In this paper, we present an approach for the optimization of a racecar using vehicle dynamics simulation in a parallel-computing environment. The use of vehicle dynamics simulations in the automotive and auto racing industries is widespread. Complex vehicle simulations can include hundreds of parameters and be very computationally expensive to perform. This limits the number of design configurations that can be considered within a reasonable time, preventing thorough exploration of the design space. It also limits the usefulness of these simulations during the course a race weekend when time is of the essence. In this paper, we present the initial results from work to overcome this problem. By using distributed computing, the number of vehicle configurations that can be considered as well as the fidelity of the simulation can increase while keeping the execution time reasonable. The results focus on both the computational savings possible and the improved ability of the designer to make effective decisions due to the huge increase in the amount of design information available. To demonstrate the effectiveness of this approach the optimization of two vehicles will be considered, a two-design variable model and three-design variable model. These results are compared to previous results that were obtained executing the code on a single processor. Good speedup and excellent scalability is observed as the number of processors is increased from 2 to 32.

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