An improved system for efficient shape optimization of vehicle aerodynamics with “noisy” computations

• Published in: Structural and multidisciplinary optimization Vol. 65; no. 8
• Main Authors: Wang, Qingyu, Nakashima, Takuji, Lai, Chenguang, Du, Xinru, Kanehira, Taiga, Konishi, Yasufumi, Okuizumi, Hiroyuki, Mutsuda, Hidemi
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-08-2022; Springer Nature B.V
• Subjects: Aerodynamics; Algorithms; Computational fluid dynamics; Computational Mathematics and Numerical Analysis; Convergence; Drag coefficients; Engineering; Engineering Design; Global optimization; Mathematical models; Optimization; Research Paper; Shape optimization; Theoretical and Applied Mechanics; Regression kriging with re-interpolation; Efficient global optimization; Vehicle aerodynamics; Computational fluid dynamics; Optimizer; Shape optimization
• ISSN: 1615-147X; 1615-1488
• DOI: 10.1007/s00158-022-03323-9

To efficiently achieve tangible improvements in the aerodynamic objectives of a vehicle based on a computational fluid dynamics (CFD) simulation that produces unavoidable noise, an improved system is proposed. This system, called regression kriging with re-interpolation (RKri)-based efficient global optimization (EGO) with a pseudo expected improvement (PEI) criterion (RKri-EGO-PEI), is used to directly filter out the noise produced by the CFD simulation, maintain a smooth trend of the surrogate model, and conduct point infills in a parallel manner. To guarantee optimization processes for tuning the hyper-parameters of RKri and searching for a solution of appropriate quality to the PEI function, the performance advantages of the optimizers on a parallel EGO algorithm called EGO-PEI are comprehensively investigated. Then, the best is chosen as the optimizer for the RKri-EGO-PEI system. To confirm the performance of the proposed system, RKri-EGO-PEI competes with ordinary kriging-based EGO-PEI (OK-EGO-PEI) and RKri-based EGO (RKri-EGO) systems on a real-world optimization problem of vehicle aerodynamics. The results of the investigation show that the performance of the optimizer with a higher central goal of exploration–exploitation can not only promote a higher-level convergence of the EGO-PEI algorithm within an appropriate number of point infills, but also ensure the same convergence level of the EGO-PEI algorithm as that using other optimizers, with fewer iterations. In addition, RKri-EGO-PEI searches for a lower drag coefficient (Cd) of the vehicle model with a faster speed and smaller wall-clock time cost than those of OK-EGO-PEI and RKri-EGO under an optimization problem with “noisy” computations.