Optimization Design Of Multi-Element Airfoil Based On The Genetical Algorithm With Subsection Evolution

Compare with some traditional optimal methods, genetic algorithm(GA)is more and more widely applied in the field of engineer optimization for its robustness, randomicity as well as global optimal performance. Meanwhile, with the high speed development of aviation technique, aerodynamic performance of multi-element airfoil is requested more and more. So, the purpose of this paper is to ameliorate the traditional GA so as to optimize the gap of multi-element airfoil. Four aspects are included:(1) Based on the basic theory of GA, make some modifications to carry out subsection evolution out, which can avoid the low efficiency because of the too long binary code. Substitute genetic factors for coefficients of model function used to express the figuration of multi-element airfoil, define the lift force coefficient as fitness function., and search the optimal solution through powerful searching performance of GA.(2) Generate the mesh used in every optimization design step through solving Ellipse equation and transform method. Add IBLANK value of every node to the NS solver to decide whether the node will be computed or not. So that different mesh can be solved through the NS solver and then transfer information to each other.(3) Study the theory of chimera grid, process two dimension program, including construction of overlap and flow field information transfer through inner and outer boundary and some other connection with other program. Bidirectional holes is put forward. Construction of hole around flap wing and main wing is carried out at the same time, this method is called bidirectional holes. It can provide enough wide overlap to make the information transfer easy and it can also improve the mesh quality of both main wing and flap wing.(4) Make two cases of multi-element wing optimization, and get the evolution history, airfoil shape modification, flow field data and so on. Through the analysis of the results, make the conclusion of that the GA optimal model is efficient and reasonable.