Renault runs a crash optimization study on a 20 million elements car model with ESI’s Virtual Performance Solution

Virtual Performance
Ground Transportation
This project was a success essentially due to the close collaboration between Renault and ESI. It brought to light the outputs and benefits linked to large size models for crashes. We
were able to handle the challenges tied to the model’s creation and its use within an intensive context, such as optimization
Marc Pariente, Numerical Simulation Trade Specialist, Renault SAS

Challenge

With increasing vehicle complexity, and the need for faster innovation, virtual prototypes have to evolve to become more predictive, and to sustain faster design iterations and optimization studies. This research project was aimed at driving the evolution of current numerical methods towards the next generation of virtual prototypes. Made of much bigger, refined models (up to 20 million elements), encapsulating more detailed physics, they boast faster turnaround time on high performance, massively parallel computers (up to 1000 cores). It is a unique experience launched after several years of collaboration between Renault and ESI on Virtual Performance Solution (VPS), ESI’s solution dedicated to performance prediction in multiple domains including crash.

Story

Renault’s objective was a frontal crash with a Dacia Lodgy vehicle, already on the market. The objective was double: to determine if it was worthwhile to increase the model’s discretization, and to test optimization methods on large scale models while increasing the number of optimization parameters. Read complete story. VPS received the HPCwire Readers’ & Editors’ Choice Award for Best use of HPC in Automotive.

Benefits

This project has demonstrated the possibility and the value of such refined models for automotive engineering. Renault is now convinced that this range of numerical models will soon become standard. ESI has proven that Virtual Performance Solution is able to handle large scale models and is ready to deliver more accurate results in a practical timeframe for optimization studies.