Multiparameter optimization for the nonlinear performance improvement of centrifugal pumps using a multilayer neural network

Pei, Ji; Wang, Wenjie; Gan, Xingcheng

doi:10.1007/s12206-019-0516-6

Cited by 39 publications

(13 citation statements)

References 20 publications

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“…The measuring range of shaft torque meter and rotational speed meter was 0~2 N·m and 0~12000 rpm respectively, and the error level for this two meters were 0.2%. The hydraulic performance test of engine cooling pump was carried out in accordance with the China national standard requirements for hydraulic performance test of automobile engine cooling water pump [ 33 , 34 ]. The testing pump was connected to the test apparatus through special self-designed testing clamping apparatus, and the driving motor drove the pump to work at a certain rotational speed.…”

Section: Hydraulic Performance Improvement Studymentioning

confidence: 99%

Hydraulic performance prediction and optimization of an engine cooling water pump using computational fluid dynamic analysis

2021

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In current research, the hydraulic performance prediction and optimization of an engine cooling water pump was conducted by computational fluid dynamic (CFD) analysis. Through CFD simulation, the pump head, shaft power and efficiency for the original pump at volume flow rate 25 L/min and impeller rotating speed 4231 r/min were 3.87 m, 66.7 W and 23.09% respectively. For improving hydraulic performance, an optimization study was carried out. After optimization, four potential optimized designs were put forward. The efficiency of the optimized design No.1 for engine cooling water pump was nearly 6% higher than that of the original pump model; and the head of the optimized design No.2 for engine cooling water pump was 9% higher than that of the original pump model. Under the condition of maintaining the pump head and considering comprehensive improvement effect, the optimized design No.3 was considered as the best design and selected as the test case for validating the optimum design. The hydraulic performance predictions for this optimum engine cooling water pump agreed well with experimental data at design condition with relative discrepancies of 2.9% and 5.5% for the pump head and pump efficiency, respectively. It proved that performance prediction calculation model and the automatic optimization model were effective. This research work can provide theoretical basis for the design, development and optimization of engine cooling water pump.

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Section: Hydraulic Performance Improvement Studymentioning

confidence: 99%

Hydraulic performance prediction and optimization of an engine cooling water pump using computational fluid dynamic analysis

2021

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“…The multilayer ANN with more than one hidden layer can build a strong non-linear function between inputs and outputs. The kind of ANN can be divided into a feedforward neural network and cascade-forward neural network [30]. In this study, the multilayer cascade-forward ANN was applied as shown in Figure 6.…”

Section: Multilayer Artificial Neural Networkmentioning

confidence: 99%

Multiobjective Combination Optimization of an Impeller and Diffuser in a Reversible Axial-Flow Pump Based on a Two-Layer Artificial Neural Network

Meng

Yuan

et al. 2020

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This study proposed a kind of optimization design for a reversible axial-flow pump based on an ordinary one-way pump. Three-dimensional (3D) Reynolds-averaged Navier-Stokes (RANS) equations was used to predict the pump performance, and the optimized design was validated by an external characteristic test. Six main geometry parameters of an impeller and diffuser based on an orthogonal experiment were set as design variables. The efficiency and head under forward and reverse design conditions were set as the optimization objective. Based on 120 groups of sample designs obtained from Latin hypercube sampling (LHS), a two-layer artificial neural network (ANN) was used to build a non-linear function with high accuracy between the design variables and optimization objective. The optimized design was obtained from 300 groups of Pareto-optimal solutions using the non-dominated based genetic algorithm (NSGA) for multiobjective optimization. After optimization, there was a slight decrease in the forward pump efficiency and head. The reverse pump efficiency and head on the other hand was largely improved and the high efficiency range was also widened.was validated by an experiment. Liu et al. [11] used the optimal Latin hypercube sampling (LHS) method in the multicondition optimization of a mixed-flow pump and the optimization objective was chosen as weighted average efficiency at three flow rates. However, optimized design obtained from DOE is the optimal solution within the discrete design domain. Combination of the approximation model and intelligent algorithm can get the optimal solution within the continuous design domain. An approximation model is therefore being to construct a function between design variables and optimization objective. This function can then be solved by an optimization algorithm to obtain optimal optimized solutions. Pei et al. [12] therefore combined LHS, the artificial neural network (ANN) and modified particle swarm optimization (PSO) to obtain higher centrifugal pump efficiency at three flowrates. Miao et al. [13] applied the combination of neural networks and modified PSO algorithms to improve the pump efficiency and cavitation performance. Shim et al. [14,15] completed the multiobjective optimization based on approximation model and non-dominated based genetic algorithm (NSGA) to improve stability, efficiency and cavitation performance for different types of centrifugal pump. Wang et al. [16,17] used different surrogate models to optimize the impeller and diffuser of a centrifugal pump based on CFD. However, there is not much optimization design on a reversible axial-flow pump.The reversible axial blade pump generally has two-way impeller airfoils, which can be grouped into an arc, S-shape and polynomial curve. The S-shaped impeller can obtain similar pump performance under the forward and reverse condition. In some actual engineering, the pump operation time under the forward condition is much longer than that under the reverse condition. To obtain high pump efficiency under the forward co...

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“…is has made energy efficient centrifugal pumps a necessity since pumping power consumes about 10% of the global power share [1,2]. Centrifugal pumps are a group of turbomachinery with several applications ranging from domestic use to power plants and chemical and agricultural industries [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Also, Pei et al [11] carried out a multiobjective optimization on the inlet pipe shape of a vertical inline pump using artificial neural network (ANN) and multiobjective genetic algorithm (MOGA) to increase the efficiency over a wide range. Furthermore, ANN was combined with particle swarm optimization (PSO) to establish that the multilayer neural network has a better prediction accuracy compared with the single-layer neural network in centrifugal pump efficiency optimization [2]. Meng used ANN and the nondominated sorting genetic algorithm (NSGA II) to perform a combination optimization to improve the reverse pump efficiency of an axial flow pump [15].…”

Section: Introductionmentioning

confidence: 99%

Efficiency-House Optimization to Widen the Operation Range of the Double-Suction Centrifugal Pump

et al. 2020

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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 (Qd) 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 0.8Qd, 1.0Qd, and 1.2Qd, 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.

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Multiparameter optimization for the nonlinear performance improvement of centrifugal pumps using a multilayer neural network

Cited by 39 publications

References 20 publications

Hydraulic performance prediction and optimization of an engine cooling water pump using computational fluid dynamic analysis

Hydraulic performance prediction and optimization of an engine cooling water pump using computational fluid dynamic analysis

Multiobjective Combination Optimization of an Impeller and Diffuser in a Reversible Axial-Flow Pump Based on a Two-Layer Artificial Neural Network

Efficiency-House Optimization to Widen the Operation Range of the Double-Suction Centrifugal Pump

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