Abstract
ABSTRACTTo 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.
Highlights
With the advancement of computational capabilities, the three-dimensional simulation of any complex geometries such as turbomachines is possible
The allocation of the angles was assured based on the literature (Li, 2002; Ohta & Aoki, 1996)
The impeller passage was generated by polynomial curves at the inlet and the outlet, so that a small change in angle produces a continuous surface
Summary
With the advancement of computational capabilities, the three-dimensional simulation of any complex geometries such as turbomachines is possible. The optimization of a turbomachines requires a large amount of performance data at different design points in order to generate an objective function. This process may take a long time to simulate, and the cost of optimization can be high. Hundreds of parameters of an impeller can be altered and the performance can be upgraded. Cao, Peng, and Yu (2004), Shi, Long, Li, Leng, and Zou (2010) and Marsis, Pirouzpanahand, and Morrison (2013) reported an improvement in pump performance by changing the design parameters A change in blade angles changes the hydraulic efficiency and the cavitation formation (Kamimoto & Matsuoka, 1956; Luo, Zhang, Peng, Xu, & Yu, 2008; Sanda & Daniela, 2012). Rutter, Sheth, and O’Bryan (2013) optimized the hydraulic performance of an electrical submersible pump (ESP) handling a single-phase fluid, with selected head, efficiency and input power as the objective functions. Cao, Peng, and Yu (2004), Shi, Long, Li, Leng, and Zou (2010) and Marsis, Pirouzpanahand, and Morrison (2013) reported an improvement in pump performance by changing the design parameters
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