Multi-objective hydraulic optimization and analysis in a minipump

Bin, Duan; Luo, Meng; Yuan, Chao; Luo, Xiaobing

doi:10.1007/s11434-015-0876-y

Cited by 15 publications

(4 citation statements)

References 27 publications

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“…3,4 When using CFD method to evaluate the performance of centrifugal pump, it is required to go through several steps, that is, modeling, meshing, boundary condition definition, turbulence model selection, and iterative calculation. [5][6][7] To get desired prediction results, these steps need to be carefully finished which also based on the experience of users. Moreover, due to the complexity of centrifugal pump geometry, the mechanical loss and part of leakage loss are usually not considered in the CFD calculation.…”

Section: Introductionmentioning

confidence: 99%

“…The data-based approximation models are used to evaluate the objective function in the optimization process of centrifugal pump to replace timeconsuming CFD calculations. [5][6][7] However, due to the limited nonlinear learning capability of the commonly used approximate models, such as BPNN, 6 radial basis function neural network (RBFNN), 13 response surface model (RSM), 14,15 the number of impeller parameters considered as design variables is usually less than five. This is not sufficient for the fine optimization design since the performance of centrifugal pump is affected not only by the independent geometric parameters but also by the combination of geometric parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Up to now, in the optimization design of centrifugal pump with three to five design variables and two to three objective function variables, there are more than 40 samples need to be calculated through CFD to ensure the accuracy of the approximate model. 5,6 The number of training samples will continue to increase with the expansion of the design space and the increase in the number of design variables and objective functions. This is not very efficient for fast engineering optimization design.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A comparative study of Gaussian process regression with other three machine learning approaches in the performance prediction of centrifugal pump

Zhao

Stankovic

Zhang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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Accurate prediction of performance indices using impeller parameters is of great importance for the initial and optimal design of centrifugal pump. In this study, a kernel-based non-parametric machine learning method named with Gaussian process regression (GPR) was proposed, with the purpose of predicting the performance of centrifugal pump with less effort based on available impeller parameters. Nine impeller parameters were defined as model inputs, and the pump performance indices, that is, the head and efficiency, were determined as model outputs. The applicability of three widely used nonlinear kernel functions of GPR including squared exponential (SE), rational quadratic (RQ) and Matern5/2 was investigated, and it was found by comparing with the experimental data that the SE kernel function is more suitable to capture the relationship between impeller parameters and performance indices because of the highest R square and the lowest values of max absolute relative error (MARE), mean absolute proportional error (MAPE), and root mean square error (RMSE). In addition, the results predicted by GPR with SE kernel function were compared with the results given by other three machine learning models. The comparison shows that the GPR with SE kernel function is more accurate and robust than other models in centrifugal pump performance prediction, and its prediction errors and uncertainties are both acceptable in terms of engineering applications. The GPR method is less costly in the performance prediction of centrifugal pump with sufficient accuracy, which can be further used to effectively assist the design and manufacture of centrifugal pump and to speed up the optimization design process of impeller coupled with stochastic optimization methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A comparative study of Gaussian process regression with other three machine learning approaches in the performance prediction of centrifugal pump

Zhao

Stankovic

Zhang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

show abstract

“…In 2002, Deb et al [15] proposed a modified version of NSGA, the well-known NSGA-II algorithm, which has a superior comprehensive performance compared with other evolutionary algorithms and is widely used in optimizing the centrifugal impellers. Duan et al [16] used NSGA-II to optimise the conflict between pressure head and hydraulic efficiency. The tests proved that the optimised mini-pump increased the pressure head by about 24% and the hydraulic efficiency by 4.75% compared with the original design prototype, and the flow field analysis showed that the optimised model could alleviate the impeller wake and improve the pressure distribution and flow direction.…”

mentioning

confidence: 99%

Multi-Objective Aerodynamic Design Optimisation Method of Fuel Cell Centrifugal Impeller Using Modified NSGA-II Algorithm

Liu

et al. 2021

Applied Sciences

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This paper presents a modified NSGA-II algorithm based on the spatial density (SD) operator, combined with computer graphics-based surface parameterisation methods and computational fluid dynamics (CFD) simulations. This was done to optimise the multi-objective aerodynamic design of a centrifugal impeller for a 100-kW vehicle-mounted fuel cell and improve the multi-conditions aerodynamic performance of the centrifugal impeller of the vehicle-mounted fuel cell (FC). The optimisation objectives are to maximise the isentropic efficiency of the rated and common operating conditions. The optimisation results showed that the efficiency of rated working conditions had an increase of 1.29%, mass flow increase of 8.8%, pressure ratio increase of 0.74% and comprehensive margin increase of 6.2%. The efficiency of common working conditions had an increase of 1.2%, mass flow increase of 9.1%, pressure ratio increase of 0.24% and comprehensive margin increase of 10%. The optimisation effect is obvious under the premise of satisfying the constraints, which proves the optimisation method’s engineering effectiveness and provides technical support and methodological research for the multi-objective aerodynamic design optimisation of centrifugal impellers for vehicle-mounted FCs.

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A novel hydrodynamic suspension micropump using centrifugal pressurization and the wedge effect

Xing

Xue

Hong

et al. 2023

Sci. China Technol. Sci.

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Multi-objective hydraulic optimization and analysis in a minipump

Cited by 15 publications

References 27 publications

A comparative study of Gaussian process regression with other three machine learning approaches in the performance prediction of centrifugal pump

A comparative study of Gaussian process regression with other three machine learning approaches in the performance prediction of centrifugal pump

Multi-Objective Aerodynamic Design Optimisation Method of Fuel Cell Centrifugal Impeller Using Modified NSGA-II Algorithm

A novel hydrodynamic suspension micropump using centrifugal pressurization and the wedge effect

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