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ABSTRACT

Hierarchical design scenarios arise when the performance of large-scale, complex systems can be affected through the optimal design of several smaller functional units or subsystems. Monte Carlo simulation provides a useful technique to evaluate probabilistic uncertainty in customer-specified requirements, design variables, and environmental conditions while concurrently seeking to resolve conflicts among competing subsystems. This paper presents a framework for multidisciplinary simulation-based design optimization, and the framework is applied to the design of a Formula 1 racecar. The results indicate that the proposed hierarchical approach successfully identifies designs that are robust to the observed uncertainty.

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