Optimization for Airfoil Design

A significant challenge to the application of evolutionary multiobjective optimization (EMO) for transonic airfoil design is the often excessive number of computational fluid dynamic (CFD) simulations required to ensure convergence. In this study, a multiobjective particle swarm optimization (MOPSO) framework is introduced, which incorporates designer preferences to provide further guidance in the search. A reference point is projected onto the Pareto landscape by the designer to guide the swarm towards solutions of interest. The framework is applied to a typical transonic airfoil design scenario for robust aerodynamic performance. Timeadaptive Kriging models are constructed based on a high-fidelity Reynolds-averaged Navier– Stokes (RANS) solver to assess the performance of the solutions. The successful integration of these design tools is facilitated through the reference point, which ensures that the swarm does not deviate from the preferred search trajectory. A comprehensive discussion on the proposed optimization framework is provided, highlighting its viability for the intended design application. 67.1 Airfoil Design....................................... 1311 67.1.1 Airfoil Design Architecture .......... 1311 67.1.2 Intelligent Optimization: PSO ...... 1312 67.1.3 Multiobjective Optimization ....... 1313 67.1.4 Surrogate Modeling ................... 1316 67.2 Shape Parameterization and Flow Solver ................................... 1317 67.2.1 The PARSEC Parameterization Method .................................... 1317 67.2.2 Transonic Flow Solver................. 1318 67.3 Optimization Algorithm........................ 1319 67.3.1 The Reference Point Method ....... 1319 67.3.2 User-Preference Multiobjective PSO: UPMOPSO ........................... 1320 67.3.3 Kriging Modeling ....................... 1322 67.3.4 Reference Point Screening Criterion ................................... 1323 67.4 Case Study: Airfoil Shape Optimization . . 1323 67.4.1 Pre-Optimization and Variable Screening .............. 1324 67.4.2 Optimization Results.................. 1325 67.4.3 Post-Optimization and Trade-Off Visualization ........ 1326 67.4.4 Final Designs ............................ 1327 67.5 Conclusion........................................... 1329 References ................................................... 1329