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• W3088635049 abstract "Most pumping machineries have a problem of obtaining a higher efficiency over a wide range of operating conditions. To solve that problem, an optimization strategy has been designed to widen the high-efficiency range of the double-suction centrifugal pump at design ( Q d ) and nondesign flow conditions. An orthogonal experimental scheme is therefore designed with the impeller hub and shroud angles as the decision variables. Then, the “efficiency-house” theory is introduced to convert the multiple objectives into a single optimization target. A two-layer feedforward artificial neural network (ANN) and the Kriging model were combine based on a hybrid approximate model and solved with swarm intelligence for global best parameters that would maximize the pump efficiency. The pump performance is predicted using three-dimensional Reynolds-averaged Navier–Stokes equations which is validated by the experimental test. With ANN, Kriging, and a hybrid approximate model, an optimization strategy is built to widen the high-efficiency range of the double-suction centrifugal pump at overload conditions by 1.63%, 1.95%, and 4.94% for flow conditions 0.8 Q d , 1.0 Q d , and 1.2 Q d , respectively. A higher fitting accuracy is achieved for the hybrid approximation model compared with the single approximation model. A complete optimization platform based on efficiency-house and the hybrid approximation model is built to optimize the model double-suction centrifugal pump, and the results are satisfactory." @default.
• W3088635049 created "2020-10-01" @default.
• W3088635049 creator A5024173689 @default.
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• W3088635049 date "2020-09-26" @default.
• W3088635049 modified "2023-10-16" @default.
• W3088635049 title "Efficiency-House Optimization to Widen the Operation Range of the Double-Suction Centrifugal Pump" @default.
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• W3088635049 doi "https://doi.org/10.1155/2020/9737049" @default.
• W3088635049 hasPublicationYear "2020" @default.
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