCADE has played a pivotal role for over 15 years in allowing the quick deployment of FBW software releases used throughout all its business units.   The latest version of SCADE is being used by EMBRAER in the development of the FBW for EVE - a leader in EVTOL industry. The brightest new feature in this latest version is SCADE Model Coverage Assistant (MCA), which is being used for the first time. This discussion will describe how EMBRAER believes how SCADE MCA will considerably alleviate verification activities and reduce software lead-times during the most strenuous phase of the aircraft development.

Battery/Electric Vehicle

The safety of battery systems in electric vehicles (EVs) and portable electronics under impact or penetration is critical. Traditional modeling methods, relying on simplified approaches, fall short in capturing complex interactions and dynamic behaviors. Advanced simulation techniques using LS-DYNA offer more accurate predictions of battery responses to penetration loads. This abstract outlines recent methodologies employing LS-DYNA for high-fidelity modeling of battery components and protective measures. Innovations include sophisticated material models for electrodes, electrolytes, separators, and structural packaging. These advancements align with standards like SAE J264, ensuring battery safety and reliability, thereby supporting the adoption of electric mobility and renewable energy technologies.

Machine Learning

N. Abdelhady1, D. Borsotto1, V. Krishnappa1, K. Schreiner1, C.-A. Thole1, T. Weinert1 1SIDACT GmbH Reaching and fulfilling several design and crash criteria during the development process is what makes the engineer adapt and redesign the simulation model over and over again. Ideally resulting in new simulation runs with in best case improved performance, matching the intention of the applied changes. For the more demanding case of unforeseen results which do not necessarily fit to the expectations of the actual changes, machine learning methods and a workflow are being introduced here, which allow to identify the root cause of this behavior.  In a first instance every new simulation run is being added into an analysis database, which is continuously being used to compare new simulations against. Previous studies have already shown that this process can assist the engineer in automatically highlighting new behavior and pin pointing the engineer to the regions of interest. Rather than only highlighting the new behavior now a second phase is being triggered additionally.  In this second phase the previously detected event is being isolated and analyzed against the gathered data of the development history. The analysis methods used are based up on the Principal Component Analysis, a reduced order modelling technique. This allows not only identifying structures in the data but also correlating deformation patterns against each other. Especially the latter one is of interest for an automated process, as it allows automatically detecting and suggesting possible root causes to the engineer. As an outcome of this process the engineer receives a list of correlating parts, so that he can focus on deriving a better engineering solution to achieve a deterministic behavior, rather than searching for the root cause of the event.  To provide additional information about the type of cause, as for example failure or buckling, the identified parts are also forwarded to a classification prototype. This type of classification shall assist the engineer in deriving a possible design adaptation.

Multiphysics Modeling

This paper focuses on implementing immersed interface techniques within the Incompressible CFD (ICFD) solver in LS-DYNA. For Fluid-Structure Interaction (FSI) simulations, immersed methods offer several advantages: they simplify the pre-processing stage, prevent large mesh distortions due to structural motion, and provide more robustness in scenarios involving complex contacts. However, these methods sacrifice accuracy in near-wall regions, suggesting that a hybrid approach between body-fitted and immersed methods could be beneficial in some cases. This work discusses two methods: the Resistive Immersed Implicit Surfaces (RIIS) method [1] and a method based on a new set of discontinuous finite element functions [2]. The basics of these methods will be presented, along with considerations for setting up a model in LS-DYNA, including pre- and post-processing.  [1] Fernandez MA, Gerbeau JF, Martin V (2008) Numerical simulation of blood flows through a porous interface. ESAIM: Mathematical Modelling and Numerical Analysis 42(06): 961-990. [2] R. Zorrilla, R. Rossi, R. Wüchner, E. Oñate, An embedded Finite Element framework for the resolution of strongly coupled Fluid-Structure Interaction problems. Application to volumetric and membrane-like structures, Computer Methods in Applied Mechanics and Engineering, Volume 368, 2020, 113179.

Simulation Methods II

The "Modular Contact" is a new implementation of contact algorithms in LS-DYNA. We will give a short overview of the fundamental ideas behind it and how it tries to achieve its main goals: a more uniform implementation between different contact options and improved (parallel) performance by better harnessing of modern hardware and improved MPI communication. The performance will be demonstrated on large models and put in comparison to the well-established, existing algorithms in LS-DYNA.

Battery/Electric Vehicle

As EVs become more mainstream safety concerns have been paramount for OEMs, consumers and regulators. Owing to the risk of thermal runaway in an EV crash incident, a full Multiphysics battery analysis is required to capture the mechanical deformation that triggers internal shorting and thermal runaway. This is helpful in knowing the mechanical design limits and designing vehicles with good crashworthiness while balancing the need to lightweight EVs as car companies strive for better range.

Ansys developed a simulation workflow from battery cell to vehicle level (in partnership with universities and industry test labs) to validate key pieces using physical test data from a 26Ah automotive pouch cell. LS-DYNA models were calibrated and subsequently validated against cell experimental data. Multiphysics models that captured a battery cell’s electrical, mechanical and thermal behaviors were then to used to scale up in a proof-of-concept simulation of full vehicle crash.

Ansys has partnered with George Mason University’s Center for Collison Safety (CCSA) to incorporate battery models into a full vehicle validated crash model. Ansys has tested cells (at our internal labs or through test partners) that CCSA extracted from a commercial EV to build battery models. The ongoing work to date will be discussed and used as an example to demonstrate the battery safety workflow in an EV.

Machine Learning

LS-OPT includes an improved version of the MOP (Metamodel of Optimal Prognosis) system developed for optiSLang. The feature allows automatic selection of the ‘best’ metamodel. The updated version also includes the standard LS-OPT metamodels (Neural and Radial Basis Function Networks) as candidates for the MOP selection process. Examples are presented.

A previously introduced methodology for integrating LS-OPT, LS-DYNA and Ansys Twin Builder for the purpose of creating fast full-field surrogate models, is presented with results using industry examples. LS-OPT is used to generate and schedule multiple LS-DYNA simulations according to an experimental design. The dynamic field results are then gathered as input to create a Reduced Order Model (ROM) using Ansys Twin Builder. Recent examples using single history ROM surrogates are also presented.

Finally, an outlook on current developments is provided.

Multiphysics Modelling

In the first part, we will introduce a newly implemented porous media model in the dual-CESE solver along with its applications. The implemented model is an interface jump condition method. In this method, the Darcy-Forchheimer momentum equation is solved as a jump condition at the porous structure interface. This model can be used in many different flow conditions, especially for supersonic flows, such as flows around supersonic spacecraft parachutes.  Some improvements have also been made, such as removal of the ‘ideal gas law’ restriction in order to extend its usage to more general EOS fluid applications. In this paper, we will give several examples to show the capability of these new features and how to set up the model in the dual-CESE solver format.  Please note that this porous-medium model is implemented in both the single and two-phase dual CESE FSI solvers. This capability can be used in many different applications with porous media, such as airbag deployment and supersonic parachute flows. But it

Simulation Methods II

In the last three decades, DEM (Discrete Element Method) has been extensively developed and applied to solve many geotechnical and petroleum engineering problems. Another approach is developed to account for the elastic and brittle behavior of materials. The method has been successfully implemented in the LS-DYNA to study cementitious, sintered, and rock materials such as concrete, fibers, rocks, etc.  Explosives with the combination of rock, concrete, and other brittle materials are simulated using LS-DYNA and studied extensively by researchers. In such cases, the volume of the fragments and number of fragments would be beneficial to the researcher. To assess this, we have developed a system that computes the number of fragments, fragment volume, and number of bond links associated with the DEM particles. Users have the option of outputting the results described above, depending on the user control card by using *DATABASE_DEFRAGMENT.  Additionally, DEM has been extended to simulate the likes of intumescent material through new keyword *DEFINE_DE_TEMP. The DEM formulation has been extended to include the temperature effect on the radius of DEM particles. The method takes the user’s temperature with several inputs and expands the radius of the particle based on the temperature and coefficient of thermal expansion. With recent improvements, the DEM can be thermally coupled with the Finite Element Method (FEM), and particles can change size in response to temperature changes in the DEM. The new improvements allow for the simulation of complex thermal behavior in a variety of real-world scenarios, like soil interaction with a blasting battery, a heated container containing discrete material etc. Users can activate thermal conductivity of the DEM by providing the required data in the DEFINE_DE_BY_PART, CONTROL_DEM, and DEFINE_DE_TO_SURFACE_COUPLING. The LS-DYNA/MPP decomposes the problem once based on its initial geometry. For good MPP parallel efficiency, the deformation of the geometry should not be too large to keep reasonable ratio between computation and communication time. However, most DEM problems are dealing with granular flow which involve mixing and large relative displacement between particles. The parallel efficiency degrades due to increasing data communication. A re-decomposition algorithm supports DEM features which can reduce computing costs by 30%. This algorithm would rearrange the grouping of the particles, thus avoiding the search for neighbors that are far away from the group. A Galerkin meshfree method can generate shape function without the need of element or mesh, allowing it to provide a quality solution for large deformation problems such as fragmentation, cracks, material separation etc. The shape function in the meshfree method is rational function with complexity and because of this the domain integration scheme becomes challenging. Over the last 2 decades, several integration schemes were developed to obtain a computationally efficient and stable solution. Few methods can resolve the stability related issues and the efficiency; However, the meshfree methods are still computationally expensive due to the requirement of the higher support size.  To address this issue a novel method, namely Expedited Galerkin Meshfree Method (EGMM) is developed. The method employs a nodal integration with a unique stabilization scheme to stabilize the solution at lower support size. The method only needed a minimum evaluation point and does not require direct second order derivative of the shape function. The method achieves its objectives by stabilizing the Galerkin solution with minimum computational cost. The resulting method conserves the linear momentum and the total energy. The contact between the two bodies or the particles can be easily implemented by integrating with the Discrete Element Method. Overall, the simulation result demonstrates that the method is stable and works effectively for the various types of materials. The user can define this model similar to the solids by generating solid and using *SOLID_EGMM. Numerous material behaviors are taken into consideration by using the constitutive model, which can be defined by *MAT_.

Digital Engineering

In the drive towards an electrified and sustainable future, automotive companies are increasingly adopting simulation technologies to accelerate design cycles and reduce costs. As technology complexities escalate, there is a growing need for an integrated development approach that emphasizes early-stage development and validation processes. Engineers are turning to simulation to address complex system requirements, but achieving comprehensive system validation through virtualization requires the creation of high-fidelity, physics-based plant models. These models, calibrated using machine learning and integrated with software functions and real-world driving scenarios, enable a virtual replication of the vehicle. Moreover, companies are now extending these models from product development into operational phases, leveraging the power of Hybrid Digital Twins for real-time fleet management. In this session, Ansys experts will explore the use of simulation methods, reduced-order models (ROMs), and AI/ML techniques for performance validation through model-in-the-loop (MIL), hardware-in-the-loop (HIL), and virtual drive tests. The discussion will also cover how to combine data and physics to create hybrid digital twins, which offer online insights into component operation through virtual sensors, predict service life, and optimize operating conditions. Key takeaways include understanding Hybrid Digital Twins—why and how to implement them, high-fidelity system simulation through reduced-order models, integrating test and 1D data with 3D data for accurate virtual validation, and the role of virtual sensors in predictive maintenance, all powered by physics and refined by data.

Electronics and Lighting

Heads Up Display (HUD) systems present information within the driver's field of view, allowing them to keep their eyes on the road at all times. HUD systems have been around for over half a century, but a renewed interest and investment in this technology is currently taking place. This is being driven, in large part, by the advent of Advanced Driver Assistance Systems (ADAS) where the need to display safety-critical information effectively, with minimal distraction, is essential. To keep up with the growing demand and complexity of HUD systems, engineers need a fast and effective way to design, optimize, test, and validate them. This includes defining functional specifications & quality targets, assessing key performance metrics, and visually perceiving the system as the driver would. Ansys optical solutions provide purpose-built tools and workflows to virtually design, assess and test a HUD prototype under various driving conditions. This presentation will demonstrate how optical simulation and virtual prototyping can help solve the challenges of HUD design.

Safety & Systems

Using the scenario of an electronic powertrain, we demonstrate how a suite of digital engineering tools using an Model-Based Systems Engineering (MBSE) approach. This will demonstrate how different engineering domains can collaborate and connect with tighter integration between design, analysis and simulation.

Electrification and ADAS

This joint presentation by Jeff Blackburn of Ansys and Chris Manning of dSPACE will discuss the new FMVSS-108 Adaptive Driving Beam (Smart headlamp) regulations, and how SiL and HiL simulation can be used to greatly reduce the need and inconvenience of track and road nighttime physical testing, saving time and money Integrating the Ansys AVx physics accurate camera and headlamp models with the dSPACE ASM 3D driving simulator and vehicle dynamics models yields a SiL virtual twin of your vehicle and ADB exterior lighting system allowing you to test your ADB headlamp function and control logic virtually on the desktop. 

Using a dSPACE HiL bench with an ESI (environmental sensor interface) module, the Ansys AVx simulation generated raw image frames can be directly injected into the real camera’s image processing chip. This let’s you test the ADB headlamp function and control logic using the real camera’s perception software in the convenience of the lab, versus having to send your engineering team out at night.

Battery/Electric Vehicle

Over the past decade, considerable advances have been made on battery safety models but achieving predictive accuracy across a wide range of conditions continues to be extremely difficult. From a numerical perspective, the obstacles are numerous.   Multiple physics can potentially be involved and interact with one another, electrochemistry, thermal, mechanical, fluid dynamics and so forth. The question of modeling scale also invariably arises. Is it reasonable to imagine a numerical model resolved at the micro scale being later used in a macro model such as car crash simulation?   LS-DYNA was initially approached by several actors in the automotive industry in order to develop simulation tools that would eventually allow an engineer to design a Multiphysics model for a battery pack or module that could be run as a stand-alone simulation or, later on, be included in different crash simulations at a reasonable cost. Several developments have emerged from this original ask, that are present in LS-DYNA and available to all users and engineers interested in the broad aspect of battery simulation.   In this paper, modelling techniques for the mechanical aspect of battery simulation (eg material laws), will be discussed. The BatMac module, a part of the LS-DYNA EM solver used to capture internal and external shorts will be introduced up to and including the initial heat generation, thermal expansion, and thermal runaway modelling. Validation results and workflow examples will be given. Finally, topics that are of contemporary interest to battery simulations such as busbar thermal expansion, swelling, or venting will be discussed.

Machine Learning

Deep learning methods have had a significant impact on design process in the recent past. SimAI is a deep learning-based AI platform that has shown to be very effective in approximating the behavior of fluid flow applications, especially fully developed steady state flows simulated by CFD solvers. The underlying neural networks in SimAI are very versatile and can be easily extended to structural applications as well. This study aims at demonstrating the applicability of SimAI for highly non-linear transient structural simulations like automotive crash. We use a sled model to simulate a frontal impact of the vehicle with a flat rigid-wall. Multiple training samples are generated by changing the thicknesses of several parts. This dataset is used to train a SimAI model and the resulting model can predict the full field vehicle deformation and the rigid-wall force signals within 10% relative error throughout the transient event. SimAI is many orders of magnitude faster in predicting the system behavior than direct numerical simulation and hence can be very effective in evaluating designs upfront in the vehicle development process.

Multiphysics Modelling

The study of external aerodynamics is now an integral part of the development of new vehicles. Indeed, in the current context of limiting energy consumption, it is essential for a vehicle to minimize its fuel consumption. Optimizing external aerodynamics limits the aerodynamic drag generated by the vehicle and therefore optimizes its energy efficiency. Moreover, in a car race context, it is also important to improve downforce to keep the vehicule on track in corners.

For a long time, manufacturers have used simplified models to carry out these studies. However, given the growth in computing capacity and the need to obtain results faithful to experience, the models have become more complex.

This paper helps to highlight the capabilities of the LS-DYNA software and its incompressible fluid mechanics (ICFD) solver with respect to the transient resolution of external aerodynamic problems for models with complex geometry with calculation times similar to steady state solutions. It provides a methodology adapted to the ICFD solver in order to solve external aerodynamic problems with reliability by intervening on different parameters linked to the surface mesh, the volume mesh, the boundary layer or even the turbulence model. The work presented also tests the keywords added during the latest software updates, which make it possible, among other things, to turn the wheels of a vehicle model or to simulate physical contact with the ICFD solver.

This paper presents the results of this methodology implemented to verify and improve the aerodynamic design of the Quarkus P3 which participated in the Pikes Peak race in June 2024.

Electrification and ADAS

Electronics and Lighting

Safety and Systems

Digital Engineering

Exhibition

Meals

Walk the exhibition space to grab your quick breakfast before general session.

Registration Desk Open

General Session

As the Transportation industry evolves and innovates to deliver upon the goals of clean and safe mobility, efficient engineering processes are critical.   A key enabler in shortening and making the engineering processes more efficient is simulation. Visionary automotive companies know how their world-changing ideas will perform well before prototypes and production through the use of simulation and model-based systems engineering.  No longer relegated to CAE departments, simulation has changed tremendously over the past few years, empowering every engineer to innovate faster than ever before. Learn how the latest technologies in AI/ML, physic-based numerics and cloud services have transformed workflows to deliver the latest innovations in electrification, ADAS/AD, SDV, and vehicle attributes.

General Session

The use of numerical tools and virtual analysis plays a critical role in the design and optimization of Formula 1 cars.  To deliver race-winning performance on the track, Oracle Red Bull Racing relies on virtualized design and development to reproduce real-world scenarios and re-create the complex physical environment off the track. This continuous and rapid process ensures the car is optimized for maximum performance at each racetrack. 

Gennaro will highlight how Oracle Red Bull Racing leverages Ansys technologies for aerodynamic development and thermal management to the virtual testing of impact structures. Through the accuracy and fidelity in reproducing virtual testing environments, Ansys enables the Team to reduce costs and time necessary for physical prototypes, enabling quicker iterations and innovations in the fast-paced world of Formula 1. The Innovation Partnership with Ansys is crucial for Oracle Red Bull Racing to maintain the competitive edge in Formula 1 and to deliver exceptional performance on and off the track. 

General Session

General Session

Meals

Electrification and ADAS

Electronics and Lighting

Safety and Systems

Digital Engineering

Electrification and ADAS

Ansys ConceptEV is a new innovative cloud-based design and simulation platform for the design of EV powertrains. Engineers can collaborate on a shared system simulation connected to requirements from the start of the design process. ConceptEV provides a model-based approach to optimizing the powertrain system & components with rapid evaluation of different powertrain configurations and component design choices using innovative simulation techniques.

Electronics and Lighting

The automotive industry is undergoing a transformative shift towards Software-Defined Vehicles (SDVs), where vehicle functionalities are increasingly driven by software. Managing the scalability, flexibility and interoperability of systems across diverse vehicle models is critical for successful deployment. This challenge involves the development of new hardware architectures and electronics as well as new software features. Ansys provides a comprehensive and integrated suite of tools and solutions that address the engineering challenges of software-defined vehicle development. Solutions for model-based system engineering, electronics design from chip to system, software development using real-time plant models and virtual validation of customer features. In this presentation, we will discuss how Ansys solutions enable OEMs and suppliers to partner to design, validate, and deliver the vision of the SDV.

Safety and Systems

Digital Engineering is in the focus of many companies to improve their product development. It is concerned with the application of digital methods and tools covering the complete lifecycle of products, from conception through to operation and maintenance. Ansys is going to provide our customers with a digital engineering experience covering important aspects in key areas like MBSE, model-based software development, model-based compliance, and engineering simulation. In the talk our approach to Digital Engineering is discussed with a focus on safe software and compliance to safety and cybersecurity regulations.

Digital Engineering

Automated physics-based simulation, bolstered by advancements in AI and ML, stands at the forefront of revolutionizing product development for innovative companies. This transformative technology integrates sophisticated algorithms to streamline design studies and optimization processes, significantly enhancing efficiency and accuracy in product development cycles. By leveraging AI's ability to rapidly iterate through vast datasets and simulate complex physical phenomena, companies can expedite prototyping, reduce costs associated with physical testing, and accelerate time-to-market for novel products. This paradigm shift not only fosters greater innovation by enabling engineers to explore unconventional designs with confidence but also cultivates a culture of continuous improvement and agility within organizations. Ultimately, automated physics-based simulation empowered by AI and ML represents a pivotal advancement that redefines how innovative companies conceive, refine, and bring their ideas to fruition in today's competitive landscape.

Ansys Intelligent Engineering Projects, AIEP, is a holistic solution that builds on physics-based simulation capabilities, fully automated and paired with intelligent design study techniques to leverage both classic statistics and modern data science techniques. This allows a quick adjustment of new designs to newly upcoming or changing requirements by leveraging legacy information through AI. To achieve this solution, technical domains and capabilities like physics-based simulation, automation, machine learning and optimization are assembled perfectly deploy a scalable web-based application through a data management platform to ensure data integrity, oversight, and governance.

Electrification and ADAS

One of the biggest impediments to widespread adoption of electric transportation is battery cost. Performance, safety, and hitting time-to-market all remain challenges for the EV design cycle along with an evolving technological, supply chain and regulatory landscape for batteries. Tight product cycles make it impossible to use the build, test, fix approach against a multitude of battery design choices that exist at the material and component level. Simulation is therefore critical in EV and battery product development. Cell chemistry/format, manufacturing quality, cooling system and mechanical design significantly impact vehicle level attributes such as safety and performance with some tradeoffs involved. All of the components then need to be integrated and system performance validated against real-life usage conditions. On the safety side with new mandates that require passenger warnings around thermal runaway, propagation resistance becomes important whether thermally triggered or mechanically triggered. This presentation will cover an overview of Ansys battery solutions including key challenges in battery manufacturing, safety and how system level performance and what if failure scenarios can be investigated.

Electronics and Lighting

Automotive printed circuit boards (PCBs) are the heart of all modern vehicles as they play a critical role in all advanced electronic features including autonomous driving systems, safety, infotainment, and connectivity solutions. In this presentation we will be covering the latest advancements in electronics, thermal and mechanical simulations of PCBs using Ansys tools. The presentation will highlight newer technologies to create and solve complex PCB assemblies including connectors, flex cables and housing to investigate the performance of the complete sub system and not the components in isolation, which is critical for automotive applications. Full vehicle simulations will also be presented showing virtual electromagnetic compatibility (EMC) of PCBs installed inside the vehicle along with the electrical/electronics architecture.

Digital Engineering

Capturing design insights earlier in the design process in a critical driver to shift-left initiatives, enabling increased innovation, reduced cycles times, and improved quality through earlier issue detection. Attend this session to learn how Ansys Discovery is leveraging native GPU computing and a next-generation integrated user experience to break down the barrier to upfront simulation across multiple automotive application areas, providing instantaneous design guidance where and when you need it most. Stop waiting for results and start acting on them.

Safety and Systems

An overview of the new ISO Standard: ISO/PAS 8800 (Road vehicles Safety and artificial intelligence). This standards defines safety-related properties and risk factors impacting the insufficient performance and malfunctioning behavior of Artificial Intelligence (AI) within a road vehicle context. Emphasis on the key elements of this standard such as deviation of safety requirements, data quality and completeness, SW architecture measures for the control and mitigation of failures and the evidence required to support an assurance argument for the overall safety of an automotive system that employes AI. Real life challenges to the implementation of this standard will be also covered for selected design areas. Activities I am involved in at both Aptiv and ISO/SAE/SCC to address these challenges are also highlighted.

Meals

Buffet lunch will be served during:

Advancements in Sustainable HPC Solutions for Ansys Applications

At this lunch presentation AMD and HPE will address the complex simulation challenges faced by our enterprise CAE customers. This session will focus on the deployment of highly efficient high-performance computing (HPC) clusters, power management options, advanced storage systems, and robust data management solutions. With customers relying on advanced modeling and simulation as a key business function, it’s important to choose HPC systems that optimize performance for a wide range of applications. Additionally, as thermal and power management become increasingly critical in next-generation HPC systems, customers need to understand the advantages of liquid cooling technology.

Join us to also hear about the latest performance results of AMD EPYC™ processors running Ansys CFX, Ansys Fluent, Ansys Mechanical, and Ansys LS-DYNA, demonstrating their capabilities in real-world applications.

Buffet lunch will be served during:

Case Studies in Utilizing High Performance Computing (HPC) and AI/ML for Digital Transformation

High-performance computing (HPC) and AI/ML accelerate solving complex simulations at unprecedented speeds to innovate faster.  This talk will focus on case studies of clients utilizing TotalCAE-managed HPC services for clusters and cloud to accelerate their digital transformation using HPC, cloud, and AI/ML for their CAE workflows.

Digital Engineering

Simulation has become an indispensable component of the modern product development cycle, enabling faster and more efficient design iterations. A critical aspect of this process is the digitalization and optimization of simulation workflows through the seamless integration of multidisciplinary computer-aided engineering (CAE) tools. In this presentation, we will demonstrate how to maximize the potential of your existing CAE tools by orchestrating and optimizing these workflows with Ansys optiSLang, the cutting-edge process integration and design optimization (PIDO) solution. By leveraging optiSLang, engineers can streamline complex simulations, reduce manual effort, and accelerate time-to-market. This talk will delve into key methodologies, including Sensitivity Analysis, Design Exploration, Optimization, and Robustness and Reliability assessments, showing how these approaches can improve product quality while mitigating risk and uncertainty in real-world applications.

Electrification and ADAS

In this presentation, we will focus on EV battery thermal runaway propagation simulation. In such a simulation, we model the heat generation due to exothermal reactions during thermal runaway and the subsequent heat transfer of the heat to the cooling system. The exothermal reaction models are calibrated from the accelerating rate calorimetry (ARC) data of a battery cell. The heat transfer is modelled using conjugate heat transfer models in computational fluid dynamics (CFD). Modelling of venting and vented gas reaction is also discussed. Several validated examples will be shown in this presentation including module/pack runaway propagation and the associated venting and vented gas reaction.

Electronics and Lighting

While simulation has been accepted as a best-practice in evaluating reliability of automotive electronics, the actual activity continues to be constrained. Overwhelmingly, OEM and Tier 1 organizations only evaluate reliability as a final signoff, instead of proactively integrating electronic reliability simulation into the entire product development process. In this talk, we will review current best practices for electronic reliability simulation within the automotive industry and then discuss future trends to better leverage electronic reliability simulation, including performing reliability prediction pre-layout and requiring reliability prediction simulation insight from Tier 1 and Tier 2 suppliers.

Safety and Systems

How doing our FMEDAs in Medini instead of Excel: - Improved the quality of our analysis - Resulting in a higher confidence in our design. - Made extracting data on specific failures quicker, easier and more accurate - Made estimating the risk of missing/inoperative Safety Mechanism easier and more accurate Traceability matrix between SFMEA and FMEDA improved confidence in our design.

Electrification and ADAS

Ansys Electronics continues to demonstrate its multi-decade leadership in computational electromagnetics and multi physics simulations.

In the newest release 2024R2, the unmatched market leading and multiphysics motor design simulation software, Ansys Motor-CAD, now offers capabilities that are further widening the competitive gap. Some of these new capabilities are: three new cooling methods; extending the capability of the adaptive templates; and enhanced electromagnetics.

As an advanced electromagnetic field solver that widely used for electric machine design and analysis, Ansys Maxwell in 2024R2 now has: an improved formulation for the sliding mesh interface called Continuum Air; a new reduced order model(ROM) for brush commutating machines; and a new User Defined Primitive (UDP) for hairpin coil.

Electronics and Lighting

The design complexity of high-performance electronics systems, including chip-package-board and accompanying enclosures and housings and even entire systems such as electric vehicles (EV) has dramatically increased in recent years. With further requirements to integrate high-speed digital functionality for infotainment and safety systems such as HDMI, USB, DDR4, PCIe and UCIe, reaching compliance with EMI/EMC (ElectroMagnetic Interferences and Compatibility) standards has become ever more challenging. In addition, electromagnetic co-existence issues with RF wireless protocols such as AM/FM, Satellite Radio, WiFi, Bluetooth, ZigBee, and 5G/6G can potentially occur, causing signal integrity issues within and across systems resulting in bandwidth reduction and reduced performance. Finally, the push to EV technology with the demands for higher voltages and operating frequencies has introduced some of the most challenging problems in EV development. In the worst cases, addressing EMI/EMC issues requires a significant re-design of critical EV systems resulting in increased cost of design and delayed time to market. This presentation reviews the most recent advancements in electromagnetic simulation methodologies for virtual EMI/EMC testing covering the entire design spectrum from chips to systems on to full EV capacity.

Digital Engineering

Ansys is advancing its customer experience, accelerating democratization of simulation, and powering next-generation innovation with new AI capabilities across its simulation portfolio and technical support services. As simulations are being leveraged more in early stage design processes, there is a need to run them faster compared to high fidelity simulations.

AI presents here an opportunity to address key challenges and scale the solution to non-experts. Ansys SimAI, a cloud-enabled physics-neutral platform, empowers users across industries to greatly accelerate innovation and reduce time to market. SimAI allows engineering teams to predict performance of complex problems in minutes, encouraging performance improvement, progress and innovation.

AI Models rely heavily on high-fidelity data with several data points and accuracy. Leveraging the right data becomes important for a good AI Model; this is where Ansys Minerva comes in. Minerva offers traceability, versioning and data driven insights across a centralized, access controlled repository thereby complementing and enriching the SimAI workflow.

Safety and Systems

Modern vehicles are getting more and more complex and an approach that utilizes industry best practices is needed to prevent the vehicle from behaving in ways that are unwanted and/or dangerous. This presentation describes an approach based on a process model, automotive functional safety and Safety of the Intended Function as well as automotive cyber security to prevent unexpected behavior. We will investigate how the process can be used to ensure that requirements are documented, understood and implemented. Then we will use ISO 26262:2018 to explain how unreasonable risk due to E/E malfunctions can be prevented. After that we will discuss the use of ISO 21448 (SOTIF) to prevent malfunctions caused by technological insufficiency and foreseeable misuse. The final layer of protection is achieved by using ISO 21434 cyber security to identify and analyze hacking attack risks.

Digital Engineering

Honda has been constructing a unique material intelligence digital platform using material database Granta for the purpose of highly efficient, environmentally friendly R&D. We’ve designed a material database structure ’schema’ , which is easy to be utilized for fields of design, simulation (functional and manufacturing CAE, additive manufacturing, materials informatics), additive, procurement, and material development.  Over thousand users in the company accesses to the in-house material database and we’re now tackling to enlarge its application for Honda products not only automotive, but motorcycle, power products, aerospace, and other technical fields. Besides, under VUCA circumstance for future, we suggest a prospective digital utilization on material information toward environmental, regulatory issues, and AI technology progress.

Electrification and ADAS

The electric powertrain has become a pivotal component in modern electric vehicles (EVs), directly impacting their performance, efficiency, and user experience. Ensuring the durability and reliability of the electric powertrain is crucial for the long-term success of EVs. This study focuses on developing predictive models for the durability of electric powertrains, with particular attention to Noise, Vibration, and Harshness (NVH) characteristics, which are critical to both the perceived quality and mechanical integrity of the system. By integrating advanced simulation techniques, material fatigue analysis, and real-world testing data, the research aims to enhance the accuracy of durability predictions and optimize NVH performance. The findings provide insights into the key factors affecting powertrain longevity and noise reduction, offering valuable guidance for the design and engineering of more robust and quieter electric vehicles.

Electronics and Lighting

EMC is a challenging topic for power electronic applications that are trying to switch more power at higher speeds inside smaller packages. Early system insights are valuable since EMC compliance testing failures are notorious for delaying product release and costing lots of time and money late in the development cycle. In this presentation we will look at ANSYS simulation capabilities for power electronic EMC analysis of electric vehicle drivetrains that predicts switching transients based on real-world packaging parasitics and mutual coupling between traces and components.

Safety and Systems

Legacy approaches to automotive system design often consider systems engineering and systems safety analysis as critical yet separate engineering activities. This results in specialized but siloed and disconnected processes, ill-equipped to efficiently respond to industry trends of increasing product complexity and reduced development times. To counter these challenges, as in other industries, automotive systems engineering has adopted a Digital Engineering approach centered around a model-based representation of the design intent. However, unlike other industries, model-based work products in automotive are not a direct deliverable to our customers. Why then do we invest significant engineering resources into model-based systems engineering (MBSE)? This presentation will highlight the value proposition of closely coupling automotive systems engineering and safety workstreams via a shared, common SysML model. Discussion will include how the model enables an integrated development process where safety is designed into the system from day one. Additional return on modeling investment will be shown in the form of integrated system simulations, efficiencies in system verification and validation, and accelerated design certification and instantiation.

Electrification and ADAS

Modern vehicles put new demands on electronic devices and systems for the control and conversion of electrical power. Electrification promises more efficient powertrains with increased range, faster charging, and lower weight but they also introduce new sources of electromagnetic interference (EMI). The design and optimization of these systems are essential to achieving greater power density, efficiency, and reliability. This presentation will introduce the extensive simulation capabilities used to design and fine-tune power electronics, predict their behavior under both normal and faulty conditions, and proactively address potential issues – from analysis and sizing of power converter topologies to reduced-order modeling of electromagnetic components and electronics reliability.

Electronics and Lighting

Lighting technology is significantly evolving. Displays are becoming larger, brighter, and multi-dimensional. Lighting systems are more customizable, flexible, adaptable and complex as ever. Imaging sensors are getting smaller, while image quality is getting higher. For many of these optical systems, challenges are being solved at the micro-level, but system-level integration and performance are equally as complex. With the Optics portfolio at Ansys, we can bridge the gap between microstructure optimization and system level performance. Many advances in our Optics products have enabled us to keep up with the evolving lighting & sensing technologies, such as improved GPU acceleration, stray light analysis, and full optical product workflows. Join to discover the Ansys Optics solution and its recent innovations.

Safety and Systems

The complexity of an open pit mining Fleet Management System (FMS) presents challenges for Medini Analyze FFMEA tools. The FMS is structured into four main architectural layersSite Operations, FMS, Autonomy, and Drive-By-Wire each containing multiple internal layers and many functions. The AIAG-VDA 2019 FMEA Handbook reflected in Medini does not directly provide a concept of system nesting or module reuse found in the design. This session explores an example of an open pit FMS and an approach using Medini to address its complexity. The approach is a work in progress, building on lessons from previous efforts and enhanced through collaboration with the Medini Analyze Expert Team.

Digital Engineering

Automotive external aerodynamics is a key factor in vehicle design, impacting aspects such as performance, fuel efficiency, stability, noise, and comfort. In electric vehicles (EVs), aerodynamic performance is especially critical as it significantly affects battery range and longevity. Moreover, EVs generate significantly less engine noise compared to traditional combustion engines, making aerodynamic designs crucial for achieving acoustic comfort. Another crucial area for the automotive industry is thermal management. Efficient thermal management systems help maintain the right temperature for the engine, battery, and other critical parts, ensuring optimal performance and longevity of vehicle components. This presentation will showcase the latest Ansys solutions in the field of aerodynamics and thermal management. We will also demonstrate how our Native Multi-GPU solver can accelerate simulations by an order of magnitude, enhancing the accuracy of aerodynamic predictions and enabling faster design iterations and better optimization. Lastly, we are thrilled to introduce our latest AI innovation, Ansys SimAI, which promises to revolutionize data-driven design.

Meals

Digital Engineering

Structural simulation and Durability prediction is paramount to next generation vehicle validation which ensures the reliability, safety, and performance of vehicles under various conditions like different loading conditions, environments, and usage scenarios. Electrification, rapid technology advancements and shorter product development cycles demands rapid advancements in simulation best practices in a form of new modeling & meshing practices, different connection types, innovative simulation workflows, solver accuracy, performance, high performance computing, incorporation of manufacturing aspects into simulation, test correlation at different stages and ability in running end to end durability workflows to automate process of durability performance improvement. In this presentation, we will show how Ansys structural and durability solutions will help deliver next-generation vehicles that provide a safer, more comfortable passenger experience. We will cover different approaches customers are adopting both solver and workflow perspectives for component, system and vehicle level simulations.

Electronics and Lighting

The design of automotive lighting requires a deep understanding of optical, thermal, electronics, and structural domains to comply with stringent legal requirements. The complexity of automotive lighting systems and the integration of their subsystems necessitate feedback across these different physics domains. This presentation will address several key aspects, including the optical design considerations of automotive lighting, which involve optical design principles, light source creation, materials definition, and the integration of data across various software platforms. It will also explore the capabilities of automotive lighting in validation and optimization, aiming to enhance product workflows while reducing costs and time to market. Furthermore, we will illustrate how high-fidelity computer simulations can be utilized to understand the effects of these physics domains and their interactions on automotive lighting design, which is crucial for achieving operational success, refining design decisions, and accelerating the product lifecycle.

Safety and Systems

Advanced software engineering tools can be used to generate test cases and test case sets of inputs and expected results.  This presentation will go through examples of using SCADE tools to generate test cases for models as well as exploring natural language processing based tools including generative AI to develop test cases.

Electrification and ADAS

Join us for an exclusive presentation where Ansys unveils the future of highly automated driving. Discover how we tackle the engineering challenge of ensuring safety across diverse scenarios through model-based systems engineering. Our approach to safety encompasses design integration, verification and validation, and incremental safety case development, all supported by advanced simulation and analysis. You will also gain insights into Ansys's commitment to safety and simulation at scale, highlighted by an exclusive testimonial from OEM customers introducing the 'Personal Pilot L3' in the new BMW 7 Series.

During the presentation, you will learn how to effectively integrate safety into design and verification processes, navigate system limits with a focus on safety and SOTIF (Safety of the Intended Functionality) during development and validation, and utilize synthetic sensor data for perception machine learning and AI training and validation. Additionally, you will explore how to leverage simulation at scale to validate autonomous driving features, including sensitivity and reliability analysis in scenario-based simulations.

Keywords include simulation, virtual testing, toolchain, sensor, functional safety, SOTIF, safety case, MBSE (Model-Based Systems Engineering), synthetic data, machine learning, AI, radar, camera, lidar, ultrasonic, sensor fusion, and cloud.

Electronics and Lighting

Vehicle vision ergonomics are becoming more and more relevant in todays automotive market. The comfort and safety of the passengers in a vehicle might be affected by different natural and artificial light sources. Interior lighting, sun reflections, displays and external lamps must be taken into consideration during the development of an optimum cabin. The human factors are no longer a subjective topic, and thanks to Ansys Speos and its virtual human vision capabilities, an infinite number of scenarios can be assessed, and a robust design can be achieved.

Electrification and ADAS

ADAS/Autonomous vehicle Software is very complex as SAE level 5 autonomous vehicle probably will have 1B SLOC vs 14 M SLOC in Boeing Dreamliner (As per Bloomberg energy report). Testing of such complex software is challenging and also time consuming. Product quality and maturity for such high complex ADAS/AV is function of testing at various levels like SW testing, integration testing, System Qualification Testing and vehicle testing. With conventional approach ADAS features get tested only at fag end of the project as it requires availability of vehicle with fully equipped sensors and actuators needed. This is extremely risky for meeting the stringent quality standard of safety critical systems, and extremely expensive. With development and maturation of artificial scenario and sensors simulation, Virtual Validation is gaining prominence as it address all demits of conventional development and testing approaches and gives edge by left shifting features testing along side of SW development. With increasing fidelity of simulation and realistic visual orchestrations, feature function testing gets left shifted and performed at bench level and use vehicle to measure Key Performance Indicators. With developments on physics based Radar model from companies like Ansys (AVx) enable configuration of antenna properties and give out ADC signal level data Virtual RWUP will gain prominence and minimize expensive Real World user Profile tests (RWUP) in real vehicle, and also enhance coverage of testing towards Safety of Intended Functionality (SOTIF) standards. Smart tradeoff between tests segregation between vehicle and simulation, makes projects execution faster, and cost effective.

Safety and Systems

Among aircraft systems, the Fly-by-Wire (FBW) system is one of the most safety critical and it matures throughout the flight test campaign. During the campaign, many software loads are released, and several software verification activities must be satisfied before each of those software loads are allowed to be flown. At EMBRAER, SCADE has played a pivotal role for over 15 years in allowing the quick deployment of FBW software releases used throughout all its business units.   The latest version of SCADE is being used by EMBRAER in the development of the FBW for EVE - a leader in EVTOL industry. The brightest new feature in this latest version is SCADE Model Coverage Assistant (MCA), which is being used for the first time. This discussion will describe how EMBRAER believes how SCADE MCA will considerably alleviate verification activities and reduce software lead-times during the most strenuous phase of the aircraft development.

Digital Engineering

In the drive towards an electrified and sustainable future, automotive companies are increasingly adopting simulation technologies to accelerate design cycles and reduce costs. As technology complexities escalate, there is a growing need for an integrated development approach that emphasizes early-stage development and validation processes. Engineers are turning to simulation to address complex system requirements, but achieving comprehensive system validation through virtualization requires the creation of high-fidelity, physics-based plant models. These models, calibrated using machine learning and integrated with software functions and real-world driving scenarios, enable a virtual replication of the vehicle. Moreover, companies are now extending these models from product development into operational phases, leveraging the power of Hybrid Digital Twins for real-time fleet management. In this session, Ansys experts will explore the use of simulation methods, reduced-order models (ROMs), and AI/ML techniques for performance validation through model-in-the-loop (MIL), hardware-in-the-loop (HIL), and virtual drive tests. The discussion will also cover how to combine data and physics to create hybrid digital twins, which offer online insights into component operation through virtual sensors, predict service life, and optimize operating conditions. Key takeaways include understanding Hybrid Digital Twins—why and how to implement them, high-fidelity system simulation through reduced-order models, integrating test and 1D data with 3D data for accurate virtual validation, and the role of virtual sensors in predictive maintenance, all powered by physics and refined by data.

Digital Engineering

NVIDIA Modulus is a state-of-the-art, open-source scientific machine learning platform that enables research, development and deployment of surrogate ML models for a wide range of engineering applications, such as external aerodynamics, cabin cooling, electro thermal cooling simulations etc. The platform offers training pipelines that enable development of optimized and scalable end-to-end workflows for ingesting large simulation datasets, training models in a distributed manner on multiple GPUs, physics-based validation and ease-of-deployment. It offers unique tools for embedding physical constraints derived from governing equations into ML models to increase their robustness and accuracy state-of-the-art model architectures. It also offers specialized ML model architectures that cater to specific engineering applications. In this talk, we will demonstrate these capabilities of the NVIDIA Modulus platform to Develop end-to-end training pipeline for building surrogate models Show the benchmarking workflow to evaluate and validate different model architectures This will be demonstrated in the context of two use cases external aerodynamics and structural analysis of cars.

Electronics and Lighting

Heads Up Display (HUD) systems present information within the driver's field of view, allowing them to keep their eyes on the road at all times. HUD systems have been around for over half a century, but a renewed interest and investment in this technology is currently taking place. This is being driven, in large part, by the advent of Advanced Driver Assistance Systems (ADAS) where the need to display safety-critical information effectively, with minimal distraction, is essential. To keep up with the growing demand and complexity of HUD systems, engineers need a fast and effective way to design, optimize, test, and validate them. This includes defining functional specifications & quality targets, assessing key performance metrics, and visually perceiving the system as the driver would. Ansys optical solutions provide purpose-built tools and workflows to virtually design, assess and test a HUD prototype under various driving conditions. This presentation will demonstrate how optical simulation and virtual prototyping can help solve the challenges of HUD design.

Electrification and ADAS

This joint presentation by Jeff Blackburn of Ansys and Chris Manning of dSPACE will discuss the new FMVSS-108 Adaptive Driving Beam (Smart headlamp) regulations, and how SiL and HiL simulation can be used to greatly reduce the need and inconvenience of track and road nighttime physical testing, saving time and money Integrating the Ansys AVx physics accurate camera and headlamp models with the dSPACE ASM 3D driving simulator and vehicle dynamics models yields a SiL virtual twin of your vehicle and ADB exterior lighting system allowing you to test your ADB headlamp function and control logic virtually on the desktop. 

Using a dSPACE HiL bench with an ESI (environmental sensor interface) module, the Ansys AVx simulation generated raw image frames can be directly injected into the real camera’s image processing chip. This let’s you test the ADB headlamp function and control logic using the real camera’s perception software in the convenience of the lab, versus having to send your engineering team out at night.

Safety and Systems

Using the scenario of an electronic powertrain, we demonstrate how a suite of digital engineering tools using an Model-Based Systems Engineering (MBSE) approach. This will demonstrate how different engineering domains can collaborate and connect with tighter integration between design, analysis and simulation.

Electrification and ADAS

Electronics and Lighting

Safety and Systems

Digital Engineering

Exhibition

Meals

Walk the exhibition space to grab your quick breakfast before general session.

Registration Desk Open

General Session

As the Transportation industry evolves and innovates to deliver upon the goals of clean and safe mobility, efficient engineering processes are critical.   A key enabler in shortening and making the engineering processes more efficient is simulation. Visionary automotive companies know how their world-changing ideas will perform well before prototypes and production through the use of simulation and model-based systems engineering.  No longer relegated to CAE departments, simulation has changed tremendously over the past few years, empowering every engineer to innovate faster than ever before. Learn how the latest technologies in AI/ML, physic-based numerics and cloud services have transformed workflows to deliver the latest innovations in electrification, ADAS/AD, SDV, and vehicle attributes.

General Session

The use of numerical tools and virtual analysis plays a critical role in the design and optimization of Formula 1 cars.  To deliver race-winning performance on the track, Oracle Red Bull Racing relies on virtualized design and development to reproduce real-world scenarios and re-create the complex physical environment off the track. This continuous and rapid process ensures the car is optimized for maximum performance at each racetrack. 

Gennaro will highlight how Oracle Red Bull Racing leverages Ansys technologies for aerodynamic development and thermal management to the virtual testing of impact structures. Through the accuracy and fidelity in reproducing virtual testing environments, Ansys enables the Team to reduce costs and time necessary for physical prototypes, enabling quicker iterations and innovations in the fast-paced world of Formula 1. The Innovation Partnership with Ansys is crucial for Oracle Red Bull Racing to maintain the competitive edge in Formula 1 and to deliver exceptional performance on and off the track. 

General Session

Crash I

Seats play a crucial role in ensuring the safety of occupants during crashes. Seat integrity is a key factor in minimizing occupant injuries. In this study, the third-row seat of a full-size SUV was occu-pied by three Humanetics Hybrid III 50th percentile dummies in a high-fidelity model. The front crash acceleration of FMVSS208 (56KPH rigid barrier crash) was applied to assess the seat's integrity. Additionally, to account for the impact of parameter variation, a comprehensive set of variations was applied using Ls-Opt. This aimed to investigate the robustness of seat integrity performance considering parameters such as H-point, material properties, part thickness, and acceleration variation.

Forming I

Stamping simulation includes: 1) forming simulation – modeling large plastic deformation during in-die forming operation and predict failures, such as wrinkling, fracture, and springback; 2) out of die handling simulation – modeling handling operation and predict dynamic effect caused surface and dimensional defects. Using rich application programming interface (API), scripts have been developed to streamline the modeling and failure evaluation of stamping simulation in LS-PrePost. The developments will be demonstrated through two case studies: 1) realization a damage-based failure index tool in hot stamping fracture prediction; 2) Graphic user interface (GUI) for parameter inputs and automatic modeling drop test. In addition to more than 90% efficiency improvement, the API supported scripts also democratize complex simulation modeling and reduce human errors.

Occupant and Pedestrian Safety I

Virtual testing applications are rapidly growing in the ever-changing landscape of automotive mobility applications. The key drivers to the transformation in mobility are technological changes for the future of mobility such as ADAS (Advanced Driver Assistance System), AV (Autonomous Vehicle). These technological evolutions will require assessment of a variety of safety scenarios with virtual assessment tools and would not be practical with physical testing only. One such virtual assessment tool in roadmap by consumer testing agencies like Euro NCAP and C-NCAP and other regulatory bodies is the Finite Element (FE) Human Body Model (HBM). The automotive industry must adopt HBM and other virtual safety tools to meet the requirements set forth by the consumer agencies and regulatory frameworks. The FE HBM representing the human is the most promising virtual assessment tool to support virtual testing in conjunction with anthropometric test devices (ATD) used for virtual and physical testing. HBM needs to fulfil the requirements for virtual testing by demonstrating compliance and should also meet the need of industry in product development applications. This requires HBMs to be omnidirectional biofidelic, user friendly (easy-to-use), robust, computationally efficient (fast), and certifiable by the virtual testing requirements from various agencies. HBM should also be capable of assessing injuries relevant to the accident field data in the most comprehensive manner to elevate the vehicle safety features.  Another requirement is the ease of positioning the FE- HBM in the virtual testing environment in an effective manner. With the Accelerated Positioning Tool (APT-C), this process is streamlined and automated, by employing advanced methodologies such as Optimization and Machine Learning. Through these techniques, precise marker-based positioning of HBM models is achieved which includes model position, seat-squash, and belt routing. Additionally, the tool incorporates spine-posture calculations derived from data obtained in the UMTRI volunteer study, further enhancing its accuracy and applicability. The developed HBM-C and APT-C show promising capabilities to be used for virtual testing and product development aimed to improve vehicle safety applications and grow to other industries like aviation, defense, space, and sports (NFL, NASCAR) applications. This proactive approach to safety assessment is essential in the dynamic landscape of technological advancements and evolving safety regulations.

Simulation Methods I

In vehicle development CAE plays crucial role in arriving at optimum structural design to meet various vehicle performance targets in different domain such as Crash, NVH, Durability etc. Accurate CAE methodology can aid in reducing the number of physical tests & reducing overall vehicle development time. However, there are instances where there are gaps observed between test results and CAE predictions. These gaps get amplified in crash simulations as the event is highly dynamic and non-linear behavior simulation is always challenging. In order to enhance CAE methodology, it was decided to incorporate the effect of manufacturing and testing variations in crash CAE simulations. Manufacturing process accounts for variations due to inherent variation in material properties, spot weld nugget diameter, manufacturing processes such as stamping etc. whereas Physical Testing houses variation in barrier positions, test speed etc. within specified tolerance defined by regulatory bodies. These variations affect structural performance and negating these issues in early design phase will help to arrive at robust structural design. In this paper, the CAE based approach for accommodating manufacturing and testing variation in crash CAE simulation for arriving at robust BIW design is described. In the current work, unsupervised machine learning based CAE approach is used to identify variations in structural performance arising out of manufacturing and testing variations. This paper also describes accuracy verification of this CAE approach based upon its comparison with quasi-static experimental test.

Crash I

Demands for ever increasing efficiency of automotive crash analysis have continued to rise in the drive to reduce automotive development costs. When major design changes are required to satisfy product performance late in the development process, significant cost and time are required to implement them. To alleviate this, automotive manufacturers have adopted the concept of "front-loading" to identify problems early-on in the development process. The earlier issues in the production phase can be identified, the more efficiently and effectively development can be performed. JSOL Corporation has developed a new method for modelling automotive body structures to simulate crash analysis in the early stage of design that employs Hughes-Liu beam elements with arbitrary cross-sectional geometry. Then JSOL Corporation has released a new modelling tool J-SimRapid that can easily create the reduction models. This tool enables front-loading crash safety analysis in the conceptual design phase. Furthermore, it can also reduce large-scale models in the detailed design phase. Consequently, J-SimRapid makes overall automotive design phases more efficient. In this presentation, the new model reduction method will be introduced along with examples of reduced model analysis by J-SimRapid.

Forming I

Achieving a sheet metal part within dimensional tolerance without the need for tool recutting is the ultimate goal of every forming simulation. Several key factors are essential to reach this goal: an accurate forming simulation with precise material descriptions, correct friction values, and an accurate binder model to determine the correct material flow and stress state of the part after forming. Additionally, an accurate springback calculation is required, considering the part clamped in the measurement device and the effect of gravity. With these results the new tool geometry has to be determined to compensate the springback of the sheet metal part.

Occupant and Pedestrian Safety I

For airbag deployment simulation, CPM is widely used due to its usability and cost performance. On the other hand, it has some difficulty in representing the gas flow into the narrow space like curtain side airbags. CPG is a new method which solves the Navier-Stokes equation for a space discretized by particles, and is expected to be able to solve his problem by representing pressure propagation and its effect to the fabric more accurately. This paper shows the preliminary validation results comparing test results and CPM for several airbags. CPG shows some better behaviors compared to conventional methods and possibilities to apply to the airbag deployment simulations.

Simulation Methods I

Accurate simulation of solder reflow processes during Integrated Circuit (IC) packaging process is essential for ensuring the structural reliability and thermal behavior of electronic devices. In this presentation, a new approach utilizing the adaptive Incompressible Smooth Particle Galerkin (ISPG) method in LS-DYNA is presented. This method aims to simulate solder reflow processes during Flip Chip Ball Grid Array (FCBGA) die attachment processes and overall IC assembly. The ISPG method combines the advantages of incompressible fluid simulations and SPG (particle) techniques using a Lagrangian approach. Existing methods such as Surface Evolver or Fluid-Structure Interaction (FSI) techniques often struggle to accurately represent the interactions between solid composites and fluid-like molten solder balls during their reflow process. They also face challenges in maintaining efficiency and computational feasibility when simulating complete packages. This work demonstrates the effectiveness of the new approach called Adaptive ISPG which has been recently implemented in LS-DYNA.  The ISPG technique utilizes a Lagrangian particle-based approach to solve the Navier-Stokes equation, specifically designed for modeling molten solder balls used in IC packaging. This technique seamlessly integrates with the structural physics capabilities within the LS-DYNA solver. The evolution of solder ball shapes is determined by the material's surface tension and the boundary conditions set by the structure. The adaptive feature of ISPG further enhances its capabilities, enabling improved flow behavior in narrow gaps and around sharp corners, which provides more accurate and realistic simulations of the solder. This novel workflow offers significant potential in optimizing new parameters of packaging assembly process through simulation-guided adjustments, enhancing the reliability and performance of IC packages.

Crash I

Virtual validation activity performed to fullfill all the ECE-R21 / FMVSS 201 standards requirements. Deep investigation on how dashboard stiffness will impact all the parameters to be checked like glass survival, deceleration, reaction force, gap and flush.

Forming I

The development of hot forming functionality in Ansys Forming has been a collaborative effort with BENTELER Automotive as our industrial partner.

Hot forming simulation necessitates accurate computation of both temperature distribution and material deformation of the sheet metal part. Historically, setting up a hot forming simulation in LS-DYNA demanded extensive finite element (FE) expertise, posing a significant barrier for many companies.

To address this, we collaborated with BENTELER to define the requirements for an intuitive and user-friendly graphical user interface (GUI). Our aim was to streamline the simulation setup process so that users only need a comprehensive understanding of the hot forming process, rather than deep FE expertise.

In this presentation, we will demonstrate how we achieved this goal with Ansys Forming. We will cover the defined specifications, underlying assumptions, and the implementation process in both Ansys Forming and LS-DYNA. Additionally, we will showcase a case study from BENTELER, detailing the simulation setup and results. Finally, we will provide an outlook on future development plans for hot forming simulations with Ansys Forming.

Occupant and Pedestrian Safety I

The authors have previously published a paper characterizing a laminated glass pane to simulate the delamination and fragmentation response of glass in a blast event. In this study, a computational model in LS-Dyna was developed and correlated with physical testing of laminated safety glass panes subjected to blast loads. To generate an accurate model, validation of the component parts including Polyvinyl Butyral (PVB), adhesion and glass that make up the laminated safety glass was completed.

In a new study, the authors using this model undertook several pedestrian head impact tests on a windshield in accordance with the Euro New Car Assessment Program (NCAP) and UN ECE R127 to determine its suitability. Building on this model, the glass material was further refined to correlate with previous physical tests completed with an emphasis to correlate against the head injury criterion (HIC) number as well as match the acceleration vs. time history curve. Following refinement, more physical testing was completed which was compared to the model and showed good agreement between the physical testing and model. Furthermore, when compared to the old approach to simulate pedestrian head impact on a windshield, the new approach demonstrated better membrane action following glass cracking which resulted in better correlation to the acceleration vs. time history curve. The approach and results of the study are presented within this paper.

Simulation Methods I

Accurate simulation of solder joint shapes and dimensions during the Flip Chip Ball Grid Array (FCBGA) packaging reflow process is crucial for ensuring signal integrity and structural reliability in electronic devices. This presentation introduces a novel approach utilizing the adaptive Incompressible Smooth Particle Galerkin (ISPG) method in LS-DYNA.   Our methodology involves calibrating the ISPG model using NVIDIA's FCBGA, where a set of optimal parameters was identified. To validate the calibrated model, various FCBGA designs were simulated and compared against experimental solder joint cross-sectional data. The results demonstrated a strong correlation between the calibrated model and the experimental data, confirming the high accuracy of the ISPG method.  This study highlights the significant impact of ISPG material model parameters on solder joint morphology, aiming to improve the design process by identifying key parameters.

Buffet lunch will be served during:

Advancements in Sustainable HPC Solutions for Ansys Applications

At this lunch presentation AMD and HPE will address the complex simulation challenges faced by our enterprise CAE customers. This session will focus on the deployment of highly efficient high-performance computing (HPC) clusters, power management options, advanced storage systems, and robust data management solutions. With customers relying on advanced modeling and simulation as a key business function, it’s important to choose HPC systems that optimize performance for a wide range of applications. Additionally, as thermal and power management become increasingly critical in next-generation HPC systems, customers need to understand the advantages of liquid cooling technology.

Join us to also hear about the latest performance results of AMD EPYC™ processors running Ansys CFX, Ansys Fluent, Ansys Mechanical, and Ansys LS-DYNA, demonstrating their capabilities in real-world applications.

Aerospace Structure Impact and Dynamics I

The high strength, toughness, quasi-ductility over monolithic ceramics, and elevated temperature oxidation resistance make carbon-carbon (C/C) ceramic matrix composites (CMCs) excellent candidates for hypersonic vehicle components expected to experience high strain rates and high temperatures in service. However, accurate characterization of the material behavior under such extreme/harsh conditions presents significant challenges. This work will present the results of LS-DYNA computations conducted in support of an ongoing Southwest Research Institute (SwRI) internal research (IR) program focused on the high temperature, high strain rate behavior of C/C composites. The goal of this IR program is to develop and fabricate a system capable of rapidly heating metals and C/C CMC test specimens up to 4000°F to facilitate elevated temperature tensile split-Hopkinson pressure bar (SHPB) testing. This talk will provide an overview of SwRI’s dynamic material testing capabilities, challenges associated with the current effort, and will primarily focus the use of explicit finite element (FE) simulations to support the experimental program. Results of a computational investigation into apparent non-equilibrium behavior exhibited in previous SHPB tension tests conducted on a commercially available C/C composite material will be presented. Said non-equilibrium behavior is suggested by the measured signals on the input and output bars during these tests, particularly the dissimilarity of the transmitted wave and the sum of the incident and reflected waves. In the computations, the entire SHPB set up is represented/meshed. Two different approaches are taken to model the SHPB tension test coupons. The first approach considers the woven C/C CMC as a smeared homogeneous continuum. These continuum simulations resulted in sudden, brittle failure, which leads to wave attenuation/dispersion of the transmitted strain signals (i.e., the transmitter bar strain signals decrease in amplitude with increasing propagation distance). To correlate the simulated and experimental strain amplitude at the location of the transmitter bar strain gage in the tests, it was found that the value of the maximum principal stress at failure in the continuum models needed to be increased to what the authors believe is an unrealistically large value. It was also found that the continuum modeling approach is unable to capture the widening of the transmitter bar signals that is present in the SPHB experimental data. In the second modeling approach, the woven C/C CMC mesostructure (i.e., the tows and matrix) was explicitly modeled. Compared to the continuum simulations, the mesoscale simulations exhibited a more progressive failure, which was found to result in enough additional “ductility” such that the amplitude of the strain and force signals did not decrease with increasing propagation distance along the transmitter bar. Additionally, the mesoscale simulations resulted in transmitted strain signals that were wider those in the continuum simulations and were in good agreement to those in the experiments. Whereas the maximum principal stress at failure used in the continuum models had to be unrealistically increased to correlate the simulated transmitted strain signals to those in the experiment, this was not the case for the failure properties used for the tows and matrix in the mesoscale simulations, highlighting the importance of the mesoscale approach.

Human Body Models

THUMS (Total Human Model for Safety) is a detailed biofidelic finite element model of the human body, which encompass different genders and physiques including detailed anatomical features of the skeletal structure, internal organs, and other soft tissues like skin, flesh, and ligaments. The application of THUMS in numerical simulation offers exciting opportunities in automotive and civil aerospace development in areas such as safety, comfort, and ergonomics. These models will play an increasingly important role in the study of human body kinematics and assessment of injury risks in collision accidents.   The Civil Aviation University of China (CAUC) aims to establish a posture database according to civil aviation standards and perform detailed numerical studies on the biomechanical response of aircraft occupants using THUMS in a variety of simulated impact scenarios. CAUC collaborated with Arup and its software house Oasys LS-DYNA, to help build this posture database and explore the feasibility of using Oasys PRIMER (a world leading LS-DYNA pre-processor) with its comprehensive human body model positioning tools and THUMS positioning metadata to achieve complex emergency braced postures.  This presentation describes the entire positioning workflow used to achieve complex brace postures using the multi-stage positioning method in Oasys PRIMER and the *CONTROL_STAGED_CONSTRUCTION keyword in LS-DYNA. Further investigations were made to optimize simulation run times. Using a generic aircraft seat, a series of standard dynamic load cases are performed to predict occupant kinematics and the extent of injury risk to the aircraft occupant. This research will contribute to the wider application of THUMS in the aviation industry and promote biomechanical research into aircraft occupant safety.

Material and Constitutive Modelling I

Joining is a critical part of any structure for transferring load and maintaining integrity for the product. Ultrasonic weld is one of the popular methods for joining plastic parts in automotive industry. Along with providing a visually demanding finish, the method has been established for tight, strong, and dimensionally accurate joints. With the increase of complexity and integration of electrical and sensing instrumentation in autonomous and electric vehicles, ultrasonic weld provides a necessary means of attaching parts into plastic parts without compromising visual impact. However, the sonic weld performance is yet to be quantified, and the criteria for capturing weld separation, and losing this connected load path during structural vehicle analysis, has not been studied extensively.  Sonic welds, even though it is a very effective joining method, the whole welding tooling process is expensive and time consuming. Ideally, to optimize the welding spot number and develop future cost-effective welding method, it is crucial to understand the actual weld performance under various variables such as material, thickness, temperature, strain rates etc.  In the following study, sonic weld performance has been evaluated in five different material combinations, three different strain rates and three different temperatures. Based on the analysis, a fully characterized FEA sonic weld modeling method has been developed, which captures weld separation, for in-production parts joined with sonic welding method. This method can be applied on full vehicle level analysis, like front and rear low speed, and enables for optimized design by determining an ideal number of sonic welds necessary for this type of structural loading.

SDM

In recent years, the speed required for product development has increased significantly. With the increasing sophistication of requirement levels, data-driven development utilizing past data is gaining attention in the automotive industry. Compared to physical testing, simulation is characterized by the ease of obtaining information through calculation and analysis, but on the other hand, handling huge amounts of data is a challenge. In this paper, we propose a new process to search for similar behavior of the part of interest from dozens of crash simulation results by using the order-reduction technique. Assuming that an irregularity occurred in the behavior of a member, the modeling history database was searched for members with similar irregularities using the shape of the member as a key, and highly similar behaviors were found in the database.

Aerospace Structure Impact & Dynamics I

Impacts at or exceeding speeds of 7 km/s are classified as hypervelocity impacts (HVI). Micro-meteorites and space debris travelling at these speeds pose a present and prevalent threat to the operational safety of satellites. The damage can be measured through loss of operation and the corresponding economic expenses. HVIs against satellite shielding structures need to be understood to improve their design, however, physical testing is usually associated with high economic expenses. A commonly accepted and widely used alternative relies on numerical modelling of HVI shielding structures. More recently, the use of advanced composite materials in space applications, such as carbon fiber reinforced polymers (CFRPs), has increased. This necessitates the development of accurate numerical modelling techniques adept at predicting failure mechanisms and damage under these extreme loading conditions. Generally, numerical techniques employed in HVI modelling rely on the Lagrangian implementation of the finite element method (FEM; well-suited for tracking relative motion between interfaces, useful in modelling delamination) or the meshfree smoothed particles hydrodynamics method (SPH; excellent for replicating extreme deformation and fragmentation). To leverage the benefits of both methods and capture the wide range of failure mechanisms and damage occurring simultaneously, this study employs an adaptive FEM-SPH method. 16-ply laminated composites under HVI by three different types of orbital debris (steel, aluminum and nylon, classified as high, medium and low-density materials by NASA, respectively) are studied. Crater size and delaminated area are used as comparison metrics to gauge the accuracy of the numerical models and variations in the prediction of these features are explored and quantified across different composite material models.

Human Body Models

HBMs have become a much-needed tool for the safety simulations of the automotive industry. Out-of-position load cases for passengers and simulations for other vulnerable road users, like pedestrians and cyclists are increasingly needed, and HBMs can address this need. Euro NCAP in its Vision 2030 plans to replace ATDs with HBMs and the same is expected by other consumer information programs.  Nevertheless positioning, pre-processing and post-processing of these models on a level suitable for industrial use has not been straight-forward until now.  ANSA, offers a novel solution to this complex problem, making the positioning and handling of an HBM, as easy as with an ATD model. Using an advanced integrated MBD solver in parallel with morphing algorithms, engineers are provided with real time articulation and positioning of a HBM within an easy user interface. While the user just articulates the human model with the mouse in a most direct way, the biofidelic joint modelling guarantees realistic model movements and the generation of a ready-to-run model without the need of pre-simulation.  In parallel we are developing tools to tackle the problem of producing variants of an HBM adapting it to different anthropometries, thus better representing the population variability. We are addressing the problem with morphing and remeshing tools along with methods of incorporating anthropometric data into our algorithms.  Vulnerable road users is another area of great interest where HBMs are the only choice. We are conducting our own research projects related to cyclists' postures. Through statistical processing of laboratory scanned data we aim to produce statistical rider models that will be applied on the HBMs. This gives the possibility to the engineers, to simulate crash events of any kind of bicycle for any rider anthropometry.  On the post-processing side, running interactively or in batch mode, the META HBM tool automatically creates PPTX and PDF reports including videos and images of GHBM's kinematics, strain contour plots, elements erosion identification, chest-bands deformations, and injury criteria calculations (Brain CSDM, Abdominal soft tissue organs SED, etc.). Moreover, time history results can be extracted from the Occupant Injury Criteria tool. Injury criteria like HIC, BrIC, Nij, etc. are calculated. The extracted and calculated results can be compared to corresponding results of Anthropomorphic Test Devices (ATDs), while it is also easy to make comparisons between multiple HBMs simulation runs or between results from different solvers.

Material and Constitutive Modeling I

In comparison to Metals, several aspects of Polymers such as Viscoelasticity, Early Necking, Wide range of ductility, poses several challenges for manual calibration that can be accepted for us in full-vehicle crashworthiness. This paper provides recent advancements in d3VIEW to develop an accurate *MAT_024 and *MAT_SAMP models. The automation is powered by d3VIEW's Workflow and powered by its new product, FORA, a generative-AI multi-agent that is tightly integrated with d3VIEW.

SDM

From an industrial or productive standpoint, the scale of simulation   models, the number of involved simulation model components, and the complexity of the utilized processes with a vast amount of data are at a level that is challenging to manage manually. The introduction of virtual testing adds to the complexity of the development process and the quantity of data to be handled. Consequently, the use of an SDM system for this purpose can be advantageous in numerous ways.  The introduction of virtual testing can be accomplished in several steps. The initial step is the automation of data preparation, encompassing both input data and produced result data for both the OEM and the testing authority. Subsequent steps involve the implementation of individual processes and security mechanisms against data manipulation. This paper/presentation primarily addresses the initial step and outlines a methodology for achieving the objective of safeguarding against data manipulation and intellectual property (IP) infringem

Aerospace Structure Impact and Dynamics I 

Due to the anticipated growth in the Advanced Air Mobility (AAM) market, there has been a surge of interest in novel advanced material systems like thermoplastics. This new-age aerospace industry could benefit from novel manufacturing and joining processes to meet the expected high-production volume requirements compared to traditional aircraft. As these materials are projected to be used in primary and secondary structural applications, there is a need to understand their resistance to dynamic impact conditions. The present research evaluated flat thermoplastic composite laminates for bird strike resistance through a series of experimental and numerical investigations. The test article was a flat composite panel fabricated with unidirectional carbon fiber and a thermoplastic resin system, and the impactor was a bird. Before conducting the bird strike experiments, pre-test numerical analysis was conducted to determine the laminate thickness that would achieve two desired outcomes: bird penetration through the laminate and bird rebound. The composite panel was modeled using LS-Dyna *MAT Laminated Composite Fabric, commonly referred to as MAT058. Coupon and sub-component level tests were conducted to develop the material model. Pre-test numerical analysis was used to predict the laminate’s response and determine the impact velocities. Bird strike experiments were then conducted and numerically validated using post-test simulations.

Human Body Models

Current trends and developments in the automotive market have caused changes in the way we evaluate and analyze our vehicles. Reacting to a growing demand for realism in simulations, Hans was born. Hans is the DYNAmore's human model, characterized by its detail and excellent performance. Realistic Human Body Models (HBMs) are one of the major revolutions in the world of vehicle development simulation nowadays. But the greater realism of the models comes together with a greater complexity in their handling. GNS offers its well-known products in response to this new need: Generator4 and Animator4 . Thanks to Generator4, Hans and other HBMs can sit comfortably through a simple GUI that is as flexible as the model itself. An industry that requires high realism in passenger models also expects a high level of accuracy in capturing the effects of changing model position and interaction with its environment, reflecting seat deformations and realistic positioning of anchoring systems.

Material and Constitutive Modeling I

The diverse characteristics of Polymer materials, in comparison with Metals, presents significant challenges to develop accurate material laws for use in Crashworthiness simulations efficiently.

With the advent of decision-making Workflows combined with the power of AI, this paper provides benefits, efficiency and accuracy of HPC and AI powered Workflow-based calibration over traditional methods using several Industrial materials used frequently large deformation and high-velocity applications.  The paper will cover sequential and parallel calibration of brittle and ductile Polymers, using *MAT_024 or *MAT_SAMP_LIGHT, with DIC for Quasi-static hardening curve, strain-rate based failure with damage using *MAT_ADD_GISSMO, and Mesh-regularization with the use of AI. The paper will demonstrate the immediate availability of the AI-powered Workflows as Software-as-as-Service (SaaS) in the Cloud that can consume Test-data in Excel format and generate calibrated material cards that can be applied in real-world applications.

SDM

In recent years, crash and safety simulations have reached a very high level of accuracy in the prediction of the crashworthiness of the vehicle and the probability of injury for occupants and pedestrians under a multitude of loading scenarios. Among several factors, this achievement is also attributed to the fine resolution of finite element models that enables the precise representation of even the smallest parts and geometric features affecting the simulation results and the increase in the number of simulated loadcases. However, accuracy does come with a cost: Model size and variability have considerably increased, together with the number of loading scenarios that need to be simulated on each model variant.  From the definition of the different model variants from the CAD structure and the subdivision of the simulation models into functional sub-assemblies, to the set-up of the numerous different types and flavors of loadcases, there's one thing in common: The modular management of the model at each phase of its lifecycle. At early phases, modules are as small as CAD parts. Later, modules become functional sub-assemblies which, for crash and safety loadcases, are handled as include files. Although the modular way of work is the only reliable method to enable parallel work on different areas of the model, it increases the“administration cost” during the simulation preparation, as the pool of different modules (be they parts or include files) needs to be consumed by higher level structures in a way that facilitates data reuse and enables traceability throughout the complete digital thread of a simulation model.  BETA CAE Systems' Suite of applications addresses these challenges during model build and loadcase set-up with its Modular Model and Run Management solutions, that facilitate the handling of the different model and loadcase variants in a way that maximizes data reuse and enables traceability from part to simulation run, while mitigating the “administration cost” with the extensive use of templates. From the population of CAE subsystems based on the CAD structure, to the definition of different subsystem variants, vehicle configurations and loadcases, templates act as recipes that hold the instructions on which ingredients to use and how, in order to complete each given task.  This work discusses the definition and use of templates during model build and loadcase set-up, focusing on three key phases of the simulation preparation: First, the CAD to CAE structure mapping during Subsystem definition from PDM/PLM structures. Second, the handling of Subsystem variants in the scope of the different vehicle configurations. Third, the handling of parametric include files for the definition of Loadcases. Insights are given on the methods used for the adaptation of the templates to the specifications of each model in hand and how these are finally interpreted into LS-DYNA keywords behind the scenes, by making use of parameters, transformations and ID management techniques.

SDM

Due to the continuously increasing demand in Computer Aided Engineering (CAE), it is essential for high efficiency and transparency to automate and standardize processes. In many cases, Simulation Data Management (SDM) software is used for this purpose.   To achieve all mechanical target values of a product, there are several standard disciplines in the field of CAE, such as crash, Noise Vibration Harshness (NVH) or fatigue. Assembly, solving and postprocessing for these disciplines can differ greatly from one another. For this reason, it is best practice in many companies to carry out the optimization of a model in each discipline separately and to compare the results and structural adjustments with other disciplines at regular intervals. This approach can lead to two disadvantages:   Firstly, this results in redundant work. Model adjustments successful for one discipline must be redone in other disciplines later on. Secondly, deterioration in other disciplines may be discovered late: Model modifications that produce positive results for one discipline can have a negative impact on the results in other disciplines. This can lead to short-term changes of plans, postponements, or additional costs for optimizations.   With the help of a base model and SDM, several disciplines can be covered based on a single source of truth. The basic approach is not to work directly on the solver specific files, but on the base model itself. All discipline and solver specific files are generated from this file via SDM automatically. As an example, the two disciplines crash and NVH are considered in this paper. LS-Dyna is used as solver for both disciplines.   There are several criteria that must be met to successfully carry out multidisciplinary variant creation and result evaluation:   A complete database for all discipline-specific information, such as load case definitions or connection configuration is essential. Furthermore, a flexible and simple selection of the load cases is needed. It is also crucial to have an automatic process flow from the creation of the include to the finished report. This report must be designed according to the discipline-specific load case. A clear overview of all created variants is as important as a dynamic variant comparison of the results for each discipline.   The SDM software SCALE.sdm fulfills all these points, which is why it is used as the basis for this paper. So far SCALE.sdm is used by users in the industry like in the best practice example mentioned above: For each discipline there is a separate variant tree that is considered independently. By customizing specific key features such as an automatic creation of the include- and inter-include-connections, it is now possible to display all disciplines together with a single variant tree.   This approach makes it possible to avoid the above-mentioned disadvantages of the separate discipline approach: All the desired disciplines can now be covered by a single model modification, allowing the user to work effectively on structural optimizations while minimizing resources.

Aerospace Structure Impact and Dynamics I 

This paper discusses the challenges of simulating a rotating helicopter main rotor blade constructed from heterogeneous materials. A two-step LS-DYNA process is used to pre-load the helicopter blade: first, a centrifugal force is applied with the blade at rest, followed by initializing the stresses for a transient explicit rotation simulation. The blade, modeled with a combination of shell and solid elements, encounters instabilities such as element erosion and checkered stress patterns when the adhesive is modeled with *CONTACT_AUTOMATIC_SURFACE_TO_SURFACE_TIEBREAK. Several strategies are proposed to resolve these instabilities, including replacing TIEBREAK contacts, adjusting contact settings, and employing implicit transient preload simulations. After the implementation of the strategies, the model's stability was verified over a full revolution simulation.

Human Body Models

Hans - Human Body Model: Enhansments  Over the last months, our Hans - Human Body Model has successfully been introduced to a few selected pilot customers. These customers tested the model in their environments, evaluated application load cases, and kindly shared their feedback with us. Coming this fall, an update of our Hans – Human Body Model will be released based on this valuable customer feedback. The model has further improved in several aspects like the overall response, geometrical level of detail, robustness, etc.   In terms of usability, more output definitions are now available as well as the occupant and pedestrian postures are fine-tuned to better fulfill the requirements of certification load cases. Regarding the model performance, several load cases have been added to the correlation repository of Hans.  To evaluate potential injuries, a stand-alone tool will be released with the upcoming update of Hans. This tool encompasses the calculation of injury risks based on the output of a Hans analysis.

Material and Constitutive Modelling I

This contribution will deal with the latest developments in the field of material models in LS-DYNA. This includes completely new methods as well as extensions and improvements to existing models. The range of materials concerned includes metals, foams, composites, plastics, honeycombs, glass, adhesives, damage and failure models, and others. The new developments are aimed at improving prediction quality, robustness, performance, and user-friendliness. Examples will be used to illustrate the new features.

Battery/Electric Vehicle

The rapid advancement of battery technology has driven the demand for advanced tools for a comprehensive understanding of battery cell behavior. This contribution proposes a holistic approach to generate digital twins of battery cells via coupled electro-thermo-mechanical modeling techniques within LS-DYNA. These models serve as precise representations of real-world battery cells, contributing to the development of efficient and secure battery systems.

Machine Learning

Topology optimization plays a crucial role in generating initial design concepts during the early stages of vehicle development. It is a Finite Element Analysis (FEA) based technique that helps to optimize the shape and distribution of the material in a desired packaging space. This paper explores the application of LS-TaSC and LS-DYNA for topology optimization of giga casting in a vehicle’s underbody. 

This paper touches on the importance of topology optimization in the design development process and elaborates how the LS-TaSC tool can be used to get directional guidance before initiating detailed 

design (CAD) work. BIW (Body-in-White) global static bending and torsional stiffness load cases were considered while setting up the optimization model. Various optimization setting parameters, constraints, and post-processing tools available in LS-TaSC were explored and have been elaborated in the paper. 

The use of LS-TaSC and LS-DYNA in this project enabled the generation of an initial giga casting design concept, indicating the critical areas where material is needed or can be removed. These designconcepts were further refined by the design team using CAD tools, considering more realistic manufacturing and performance constraints.

Multiphysics Modeling

The sloshing phenomena is defined as the movement of a free fluid in a space or tank. Sloshing of fuel in a tank of an aircraft can cause dynamic disturbances that can affect the functionality of the operational system. This work examines two industrial real-life sloshing problems. The first example is the sloshing of fuel in a satellite tank. This can arise from the operational maneuvers of the satellite, which in turn can cause significant inertial forces that may lead to the decrease in performance of the satellite even up to the point of control loss. Another example concerns the slag liquids that may accumulate during the combustion process and acceleration phase of the rocket. These liquids can undergo sloshing which may narrow the nozzle opening and affect the motor performance and mission success. This work presents the attempt to model these sloshing phenomena using the most appropriate LS-DYNA solver. An overview of the available FSI solvers are presented and test cases are done in order to define the most appropriate way to model these real industrial sloshing problems.

Simulation Methods II

In recent years the fidelity of finite element models in structural mechanics has reached a remarkable quality and level of predictiveness. Hence, the question arises, if these models could even be more than the digital twin during product development: Could they be also used for homologation and certification of the corresponding products through the respective authorities or regulators as well? This idea is often referred to Certification by Analysis (CbA) or Virtual Testing (VT). It should be noted that in different industries various steps towards these goals are already taken.   The respective virtual testing procedures (including certification and homologation by regulators) require additional attention: Method development for solver enhancements needs to ensure data as well as IP protection of all stakeholders and provide measures to prevent data manipulation possibilities before homologation and market launch of the product. There are various technical as well as legal aspects to the whole process. But it needs to be emphasized that one key ingredient will be the ability of the solver to protect and safeguard input and output data while at the same time allow for traceability and transparency through fingerprinting technology when it comes to data sources like i.e. material properties, geometrical data, solver settings like version, solution method and contact parameters. Clearly, these requirements can only be met by new solver enhancements.   The present talk will target the big picture of CbA, show the route to achieve some of the most pressing issues and showcase a proposal for a first step into new solve features. This approach will be exemplified by the new Euro NCAP Virtual Far Side Simulation & Assessment regulation for occupant safety.

Battery/Electric Vehicle

With the increasing popularity of electric automotive vehicles, electric vertical take-off and landing (eVTOL) aircraft, and commercial drones, there is a subsequent increase in the need to better understand and predict the mechanical behavior of batteries during crash and impact events. A plasticity model of the cylindrical metal casing of commercially available batteries is developed through mechanical testing. The plasticity model is then validated by simulating the mechanical tests with LS-DYNA, which is then used to simulate crush tests on full batteries to determine the mechanical response of the inner battery components. Future work includes using LS-DYNA to accurately predict the coupled mechanical, electrical, and thermal response of batteries to prevent unwanted battery ignition during vehicle and aircraft crashes.

Machine Learning

The process of developing a new vehicle in the automobile industry typically involves a comprehensive timeline of 3 to 4 years. This duration is necessary to conduct a wide array of tests and adhere to various regulations across different domains, particularly focusing on aspects such as Crashworthiness and Safety standards. In an alternative ‘mid-cycle refresh’ design and development process, the industry also considers introducing a new vehicle by making ‘delta’ mechanical/electrical and technological changes to an existing vehicle design. Either of the vehicle development processes require extensive virtual validation through CAE simulations of Crashworthiness load cases.

The purpose of the Crashworthiness analysis is to assess how well a vehicle's structure can protect its occupants during a collision. This study involves transforming the vehicle Crash Model Partially into a Lumped Parameter representation using DEP MeshWorks’ - Reduced Order Modelling (ROM) technology. The ROM model shows an impressive 85-95% correlation with the original Detailed Finite Element model. The complex process of converting the Detailed Finite element model into lumped parameter representation is automated through the ROM approach. The ROM model is further parametrized with a broad ranging category of parameters involving a) shape, b) gage, c) material, d) spot welds, e) adhesives, f) seam welds, g) crush-initiators, h) reinforcements, i) darts, j) bulk-heads, k) slots/holes, l) laser welded blanking, m) ribbing and o) composite lay-up to convert it to an intelligent ‘Parametric ROM Dyna model’ using DEP MeshWorks.  This parametric ROM model is integrated with a DOE (Design of Experiments) based Optimization scheme to obtain an optimized design that maximizes Crashworthiness performance and minimizes weight & hence cost. Thanks to the significantly reduced number of nodes/elements in the parametric ROM model, the entire optimization process can be completed in less than 50% of the time that detailed models would require. 

Mass & Intertia representation, condensed stiffness & damping and other related techniques form the foundation of the ROM technology available in MeshWorks. The combining of two significant technologies – ROM & Parametric CAE provides users the advantages of optimization executed in a drastically reduced timeframe.

Multiphysics Modelling 

Parachutes for aerospace application is a new research area in the current era of space science. The scope of our project includes parachute design and inflation techniques. The current research project focuses on the following application areas: ● Parachutes for Re-entry Capsule ● RLV Parachutes Parachutes are used as aerodynamic decelerators in airdrop systems, so inflation is a significant fluid-structure interaction (FSI) phenomenon. New patterns of parachutes are constantly being developed and tested for airdrop systems but this research into parachute inflation is heavily reliant on historical experimental data. Till now, no parachute inflation model that is not based on this experimental data was developed. Material and instrumentation have changed significantly since the early experimental testing, yet the methods to develop the parachutes can still be traced to the same techniques used over ninety years ago. Rapid development of computational technology and modern computational mechanics combined with numerical simulation techniques have become more widespread in parachute research field and would enable us to develop the parachutes that are more optimized. Simulating the landing of a vehicle on water using LS-DYNA is a complex task that involves the interaction between the fluid (water) and the structure of the vehicle. This type of simulation is crucial for vehicles designed to land on water. The process typically involves several steps and requires specialized techniques within LS-DYNA.

Simulation Methods II

FMVSS301 mandates fuel system integrity for vehicles post-crash, so a detailed assessment of the fuel system is needed to validate its integrity. Modeling the fuel tank assembly for vehicle crashworthiness is very challenging due to the fuel slosh phenomenon which occurs in the dynamic crash event. The fuel slosh behavior affects the overall dynamics of the fuel tank and its interaction with surrounding vehicle structures. Extensive studies can be found in the literature to improve fuel tank modeling. However, there is limited information related to modeling fluid’s free surface and the pressure profile imparted by the fluid on the tank during the crash event. A fully coupled Fluid Structure Interaction (FSI) simulation of this event, using an explicit mechanical solver and an incompressible fluid solver (ICFD), is computationally expensive. Smooth Particle Hydrodynamics (SPH) has long been the choice for this application. This study uses the weakly-incompressible SPH formulation (ELFORM 15) to model the fluid behavior. In Phase-I of this two phase detailed calibration effort, the model is calibrated against a series of rigid tank slosh tests at two different speeds and various volume fills with and without baffles. The study shows good correlation of both the pressure time histories and the slosh pattern (fluid free surface) between the experiment and simulation.

Battery/Electric Vehicle

The safety of battery systems in electric vehicles (EVs) and portable electronics under impact or penetration is critical. Traditional modeling methods, relying on simplified approaches, fall short in capturing complex interactions and dynamic behaviors. Advanced simulation techniques using LS-DYNA offer more accurate predictions of battery responses to penetration loads. This abstract outlines recent methodologies employing LS-DYNA for high-fidelity modeling of battery components and protective measures. Innovations include sophisticated material models for electrodes, electrolytes, separators, and structural packaging. These advancements align with standards like SAE J264, ensuring battery safety and reliability, thereby supporting the adoption of electric mobility and renewable energy technologies.

Machine Learning

N. Abdelhady1, D. Borsotto1, V. Krishnappa1, K. Schreiner1, C.-A. Thole1, T. Weinert1 1SIDACT GmbH Reaching and fulfilling several design and crash criteria during the development process is what makes the engineer adapt and redesign the simulation model over and over again. Ideally resulting in new simulation runs with in best case improved performance, matching the intention of the applied changes. For the more demanding case of unforeseen results which do not necessarily fit to the expectations of the actual changes, machine learning methods and a workflow are being introduced here, which allow to identify the root cause of this behavior.  In a first instance every new simulation run is being added into an analysis database, which is continuously being used to compare new simulations against. Previous studies have already shown that this process can assist the engineer in automatically highlighting new behavior and pin pointing the engineer to the regions of interest. Rather than only highlighting the new behavior now a second phase is being triggered additionally.  In this second phase the previously detected event is being isolated and analyzed against the gathered data of the development history. The analysis methods used are based up on the Principal Component Analysis, a reduced order modelling technique. This allows not only identifying structures in the data but also correlating deformation patterns against each other. Especially the latter one is of interest for an automated process, as it allows automatically detecting and suggesting possible root causes to the engineer. As an outcome of this process the engineer receives a list of correlating parts, so that he can focus on deriving a better engineering solution to achieve a deterministic behavior, rather than searching for the root cause of the event.  To provide additional information about the type of cause, as for example failure or buckling, the identified parts are also forwarded to a classification prototype. This type of classification shall assist the engineer in deriving a possible design adaptation.

Multiphysics Modeling

This paper focuses on implementing immersed interface techniques within the Incompressible CFD (ICFD) solver in LS-DYNA. For Fluid-Structure Interaction (FSI) simulations, immersed methods offer several advantages: they simplify the pre-processing stage, prevent large mesh distortions due to structural motion, and provide more robustness in scenarios involving complex contacts. However, these methods sacrifice accuracy in near-wall regions, suggesting that a hybrid approach between body-fitted and immersed methods could be beneficial in some cases. This work discusses two methods: the Resistive Immersed Implicit Surfaces (RIIS) method [1] and a method based on a new set of discontinuous finite element functions [2]. The basics of these methods will be presented, along with considerations for setting up a model in LS-DYNA, including pre- and post-processing.  [1] Fernandez MA, Gerbeau JF, Martin V (2008) Numerical simulation of blood flows through a porous interface. ESAIM: Mathematical Modelling and Numerical Analysis 42(06): 961-990. [2] R. Zorrilla, R. Rossi, R. Wüchner, E. Oñate, An embedded Finite Element framework for the resolution of strongly coupled Fluid-Structure Interaction problems. Application to volumetric and membrane-like structures, Computer Methods in Applied Mechanics and Engineering, Volume 368, 2020, 113179.

Simulation Methods II

The "Modular Contact" is a new implementation of contact algorithms in LS-DYNA. We will give a short overview of the fundamental ideas behind it and how it tries to achieve its main goals: a more uniform implementation between different contact options and improved (parallel) performance by better harnessing of modern hardware and improved MPI communication. The performance will be demonstrated on large models and put in comparison to the well-established, existing algorithms in LS-DYNA.

Battery/Electric Vehicle

As EVs become more mainstream safety concerns have been paramount for OEMs, consumers and regulators. Owing to the risk of thermal runaway in an EV crash incident, a full Multiphysics battery analysis is required to capture the mechanical deformation that triggers internal shorting and thermal runaway. This is helpful in knowing the mechanical design limits and designing vehicles with good crashworthiness while balancing the need to lightweight EVs as car companies strive for better range.

Ansys developed a simulation workflow from battery cell to vehicle level (in partnership with universities and industry test labs) to validate key pieces using physical test data from a 26Ah automotive pouch cell. LS-DYNA models were calibrated and subsequently validated against cell experimental data. Multiphysics models that captured a battery cell’s electrical, mechanical and thermal behaviors were then to used to scale up in a proof-of-concept simulation of full vehicle crash.

Ansys has partnered with George Mason University’s Center for Collison Safety (CCSA) to incorporate battery models into a full vehicle validated crash model. Ansys has tested cells (at our internal labs or through test partners) that CCSA extracted from a commercial EV to build battery models. The ongoing work to date will be discussed and used as an example to demonstrate the battery safety workflow in an EV.

Machine Learning

LS-OPT includes an improved version of the MOP (Metamodel of Optimal Prognosis) system developed for optiSLang. The feature allows automatic selection of the ‘best’ metamodel. The updated version also includes the standard LS-OPT metamodels (Neural and Radial Basis Function Networks) as candidates for the MOP selection process. Examples are presented.

A previously introduced methodology for integrating LS-OPT, LS-DYNA and Ansys Twin Builder for the purpose of creating fast full-field surrogate models, is presented with results using industry examples. LS-OPT is used to generate and schedule multiple LS-DYNA simulations according to an experimental design. The dynamic field results are then gathered as input to create a Reduced Order Model (ROM) using Ansys Twin Builder. Recent examples using single history ROM surrogates are also presented.

Finally, an outlook on current developments is provided.