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 combining 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 seconds 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 hours.
Highlights
High-fidelity modeling and simulation has been widely used in nowadays engineering design
We propose to combine the two techniques, i.e., the mesh morphing and the reduced order modeling, to formulate an automatic procedure for efficient aerodynamic shape optimization based on computational fluid dynamics (CFD) simulations
The present study focuses on the aerodynamic performance of vehicles and the full order model is represented by the resolution of the well-known Navier-Stokes (NS) equations, i.e., a system of coupling differential equations which govern the dynamics of fluids
Summary
High-fidelity modeling and simulation has been widely used in nowadays engineering design. Efficient methodologies can be developed to speed up the procedure of field solution (formation and solution of the algebra equations) by combining pre-computed solution snapshots defined on well-chosen geometric configurations, avoiding the heavy computation of the algebra equations and enabling to perform simulations of complex phenomena almost in real time This is the basic idea of reduced order modeling (ROM) [1]. This nonintrusive choice allows us to apply the method to different shape optimization problems, changing only the high fidelity solver or the parametrization technique.
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