Configuration optimization of two tandem airfoils at low Reynolds numbers

Abstract

Optimization was used to find the best configuration of two airfoils in tandem placed into an incoming flow. Upstream airfoil (forefoil) was pitching at a fixed frequency, while the downstream airfoil (hindfoil) was kept at a fixed angle of attack. Study was performed at a low Reynolds number of 30,000 based on chord length. Selig-Donovan 7003 (SD7003) was used for both airfoils, which is a high-performance airfoil specially designed for low Reynolds number flows. The optimization studies were conducted using a genetic algorithm (GA) to maximize aerodynamic performance. The design variables in this study were: horizontal and vertical spacing between the airfoils and hindfoil's angle of attack. Since the optimization process is time-consuming, machine learning was used to train four artificial neural networks (ANNs) to be coupled with genetic algorithm to reduce the computational cost. Two separate optimization cases were considered at two different orders of magnitude in pitching amplitudes of the forefoil, while the pitching frequency was kept at constant value. We found that in both cases, optimum tandem configurations had a smaller combined drag coefficient in comparison with the addition of two separate airfoils. The case with high pitching amplitude produced higher magnitude of lift, while the low amplitude case resulted in a significant improvement in aerodynamic performance.