基于并行多目标遗传算法大涵道分开式排气系统气动优化设计

Aerodynamic optimization design of high bypass ratio separate-flow exhaust system based on parallel multi-objective genetic algorithm

通过引入快速非支配排序算法、拥挤距离以及拥挤距离比较算子等对基本遗传算法进行改进,并结合massage passing interface(MPI)并行编程技术,发展了主从式并行多目标遗传算法(PMGA).将PM-GA与排气系统型面参数化设计方法、Navier-Stokes方程求解器相结合建立了分开式排气系统气动优化设计平台.应用该平台对某型分开式排气系统进行了多目标优化设计,得到了一组在三个目标上都优于初始设计的Pareto最优设计.将典型的Pareto最优设计和初始设计进行分析、比较,证明了该气动优化设计平台的高效性和可靠性.

Simple genetic algorithm （GA） was improved with fast non-dominated sort approach, crowded distance estimation and crowded comparison operator. A new algorithm called parallel multi-objective genetic algorithm （PMGA） was developed using the master- slave parallel programming model with the support of massage passing interface （MPI）. To establish the aerodynamic optimization design platform for high bypass ratio separate flow exhaust system, PMGA was combined with profile parameterization of exhaust system and Navier-Stokes solver. With this platform, aerodynamic optimization design of the high by- pass ratio separate-flow exhaust system was performed, and a set of Pareto-optimal solu- tions which was better than the initial design in three objectives was obtained. Detailed com- parison and analysis of the Pareto-optimal designs and initial design confirmed the high effi- ciency and validity of the parallel multi-objective aerodynamic optimization design platform.