To reduce the total design and optimization time, numerical analysis with surrogate-based approaches is being used in turbomachinery optimization. In this work, multiple surrogates are coupled with an evolutionary genetic algorithm to find the Pareto optimal fronts (PoFs) of two centrifugal pumps with different specifications in order to enhance their performance. The two pumps were used a centrifugal pump commonly used in industry (Case I) and an electrical submersible pump used in the petroleum industry (Case II). The objectives are to enhance head and efficiency of the pumps at specific flow rates. Surrogates such as response surface approximation (RSA), Kriging (KRG), neural networks and weighted-average surrogates (WASs) were used to determine the PoFs. To obtain the objective functions' values and to understand the flow physics, Reynolds-averaged Navier-Stokes equations were solved. It is found that the WAS performs better for both the objectives than any other individual surrogate. The best individual surrogates or the best predicted error sum of squares (PRESS) surrogate (BPS) obtained from cross-validation (CV) error estimations produced better PoFs but was still unable to compete with the WAS. The high CV error-producing surrogate produced the worst PoFs. The performance improvement in this study is due to the change in flow pattern in the passage of the impeller of the pumps.

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In this paper, an effort has been made to maximize the efficiency of ESP by using surrogate models for optimization. Inlet and outlet blade angles are selected as design variables. Initially, a pump stage simulation is performed using Navier Stokes solver which include rotating impeller and stationary diffuser. The steps of the simulations are: generate geometry, grid and solve. The surrogate models were response surface approximation, Kriging and radial basis neural network. A hybrid genetic algorithm has been implemented to find optimal design. The multiple surrogate approach was used to reduce uncertainty in optimal point search. The initial CFD generated results were used to generate surrogates and the surrogates were used to give optimal point via genetic algorithm. Design of experiments were used to find the designs and low fidelity based surrogates were constructed to reduce the number of simulation runs. The optimization procedure sufficiently reduces the design time. Hence, the approach shows an optimal design of ESP and selection method of right surrogate for the shortening of computational cost.

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A numerical analysis and optimization has been done for a single stage electric submersible pump (ESP) which is basically a centrifugal pump to retrofit into wellbore in groundwater pumping or oil pumping. The pump is designed and simulated using a commercial code. The objective was to improve the pump performance if the inlet and outlet angles of impeller and diffuser are modified. Hence, the variables were the blade angles and 15 different designs were produced via Latin hyper cube sampling within the variable ranges. The designs were evaluated to find the hydraulic efficiency or the objective function value using a Reynolds average Navier Stokes equation solver. Two surrogates were constructed from the CFD results and the optimal points were predicted. The optimal designs were again evaluated using CFD solver and further analyses were made. It was found that the efficiency was increased by 4.23%.