Traditionally, the automotive industry has relied on physical crash tests with real prototypes to evaluate and improve vehicle safety. These tests are destructive, and though they are invaluable, they are also costly, resource-intensive, and very limited in scope. Vehicle crashes and safety analysis have recently moved swiftly into the digital realm. This is a profound shift that is being powered by artificial intelligence (AI), simulation, and high-performance computing (HPC).
This transformation is not only technological but also driven by evolving consumer expectations, regulatory landscapes, and the emergence of new vehicle architectures that present totally new crash scenarios.
The Changing Landscape of Crash Safety
From prototypes to pixels
Though traditional crash testing has been the gold standard for validating vehicle safety, it also requires expensive prototypes. Also, they can only explore a limited number of crash configurations with this type of testing. Physics-based simulation and AI-augmented modelling platforms like Ansys SimAI allow engineers to test thousands of crash scenarios – all virtually, with a significant reduction in time. It reduces the time from months to hours while preserving accuracy.
Growth drivers reshaping safety testing
Several forces are driving the adoption of virtual crash analysis, including:
- Consumer demand for safety ratings: Modern consumer expectations have increased. They associate 5-star crash ratings with trust and brand credibility, making safety a powerful differentiator for OEMs.
- Stringent global safety regulations: NCAP programs worldwide, including Bharat NCAP, Euro NCAP, IIHS, FMVSS, and UNECE, mandate increasingly rigorous tests. Keeping time and cost in mind, virtual is the answer.
- Emergence of new vehicle architectures: Electric, hybrid, and autonomous vehicles today present new risks, such as battery fire hazards, lightweight structural materials, and altered crash energy pathways.
Re-engineering Engineering: Technology Trends Transforming Crash Analysis
Advancements in technology have enabled an era of “re-engineering engineering”, which is redesigning how engineers can predict, analyze, and prevent vehicle crashes:
- AI and Machine Learning: Today’s tools can go beyond traditional solvers by learning from existing simulations to foretell complete transient crash outcomes. New designs can be processed without parameterizing geometry. These results are 10 to 100x faster than traditional finite element solvers while maintaining accuracy. Algorithms can detect patterns in injury risks, optimize crash simulations, and accelerate design iterations. For example, Ansys SimAI can predict hood deformation and Head Injury Criterion (HIC) values for pedestrian impacts with less than 10% relative error compared to solver results, at speeds many orders of magnitude faster.
- Virtual Crash Testing & Digital Twins: Scalable, repeatable, and rapid crash analysis is now possible as vehicles can be entirely recreated digitally.
- Advanced Materials Modeling: Precise modeling of new materials, from composites to ultra-high-strength steels, can help forecast structural behavior under crash forces.
- Human Body Models (HBMs): These advanced virtual surrogates, including widely used models such as THUMS, THOR, and HANS, provide far more profound insights into injury risks across varied occupant profiles. When combined with AI tools, HBMs can deliver near real-time injury risk prediction across multiple crash scenarios.
- End-to-End Workflow Automation: With AI-augmented simulation, repetitive tasks like model setup, meshing, and report generation can now be automated. This significantly reduces manual effort, accelerates crash analysis, and enhances engineer productivity by allowing them to focus on higher-value design and safety insights. SimAI, for example, can learn directly from raw CAD geometries and boundary conditions, eliminating months of manual preprocessing.
- High-Performance Computing: Cloud and GPU-accelerated platforms drastically cut the simulation runtimes. This means that engineers can test more scenarios in less time. Full vehicle models often surpass 15 to 30 million elements, which takes 25 to 30 hours on 100 to 300 CPUs to solve.
Crash analysis today extends across a wide range of safety-critical applications, including frontal, side, and rear impact testing, rollover and roof crush resistance, occupant safety and injury prediction, and battery protection and EV safety.
Industry Challenges with Emerging Technologies
Quickly shifting toward new vehicle technologies like EVs brings distinctive design and safety challenges. These complexities call for advanced approaches. Simulation-driven safety analysis is proving to be a key solution for addressing them. They include:
- Cost aspects: Highly detailed models improve correlation with real-world results but are computationally more expensive. Automakers must balance investment in advanced safety tools with very competitive pricing pressures.
- Speed vs. Accuracy: Traditional crash solvers can take weeks due to their extensive preprocessing (mesh generation, CAD cleaning, material definitions). There is a growing need to bypass this meshing and work directly on CAD STL files to deliver meaningful predictions within a few hours rather than months.
- Model Complexity: EV architectures add substantial complexity in interactions between structures, motors, batteries, and inverters—all of which must be captured accurately.
- Validation against reality: Simulations must reproduce complex real-life crash scenarios and align closely with physical crash outcomes to meet regulatory and consumer trust standards.
- EV-specific safety considerations include:
- Casing design that prevents deformation at the cell level and mitigates thermal runaway.
- Battery pack integrity under side and frontal impacts.
- Prediction of cell deformation and heat propagation.
- Placement and reliability of fire sensors.
- Crash safety of EV-specific components such as motors and inverters.
- Avoiding overdesign of battery packs by basing requirements on real-world load cases and their impact on casings, internal components, and cells.
AI-driven reduced-order models like Ansys SimAI cut runtimes dramatically, with predictions of displacement fields taking approximately 10 seconds, and scalar HIC values less than a second, versus hours on traditional solvers.
Compliance and Standards
Global regulators set the pace for automotive safety testing, including NCAP Programs like Bharat NCAP, Euro NCAP, and US NCAP. These programs continue to evolve with stricter crashworthiness and occupant protection benchmarks. International frameworks, like the UNECE and FMVSS, enforce cross-border safety compliance.
For OEMs, this means ensuring country-specific safety compliance while managing variants of the same model to meet different regional safety requirements. Occupant Safety Systems, like airbags, seatbelts, and child restraint systems, must also undergo stringent virtual and physical validation.
Virtual validation powered by AI enables OEMs to remain agile and adapt quickly to region-specific safety standards without waiting for physical prototypes.
Simulation allows automakers to advance crash and safety testing via:
- Full vehicle crashworthiness with the comprehensive modeling of frontal, side, rear, and rollover impacts.
- Human body and dummy models evaluate head injury criteria (HIC) for safety assessments, allowing realistic injury prediction across demographics.
- Materials testing & characterization with the accurate modeling of new materials, composites, and adhesives.
- Design optimization, robustness & workflow automation with faster exploration of design variations across safety metrics.
- Digital engineering, such as cloud-enabled HPC, GPU acceleration, and AI-driven prediction workflows, allows for scalability and speed.
- Predictive platform and physics-agnostic AI that bridges theory and reality, capturing the fully transient nature of crashes. They can also predict outcomes across entire crash sequences, not just peak values.
- Advanced explicit crash dynamics :
- Improved robustness of contact algorithms along with advanced material failure models and detailed connections modeling to better capture interactions, failure progression, and large deformations across a wide array of materials.
- Comprehensive multiphysics capabilities including implicit/explicit solvers, meshless SPH/ALE methods, adaptive remeshing, and multiphysics coupling (such as electromagnetics and fluids) – to simulate complex crash events as well as EV-specific scenarios like tank sloshing and thermal runaway in batteries.
Benefits of Simulation
Simulation does not entirely replace physical crash testing but complements and accelerates it. Its advantages include:
- Early design validation, as engineers can test the crashworthiness of a vehicle way before a physical prototype is created.
- Cost & Time Efficiency as the reduced need for physical prototypes can cut millions in development costs and months off product cycles.
- Scenario exploration, as thousands of “what-if” scenarios can be run virtually. This covers edge cases that are impossible to replicate physically.
- Physics-driven design evaluation that provides insights into occupant kinematics and crash protection strategies, supporting safer designs.
- Airbag deployment accuracy that enhances passenger safety by optimizing airbag timing, pressure, and coverage through virtual testing.
- Pedestrian safety compliance that helps automakers design safer vehicle fronts by modeling pedestrian impact forces and injuries.
- Regulatory & consumer readiness since automakers can prepare for upcoming standards while maintaining the highly competitive time-to-market.
- AI-augmented predictive speed can help demonstrate full pedestrian head impact simulations, including hood deformation and HIC value prediction within a 10% error margin. The AI Model is trained on existing simulation data and delivers a hundred times faster results.
- SimAI’s SaaS cloud delivery with SDK integration allows even non-FEM experts to run high-fidelity evaluations rapidly, bridging the skills gap in early design phases.
Looking Ahead
Crash safety testing must evolve as vehicles become lighter, smarter, and even more electrified. AI-powered simulation will continue to expand predictive accuracy, while HPC and digital twins will allow automakers to simulate real-world accidents in a detail that is previously unimaginable.
The ultimate goal is a future where vehicles are designed to meet regulations and exceed consumer expectations for safety. This has to be achieved in a shorter design cycle that remains cost-effective.
