Abstract
In view of the poor performance of pumps as turbines (PAT) operation, and the problem that the structural parameters cannot be optimized in the whole domain, the hybrid model of support vector machine (SVM) model and high-dimensional model representation (HDMR) method is applied to the optimization of PAT blade. Specifically, a PAT was selected, and the surrogate model for PAT blade optimization was constructed with MATLAB, Creo, and ANSYS software. The particle swarm optimization (PSO) algorithm was used to predict the performance data by global optimization. Finally, numerical prediction and experimental methods were used to verify the predicted data. These proved the applicability of the hybrid model in the optimization of fluid machinery. The numerical simulation results show that at the optimal operating point, the numerical simulation efficiency of the optimized PAT is 5.49% higher than that of the prototype PAT, and the output power is 7.2% higher. The test results show that the external characteristic curve of the numerical simulation PAT is basically consistent with the test results. At the optimal operating point, the test efficiency of the optimized PAT is 5.1% higher than that of the prototype PAT, and the output power is 6.9% higher.
Highlights
There are a lot of high-pressure residual energy liquid in many process industries
The control point at the intersection of two adjacent control points’ lines was defined as an independent variable xi of the adaptive function of the surrogate model, and the change direction of the included angle along the corresponding bisector was defined as the value change region of the independent variable
The support vector machine (SVM)-highdimensional model representation (HDMR) surrogate model combined with particle swarm optimization (PSO) algorithm was used to optimize the blade of Pumps as turbines (PAT)
Summary
There are a lot of high-pressure residual energy liquid in many process industries. Pumps as turbines (PAT) is the basic way to recover this part of energy because of its low cost, reliable operation, and convenient maintenance.[1,2] Due to the design reasons, the operation efficiency of PAT is generally low. As the main part of PAT energy conversion, impeller performance directly determines the hydraulic loss of impeller, and determines the efficiency of PAT. Traditional optimization methods of fluid machinery include semi-empirical and semi-theoretical formula optimization method,[3] experimental design method,[4] and approximate model method.[5] With the development of computer and the research of many computational surrogate models, such as support vector machine (SVM),[6] artificial neural
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