Optimization of the diffuser in a centrifugal pump by combining                     response surface method with multi-island genetic algorithm

Wang, Wenjie; Yuan, Shouqi; Pei, Ji; Zhang, Jinfeng

doi:10.1177/0954408915586310

Cited by 37 publications

(26 citation statements)

References 21 publications

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“…At present, optimization methods for pumps mainly include those based on simplified model prediction and accurate model analysis. 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.…”

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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“…It can be seen that the straight-type and convergent-divergent-type both gave exponential correlation, with the relationship between and flow ratio for straight-type being: (17) while that of convergent-divergent-type is:…”

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confidence: 97%

Kriging meta‐model assisted calibration of computational fluid dynamics models

et al. 2016

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Computational fluid dynamics (CFD) is a simulation technique widely used in chemical and process engineering applications. However, computation has become a bottleneck when calibration of CFD models with experimental data (also known as model parameter estimation) is needed. In this research, the kriging meta-modelling approach (also termed Gaussian process) was coupled with expected improvement (EI) to address this challenge. A new EI measure was developed for the sum of squared errors (SSE) which conforms to a generalised chi-square distribution and hence existing normal distribution-based EI measures are not applicable. The new EI measure is to suggest the CFD model parameter to simulate with, hence minimising SSE and improving match between simulation and experiments. The usefulness of the developed method was demonstrated through a case study of a single-phase flow in both a straight-type and a convergent-divergent-type annular jet pump, where a single model parameter was calibrated with experimental data.Keywords: Calibration, computational fluid dynamics, expected improvement, Gaussian process, parameter estimation Correspondence concerning this article should be addressed to Tao Chen at t.chen@surrey.ac.uk. 2 IntroductionComputational fluid dynamics (CFD) simulations are usually large complex computer programs representing real life fluid systems. The complexity of such simulations can make them quite a herculean task to solve, with the run time associated with the simulation being of great concern. Such simulation run time could be for hours or days.1,2 In addition, large scale or complex systems usually place a great demand on computer memory. 3 Computation is especially a problem when the CFD simulation is required to run for a large number of times, for example when sensitivity analysis is carried out 1,4 , or the models are used within a certain optimisation problem. 4,5 Of particular interest in this research project is calibration of CFD models (also known as model parameter estimation), which can be cast into an optimisation problem.CFD model calibration can be defined as the process of adjusting numerical or physical parameters in the CFD model which, on obtaining the optimal values 6 , helps to improve agreement with experimental data. 7,8,9 The parameters in this context are typically turbulence model coefficients or turbulence model choice. The default values given by CFD software providers for turbulence parameters may apply only in standard circumstances. In this paper, calibration is formulated as an optimisation problem to minimise the mismatch between simulated and experimental outputs (responses). The calibration process may need tens to hundreds of simulation runs to evaluate the objective function, the sum of squared errors (where the errors quantify the differences between the simulated and the experimental outputs) over the region of interest, a requirement that is often infeasible for CFD.These difficulties faced by CFD simulations have led to the application of meta-modelling, whi...

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“…ese characteristics are the main reasons for selecting GA in this work. MIGA is developed from the traditional GA and can avoid the "premature" phenomenon, which exists in general GAs [34,35]. erefore, MIGA is applied in this paper to solve the proposed optimization problem (equation 14).…”

Section: Miga Optimizationmentioning

confidence: 99%

Simultaneous Shape and Stacking Sequence Optimization of Laminated Composite Free‐Form Shells Using Multi‐Island Genetic Algorithm

San

Xiao

Qiu

2019

Advances in Civil Engineering

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A simultaneous shape and stacking sequence optimization algorithm is presented for laminated composite free-form shells, by which the coupled effect of shape and stacking sequence can be considered. The optimization objective is determined as maximizing fundamental natural frequency to obtain largest stiffness of shells. Nonuniform rational B-spline (NURBS) is employed to represent free-form geometrical shapes. The coordinates of NURBS control points and fiber orientations are set up as continuous and discrete optimization variables, respectively, and optimized simultaneously. To improve the efficiency of the mixed continuous-discrete optimization, multi-island genetic algorithm (MIGA) is employed to search for the global result. Through several numerical examples, the performance of the proposed approach is demonstrated in comparison with the two-phase optimization method; the effect of boundary conditions and the setup of control points on optimal results are investigated, respectively.

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Optimization of the diffuser in a centrifugal pump by combining response surface method with multi-island genetic algorithm

Cited by 37 publications

References 21 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

Kriging meta‐model assisted calibration of computational fluid dynamics models

Simultaneous Shape and Stacking Sequence Optimization of Laminated Composite Free‐Form Shells Using Multi‐Island Genetic Algorithm

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