Swarm intelligence based on modified PSO algorithm for the optimization of axial-flow pump impeller

Miao, Fuqing; Park, Hae‐Sim; Kim, Cholmin; Ahn, Seokyoung

doi:10.1007/s12206-015-1034-9

Cited by 21 publications

(10 citation statements)

References 20 publications

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“…To improve the efficiency of a centrifugal pump, Wang WJ et al [16] and Pei J et al [17] optimized the impeller and guide vane, thereby obtaining a highly efficient hydraulic model. Miao F et al [18] proposed a multi-objective optimization of the impeller shape of an axial-flow pump based on the modified particle swarm optimization (MPSO) algorithm. This novel algorithm was successfully applied for the optimization of axial-flow pump impeller shape designs by comparing the results of the MPSO and Computational Fluid Dynamics (CFD) simulation results.…”

Section: Introductionmentioning

confidence: 99%

Multi-Disciplinary Optimization Design of Axial-Flow Pump Impellers Based on the Approximation Model

et al. 2020

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This study adopts a multi-disciplinary optimization design method based on an approximation model to improve the comprehensive performance of axial-flow pump impellers and fully consider the interaction and mutual influences of the hydraulic and structural designs. The lightweight research on axial-flow pump impellers takes the blade mass and efficiency of the design condition as the objective functions and the head, efficiency, maximum stress value, and maximum deformation value under small flow condition as constraints. In the optimization process, the head of the design condition remains unchanged or varies in a small range. Results show that the mass of a single blade was reduced from 0.947 to 0.848 kg, reaching a decrease of 10.47%, and the efficiency of the design condition increased from 93.91% to 94.49%, with an increase rate of 0.61%. Accordingly, the optimization effect was evident. In addition, the error between the approximate model results and calculation results of each response was within 0.5%, except for the maximum stress value. This outcome shows that the accuracy of the approximate model was high, and the analysis result is reliable. The results provide guidance for the optimal design of axial-flow pump impellers.

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Section: Introductionmentioning

confidence: 99%

Multi-Disciplinary Optimization Design of Axial-Flow Pump Impellers Based on the Approximation Model

et al. 2020

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“…If the standard PSO is used, the number of local optimum solutions may be too large, which causes the optimization to drop into the state of local optimum solution. To overcome this problem, the spatial neighbourhood method [23][24][25] is applied to revising the model structure of PSO. The main revision lies in the optimal position.…”

Section: Optimization Algorithmmentioning

confidence: 99%

Research on the coupled design method for the aerodynamic configuration of a mixed flow exhaust system with a confluent mixer

Zhou

Wei

et al. 2019

J Braz. Soc. Mech. Sci. Eng.

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A confluent mixer-nozzle-shaped central plug coupled design method by using the B-spline, CFD method and revised particle swarm optimization is proposed in this paper. Then, this method is used to redesign a mixed flow exhaust system with a confluent mixer, and the performance of the redesigned model is compared with the exhaust system designed by the separated method. According to the comparison results, the redesigned exhaust system has a better mass flux distribution than the original system. The difference in bypass ratio between the redesigned model and theoretical design is 2.8%. With respect to aerodynamic performance, the thrust of the redesigned exhaust system is 6.1% higher than that of the original system under the design condition, and its total pressure recovery coefficient increases to 0.995 from 0.987 of the original model. At off-design conditions, the thrust and total pressure recovery coefficient of the redesigned model increase by 4.3% and 0.6%, respectively, compared with the original model.

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“…This paper chooses to use LSSVR to establish the relationship between centrifugal pump geometry and hydraulic performance and tries to use the prediction ability of LSSVR for pump structure optimization. In view of the potential and superiority of PSO algorithm [19]- [23], we choose PSO to optimize the agent model and use it later to find the optimal solution in the prediction space.…”

Section: Introductionmentioning

confidence: 99%

Hydraulic Performance Optimization of Pump Impeller Based on a Joint of Particle Swarm Algorithm and Least-Squares Support Vector Regression

Fang

Zhang

et al. 2020

IEEE Access

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Swarm intelligence based on modified PSO algorithm for the optimization of axial-flow pump impeller

Cited by 21 publications

References 20 publications

Multi-Disciplinary Optimization Design of Axial-Flow Pump Impellers Based on the Approximation Model

Multi-Disciplinary Optimization Design of Axial-Flow Pump Impellers Based on the Approximation Model

Research on the coupled design method for the aerodynamic configuration of a mixed flow exhaust system with a confluent mixer

Hydraulic Performance Optimization of Pump Impeller Based on a Joint of Particle Swarm Algorithm and Least-Squares Support Vector Regression

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