Bayesian-Optimized Riblet Surface Design for Turbulent Drag Reduction via Design-by-Morphing With Large Eddy Simulation

Abstract

Abstract A computational approach is presented for optimizing new riblet surface designs in turbulent channel flow for drag reduction, utilizing design-by-morphing (DbM), large Eddy simulation (LES), and Bayesian optimization (BO). The design space is generated using DbM to include a variety of novel riblet surface designs, which are then evaluated using LES to determine their drag-reducing capabilities. The riblet surface geometry and configuration are optimized for maximum drag reduction using the mixed-variable Bayesian optimization (MixMOBO) algorithm. A total of 125 optimization epochs are carried out, resulting in the identification of three optimal riblet surface designs that are comparable to or better than the reference drag reduction rate of 8%. The Bayesian-optimized designs commonly suggest riblet sizes of around 15 wall units, relatively large spacing compared to conventional designs, and spiky tips with notches for the riblets. Our overall optimization process is conducted within a reasonable physical time frame with up to 12-core parallel computing and can be practical for fluid engineering optimization problems that require high-fidelity computational design before materialization.