Why LS-DYNA Still Leads the Industry for Crash, Impact, and Complex Physics Simulation

As simulation tools evolve and AI becomes a bigger part of engineering workflows, it’s easy to assume that broader, all-in-one platforms will dominate. But, in a new podcast episode with EngTechnica in their Future of Design & Engineering Software series, Jason Pfeiffer (Vice President of Rand SIM) and Turner Jennings (Rand SIM’s LS-DYNA expert), make a compelling case for why focused, physics-specific tools like LS-DYNA, continue to deliver the highest value when accuracy, speed, and risk reduction matter most.

LS-DYNA has long been the gold standard for explicit dynamics, enabling engineers to simulate highly nonlinear events like crash, impact, and material fragmentation with confidence. From modeling complex foam behavior in impact liners to evaluating aerodynamic shell performance, the ability to capture real-world physics at a high level of fidelity is critical, especially in safety-driven industries. This is exactly what we demonstrated in our Olympic helmet case study, where LS-DYNA was used to evaluate impact performance and optimize design trade-offs between safety, weight, and comfort under real-world conditions.

One of the biggest shifts discussed in the episode is the role of GPU-accelerated solvers. What once took hours can now be solved in seconds, fundamentally changing how teams approach design iteration. Engineers can run more design of experiments (DOEs), explore worst-case scenarios across variables like speed and temperature, and make faster, more informed decisions earlier in the process.

The conversation also highlights how collaboration between designers and analysts is evolving. With clearer workflows and better-defined roles, teams can work in parallel without stepping on each other, accelerating development while maintaining simulation quality. This is especially important as organizations look to scale simulation adoption without sacrificing expertise.

Of course, no discussion today is complete without addressing AI. While AI can assist with setup, automation, and even early-stage predictions, it is clear that it’s not a replacement for engineering judgment. Complex physics problems still require experienced analysts to interpret results, validate models, and make the critical decisions that impact product performance and safety.

The takeaway? Simulation success isn’t about using more tools, it’s about using the right tools, combined with the right expertise. Focused simulation approaches, supported by training, services, and continuous skill development, are what ultimately drive measurable business outcomes.

If you’re evaluating how to get more value from your simulation investment, or wondering where AI fits into your workflow, this episode offers a grounded, real-world perspective from engineers who do this every day.

Watch the full podcast episode here: