基于NSGA-Ⅱ遗传算法的前弯型叶片透平多工况优化设计OA

To improve the efficiency of high-pressure liquid recovery and utilization by turbines in the seawater desalination field,a forward-bent blade turbine was selected as the research object.With hy-draulic efficiency at 0.6Qd,0.8Qd,1.0Qd and 1.2Qd operating conditions as the optimization objec-tives,combined with the matching performance of hydraulic components,five design variables of the volute and impeller were screened through Plackett-Burman experiments.A total of 100 sets of experi-ments were designed via optimal Latin hypercube sampling,and an intelligent optimization platform was established based on the Isight software.The multiple flow components optimization was accom-plished by coupling the RBF neural network with the NSGA-Ⅱ algorithm.The research results indicate that the weighted average efficiency of the optimized turbine is increased by 2.198％,and the hydraulic efficiencies under the four operating conditions are improved by 0.170％,1.990％,3.230％and 2.370％respectively.The performance and stability of the turbine under both partial load and overload conditions are significantly enhanced,and the operating range of the high-efficiency zone is expanded.After optimization,the intensity and scope of the separation vortex induced by the mismatch between the volute outlet angle and the blade inlet setting angle are reduced,and the unstable flows such as im-peller inlet backflow and flow separation are obviously mitigated.The vortex intensity near the volute tongue is decreased,the secondary flow is reduced,and the influence of rotor-stator interaction is weakened.This demonstrates that the matching performance between the volute and impeller of the op-timized turbine is improved,and the flow field control in the rotor-stator interaction area is streng-thened.Meanwhile,due to the optimized flow field control,the flow channel energy loss and multi-operation condition energy dissipation are reduced.