Optimization of impulse water turbine based on GA-BP neural network arithmetic

Abstract

To develop an optimum design method for impulse water turbines with low specific speed, a representative impulse water turbine with low specific speed used in agricultural irrigation machinery was optimized with a combination of an orthogonal experimental design, a genetic algorithm, and a BP neural network in this study. Numerical calculation was applied to analyze interflow characteristics for optimized and original water turbines. Results showed that the internal flow characteristics of the optimized water turbine presented remarkable improvement compared with the original water turbine. Pressure distribution increased, the vortex strip in the draft tube was reduced remarkably, and impeller torque increased by 26 %. In addition, the optimized impeller was manufactured by 3D printing, and performance comparison was conducted between experiments of the optimized and original water turbines. The efficiency of the optimized water turbine reached 42.5 %, which exceeded the original water turbine’s of 8.5 %. With increasing rotating speed, maximum efficiency running point moved to a high flow rate, and highly efficient areas expanded. Internal characteristic analysis and a full-scale experiment for both water turbines showed that the performance of the optimized water turbine exhibited substantial improvement. The analysis and experiment also verified the theoretical correctness and feasibility of the proposed optimum design method.