Artificial intelligence technologies in complex engineering design

Abstract

Engineering design optimization is an emerging technology whose application both tends to shorten design-cycle time and finds new designs that are not only feasible, but also nearer to optimum, based on specified design criteria. Its gain in attention in the field of complex designs is fuelled by advancing computing power now allowing increasingly accurate analysis codes to be deployed. Unfortunately, the optimization of complex engineering design problems remains a difficult task, due to the complexity of the cost surfaces and the human expertise necessary in order to achieve high quality results. This research is concerned with the effective use of past experiences and chronicled data from previous designs to mitigate some of the limitations of present engineering design optimization process. In particular, the present work leverages well established artificial intelligence technologies and extends recent theoretical and empirical advances, particularly in machine learning, adaptive hybrid evolutionary computation, surrogate modelling, radial basis functions and transductive inference, to mitigate the issues of i) choice of optimization methods and ii) dealing with expensive design problems. The resulting approaches are studied using commonly employed benchmark functions. Further demonstrations on realistic aerodynamic aircraft and ship design problems reveal that the proposed techniques not only generate robust design performance, they can also greatly decrease the cost of design space search and arrive at better designs as compared to conventional approaches.

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