Blue Sky Solar Racing places first among Canadian teams with Dassault Systèmes PLM solutions as well as ANSYS CFX and MATLAB.
by Jeff Griffith
Every two years, a special field of cars competes in the Panasonic World Solar Challenge, crossing the Australian continent powered by nothing but the sun. Teams research, build, and design vehicles capable of completing the 3,000-kilometer journey from the tropical town of Darwin in the Northern Territory to cosmopolitan Adelaide in South Australia.
The University of Toronto’s Blue Sky Solar Racing team’s fifth-generation car, Cerulean, was the team’s entry for the 2007 challenge. The team, which consists of undergraduate and graduate students and alumni volunteers, leveraged Dassault Systèmes’ product lifecycle management (PLM) solutions, including CATIA and ENOVIA Digital Mock-up (DMU), in its designs.
Training on the Dassault Systèmes software was donated by Aventec Inc., a CATIA solutions provider based in Markham, Ontario. Dassault Systèmes is a sponsor of the Blue Sky Solar Racing team, whose members are committed to demonstrating the viability of renewable energy technology and the practical benefits of a multidisciplinary approach to solving problems.
Evaluating 60 Designs in a Few Hours
The critical challenge in developing a competitive solar vehicle is reducing drag and weight while increasing power, and ultimately optimizing the solar car. The solar panels are arrayed across the top of the body to maximize sunlight collection. They must be oriented directly toward the sun to increase the power they generate, but this often increases drag, compromising efficiency. Many design variables must be considered to improve this trade-off, including the baseline shape, track width, wheelbase, driver positioning, and others to optimize the car’s power-to-drag ratio.
Amy Bilton, chief aerodynamicist for the 2007 car, needed to analyze many different alternatives to improve the efficiency of the design. She estimated that it would have taken about two hours to create each design iteration by manually modifying the CAD geometry. Instead, she developed a CATIA design table that incorporated each of the key design variables. Then she simply typed in the values of each variable for the iterations she wished to explore. CATIA automatically generated the resulting models.
Using C++, Bilton created a routine that read the design parameters from the spreadsheet. It calculated the area available for solar arrays on the top of the vehicle and the resulting power. With MATLAB scripts from The Mathworks, she was able to quickly create many design configurations that were imported into CATIA V5 for model creation.
Bilton also used ANSYS CFX for all the CFD performed, including drag calculations on several distinct CATIA solid model configurations for the car’s main body. After completing each design iteration, she modified the model and moved steadily toward her goal of optimizing the power-to-drag ratio. She generated 60 design iterations in about five hours, approximately a 96 percent time savings compared to conventional methods.
Blue Sky decreased drag by 22 percent overall and increased the power of the solar array by four percent. The team was well on its way toward creating one of the top cars in its class.