Rescale > Case Studies > Eco-Runner Team Delft Leverages Rescale’s Big Compute Platform for Fuel-Efficient Vehicle Design

Eco-Runner Team Delft Leverages Rescale’s Big Compute Platform for Fuel-Efficient Vehicle Design

Technology Category

Analytics & Modeling - Digital Twin / Simulation
Robots - Autonomous Guided Vehicles (AGV)

Applicable Industries

Automotive
Education

Applicable Functions

Product Research & Development

Use Cases

Digital Twin
Virtual Reality

About The Customer

Eco-Runner Team Delft is a student engineering team from TU Delft, a renowned technical university in the Netherlands. Since 2005, the team has been dedicated to designing and building the most efficient hydrogen-powered vehicle possible. They participate in the Shell Eco-marathon competition annually, a competition that challenges student teams around the world to design, build, and drive the most energy-efficient car. The team's mission is not only to win the competition but also to contribute to the development of sustainable mobility solutions. They are committed to pushing the boundaries of what's possible in vehicle efficiency, using innovative design and cutting-edge technology.

The Challenge

Eco-Runner Team Delft, a student engineering team at TU Delft, has been designing and building the most efficient hydrogen-powered vehicles since 2005. They participate in the Shell Eco-marathon competition annually, where the goal is to complete a 16-kilometer course using the least amount of fuel. The challenge lies in designing a vehicle with a fuel-efficient propulsion system and a body with minimal resistance or weight. The weight of the vehicle is particularly crucial to fuel efficiency, especially in the 2017 Eco-marathon course that featured a hill. The team's design efforts were focused on making every component of the car as light as possible to reduce the energy requirement to climb the hill. However, they faced the challenge of exploring and simulating many weight-saving design options while maintaining structural integrity, all within a constrained project schedule and a student budget.

The Solution

The team used Rescale’s ScaleX platform to explore and simulate many weight-saving design options while maintaining structural integrity. Rescale’s cloud-enabled simulation allowed them to quickly design a lightweight, but strong vehicle. They used ANSYS Mechanical for their FEA to reduce the weight of the car’s carbon fiber body. Using previous models of the Ecorunner as reference points, they ran FEA on various parts of the car to determine the lay-up plan of the carbon fiber and to determine whether each component would withstand the loads applied. Rescale’s cloud big compute platform provided turnkey access to compute resources, enabling them to get simulation results almost immediately. They ran the majority of their simulations on 192 cores of the Gold hardware configuration, which was the most cost-effective configuration for their simulations. The ability to scale out on unlimited cores on demand via the Rescale platform reduced their typical simulation turnaround time from a couple of days to a matter of hours.

Operational Impact

The fast simulation turnaround enabled by Rescale’s platform kept the team on schedule to compete on event day. It also allowed the team to quickly explore a variety of new concepts and design features before iterating to a final design. This comprehensive design exploration phase led to the discovery and implementation of several design features that significantly reduced the total weight of the carbon-fiber body. As a result, the energy-efficiency of their hydrogen-powered car was significantly increased. The team was able to achieve their goal of designing a lightweight, but strong vehicle within their constrained project schedule and budget.

Quantitative Benefit

Reduced the total weight of the carbon-fiber body by 5%
Reduced typical simulation turnaround time from a couple of days to a matter of hours
Ran the majority of simulations on 192 cores of the Gold hardware configuration, the most cost-effective configuration for their simulations