Abstract
The problem of vehicle development subject to uncertain parameters is of great significance in realistic engineering applications. A probabilistic optimization approach are proposed to deal with the uncertainty and demonstrated in vehicle suspension design application. The uncertainty propagation is realized by Sparse Grid Techniques. As a hierarchical multilevel Multidisciplinary Design Optimization (MDO) method with uncertainty, Probabilistic Analytical Target Cascading (PATC) is enhanced by considering the first two statistical moments of interrelated responses. The proposed methods were demonstrated by a suspension probabilistic optimization problem, and were solved by the proposed PATC and SGNI. Results show that the enhanced PATC has good effectiveness and efficiency.
F2012 -G01-018
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Chen , X., Zhao, Q., Lin, Y., Song, K. (2013). A Probabilistic Optimization Approach to Vehicle Suspension Design Under Uncertainty. In: Proceedings of the FISITA 2012 World Automotive Congress. Lecture Notes in Electrical Engineering, vol 198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33795-6_8
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DOI: https://doi.org/10.1007/978-3-642-33795-6_8
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