Optimal design of blade in pump as turbine based on multidisciplinary feasible method.

In order to make the pump as turbine (PAT) run efficiently and safely, a multidisciplinary optimization design method for PAT blade, which gives consideration to both the hydraulic and intensity performances, is proposed based on multidisciplinary feasibility (MDF) optimization strategy. This method includes blade parametric design, Latin Hypercube Sampling (LHS) experimental design, CFD technology, FEA technology, GA-BP neural network and NSGA-II algorithm. Specifically, a parameterized PAT blade with cubic non-uniform B-spline curve is adopted, and the control point of blade geometry is taken as the design variable. The LHS experimental design method obtains the sample points of training GA-BP neural network in the design space of variables. The hydraulic performance of each sample point (including the hydraulic pressure load on the blade surface) and the strength performance analysis of blades are completed by CFD and FEA technology respectively. In order to save calculation time of the whole optimization design, the multi-disciplinary performance analysis of each sample in the optimization process is completed by single-coupling method. Then, GA-BP neural network is trained. Finally, the multi-disciplinary optimization design problem of PAT blade is solved by the optimization technology combining GA-BP neural network and NSGA-II algorithm. Based on this optimization method, the PAT blade is optimized and improved. The efficiency of the optimized PAT is improved by 1.71% and the maximum static stress on the blade is reduced by 7.98%, which shows that this method is feasible.

Impeller meridional plane optimization of pump as turbine.

How to improve efficiency is still a very active research point for pump as turbine. This article comes up with a method for optimal design of pump as turbine impeller meridional plane. It included the parameterized control impeller meridional plane, the computational fluid dynamics technique, the optimized Latin hypercube sampling experimental design, the back propagation neural network optimized by genetic algorithm and genetic algorithm. Concretely, the impeller meridional plane was parameterized by the Pro/E software, the optimized Latin hypercube sampling was used to obtain the test sample points for back propagation neural network optimized by genetic algorithm, and the model corresponding to each sample point was calculated to obtain the performance values by the computational fluid dynamics techniques. Then, back propagation neural network learning and training are carried out by combining sample points and corresponding model performance values. Last but not least, back propagation neural network optimized by genetic algorithm and genetic algorithm were combined to deal with the optimization problem of impeller meridional plane. According to the aforementioned optimization design method, impeller meridional plane of the pump as turbine was optimized. The result manifests that the optimized pump as turbine energy-conversion efficiency was improved by 2.28% at the optimum operating condition, at the same time meet the pressure head constraint, namely the head difference between initial and optimized model is under the set numeric value. This demonstrates that the optimization method proposed in this article to optimize the impeller meridional plane is practicable.