Active flow control of airfoil using mesh/meshless methods coupled to hierarchical genetic algorithms for drag reduction design

摘要

Purpose - The purpose of this paper is to investigate an active flow control technique called Shock Control Bump (SCB) for drag reduction using evolutionary algorithms. Design/methodology/approach - A hierarchical genetic algorithm (HGA) consisting of multi-fidelity models in three hierarchical topological layers is explored to speed up the design optimization process. The top layer consists of a single sub-population operating on a precise model. On the middle layer, two sub-populations operate on a model of intermediate accuracy. The bottom layer, consisting of four sub-populations (two for each middle layer populations), operates on a coarse model. It is well-known that genetic algorithms (GAs) are different from deterministic optimization tools in mimicking biological evolution based on Darwinian principle. In HGAs process, each population is handled by GA and the best genetic information obtained in the second or third layer migrates to the first or second layer for refinement. Findings - The method was validated on a real life optimization problem consisting of two-dimensional SCB design optimization installed on a natural laminar flow airfoil (RAE5243). Numerical results show that HGA is more efficient and achieves more drag reduction compared to a single population based GA. Originality/value - Although the idea of HGA approach is not new, the novelty of this paper is to combine it with mesh/meshless methods and multi-fidelity flow analyzers. To take the full benefit of using hierarchical topology, the following conditions are implemented: the first layer uses a precise meshless Euler solver with fine cloud of points, the second layer uses a hybrid mesh/meshless Euler solver with intermediate mesh/clouds of points, the third layer uses a less fine mesh with Euler solver to explore efficiently the search space with large mutation span.