Efficient aerodynamic shape optimization through reduced order CFD modeling
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Efficient aerodynamic shape optimization through reduced order CFD modeling Peng Shengfang1 · Zhang Junjie2 · Zhang Chunyu2 Received: 8 August 2019 / Revised: 26 January 2020 / Accepted: 27 January 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Although computational power is increasingly available, high-fidelity simulation based aerodynamic shape optimization is still challenging for industrial applications. To make the simulation based optimization acceptable in the practice of engineering design, a technique combining mesh morphing and reduced order modeling is proposed for efficient aerodynamic optimization based on CFD simulations. The former technique avoids the time-consuming procedure of geometry discretization. And the latter speeds up the procedure of field solution by exploiting pre-computed solution snapshots. To test the efficiency of the proposed method, the windshield of a motorbike is analyzed and optimized. It is shown that even the total number of cells of the mesh is around 0.4 million, the CFD computation and the post-processing of the results can be completed in less than 10 s if the reduced order model is adopted. Running on a personal computer, the generic algorithm is applied to optimize the profile of the windshield. A 8% reduction of the drag coefficient is achieved after 800 queries of the reduced order CFD model and the total CPU time is only around 2 h. Keywords Shape optimization · Mesh morphing · Reduced order model
1 Introduction High-fidelity modeling and simulation has been widely used in nowadays engineering design. In vehicle engineering, for example, detailed computational fluid dynamics (CFD) simulation is routinely applied to evaluate the aerodynamic performance of the vehicles. However, simulation based shape optimization is still a challenging
* Zhang Chunyu [email protected] 1
School of Industrial Design, Guangzhou Academy of Fine Arts, 510260 Guangzhou, China
2
Sino‑French Institute of Nuclear Engineering and Technology, Sun Yat-Sen University, 519082 Guangzhou, China
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task even the computational power is increasingly available because more complex and accurate models quickly use up the computational resources. High-fidelity modeling techniques such as the finite element method (FEM) and the finite volume method (FVM) adopt finely resolved meshes which inevitably form big-sized algebra equations. Preparation of the detailed meshes as well as formation and solution of the algebra equations are the most time consuming steps in a simulation. For a non-trial industrial case, preparation of the mesh may take several or tens of hours even automatic mesh generation techniques are applied. Then formation and solution of the algebra equations may take O(10–102) CPU-hours. As a consequence, the total computational cost of the simulation-based optimization is prohibitively high considering O(102–103) simulations have to be run in the optimization procedure. To make the simulation bas
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