With the increasing design dimensionality, it is more difficult to solve multidisciplinary design optimization (MDO) problems. Many MDO decomposition strategies have been developed to reduce the dimensionality. Those strategies consider the design problem as a black-box function. However, practitioners usually have certain knowledge of their problem. In this paper, a method leveraging causal graph and qualitative analysis is developed to reduce the dimensionality of the MDO problem by systematically modeling and incorporating the knowledge about the design problem into optimization. Causal graph is created to show the input–output relationships between variables. A qualitative analysis algorithm using design structure matrix (DSM) is developed to automatically find the variables whose values can be determined without resorting to optimization. According to the impact of variables, an MDO problem is divided into two subproblems, the optimization problem with respect to the most important variables, and the other with variables of lower importance. The novel method is used to solve a power converter design problem and an aircraft concept design problem, and the results show that by incorporating knowledge in form of causal relationship, the optimization efficiency is significantly improved.
Employing Knowledge on Causal Relationship to Assist Multidisciplinary Design Optimization
Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 7, 2018; final manuscript received December 8, 2018; published online January 11, 2019. Assoc. Editor: Christopher Mattson.
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Wu, D., Coatanea, E., and Wang, G. G. (January 11, 2019). "Employing Knowledge on Causal Relationship to Assist Multidisciplinary Design Optimization." ASME. J. Mech. Des. April 2019; 141(4): 041402. https://doi.org/10.1115/1.4042342
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