摘要
An optimization method for 3D blade and meridional contour of centrifugal or mixed-flow impeller based on the 3D viscous computational fluid dynamics （CFD） analysis is proposed. The blade is indirectly parameterized using the angular momentum and calculated by inverse design method. The design variables are separated into two categories： the meridional contour design vari- ables and the blade design variables. Firstly, only the blade is optimized using genetic algorithm with the meridional contour remained constant. The artificial neural network （ANN） techniques with the training sample data schemed according to design of experiment theory are adopted to construct the response relation between the blade design variables and the impeller performance. Then, based on the ANN approximated relation between the meridional contour design variables and impeller per- formance, the meridional contour is optimized. Fewer design variables and less calculation effort is required in this method that may be widely used in the optimization of three-dimension impellers. An optimized impeller in a mixed-flow pump, where the head and the efficiency are enhanced by 12.9% and 4.5% respectively, confirms the validity of this newly proposed method.
为 3D 片和南方的轮廓的一个优化方法离心或混合流动 impeller 基于 3D 粘滞计算液体动力学(CFD ) 分析被建议。片是间接地用尖动量的 parameterized 并且由反的设计方法计算。设计变量被分开成二个范畴：南方的轮廓设计变量和片设计变量。第一，仅仅片与南方的轮廓用基因算法被优化仍然是的常数。有根据实验理论的设计策划的训练样品数据的人工的神经网络(ANN ) 技术被采用构造在片设计变量和 impeller 性能之间的反应关系。然后，基于 ANN 接近了在南方的轮廓设计变量和 impeller 性能之间的关系，南方的轮廓被优化。更少设计变量和更少计算努力在这个方法被要求可以广泛地在三尺寸的 impellers 的优化被使用。在头和效率被 12.9% 和 4.5% 分别地提高的地方，在混合流动泵的优化 impeller 证实这个最新建议的方法的有效性。
基金
This project is supported by National Natural Science Foundation of China (No.50136030).
