Rapid design and optimization of airfoil based on improved genetic algorithm

Electric aircraft with low carbon consumption is gradually developed along with the growing demand of civilian aircraft. The production of electric aircraft pursues lower costs and shorter development cycle. In the process of designing an airfoil, it is hard to select the initial airfoil, and most optimization methods are very time consuming. An improved genetic algorithm (GA) with variable resolution is developed for rapid multi-objective optimization of airfoils. Based on the original Hicks-Henne shape function, the representation of airfoil on trailing edge is improved. In the calculation of aerodynamic parameters, a subsonic airfoil development system XFOIL is introduced which is faster than conventional CFD software, and the applicability and limitation of XFOIL is also analyzed. Then a joint method combining XFOIL and Matlab is proposed, and it realizes a full automatic design of airfoil without the intervention of human. In the stage of optimization, parameters of airfoil are real-coded to maintain high accuracy and efficiency. In addition, the conventional GA is improved by hybridization with variable resolution and dynamic penalty. At last, the integrated design solution of rapid multi-objective and multi-point optimization is summarized. Simulation is divided into two parts, and the improved Hicks-Henne shape function can change the angle of trailing edge effectively. By the comparison with elitist nondominated sorting genetic algorithm (NSGA-II), the proposed method will get higher lift to drag ratio within certain number of iterations, the stall characteristic is more moderate, and it especially improves efficiency performance. The integrated design solution is accelerated by numerical calculation and improved GA, and it has a lower computational cost. The simulation results show that the method is useful in engineering conditioning for the rapid design and optimization of airfoil shapes, particularly in the preliminary design stage, such as the selection of initial airfoil. Currently it is uncertain whether the results of proposed method are better than others after a long time of iterations, and our future work will focus on it.