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

To increase efficiency at the design point of a centrifugal pump, this study adopted an artificial neural network in the construction of an accurate nonlinear function between the optimization objective and the design variables of impellers. Modified particle swarm optimization was further applied to refine the mathematical model globally. The database, which consisted of 200 sets of impellers, were generated from the Latin hypercube sampling method, and their corresponding efficiencies were obtained automatically from numerical simulation. Design variables were the distributions of blade angles, and results established that the difference between the numerical performance curve and the experimental results was acceptable. Optimization with a two-layer feedforward network improved the pump efficiency at the design point by 0.454 %. Flow complexity improved as the blade curvature increased. The application of the multilayer neural network could provide a meaningful reference to single- and multi-objective optimization of complex and nonlinear pump performance.

[1]  Jun Won Suh,et al.  Multi-Objective Optimization of the Hydrodynamic Performance of the Second Stage of a Multi-Phase Pump , 2017 .

[2]  Young-Seok Choi,et al.  Three-Objective Optimization of a Centrifugal Pump to Reduce Flow Recirculation and Cavitation , 2018 .

[3]  Akira Goto.  Historical Perspective on Fluid Machinery Flow Optimization in an Industry , 2016 .

[4]  Jun Won Suh,et al.  A study on numerical optimization and performance verification of multiphase pump for offshore plant , 2017 .

[5]  Florian R. Menter,et al.  Review of the shear-stress transport turbulence model experience from an industrial perspective , 2009 .

[6]  Hongwu Zhu,et al.  Multi-objective shape optimization of helico-axial multiphase pump impeller based on NSGA-II and ANN , 2011 .

[7]  Giovanna Cavazzini,et al.  Adaptive acceleration coefficients for a new search diversification strategy in particle swarm optimization algorithms , 2015, Inf. Sci..

[8]  M. Bezerra,et al.  Response surface methodology (RSM) as a tool for optimization in analytical chemistry. , 2008, Talanta.

[9]  Shu-Kai S. Fan,et al.  A hybrid simplex search and particle swarm optimization for unconstrained optimization , 2007, Eur. J. Oper. Res..

[10]  Farshad Kowsary,et al.  Optimal design approach for heating irregular-shaped objects in three-dimensional radiant furnaces using a hybrid genetic algorithm–artificial neural network method , 2018 .

[11]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[12]  Nilanjan Dey,et al.  Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings , 2016, Neural Computing and Applications.

[13]  Chao Yuan,et al.  Multi-objective hydraulic optimization and analysis in a minipump , 2015 .

[14]  Meng-Sing Liou,et al.  Transonic Axial-Flow Blade Optimization: Evolutionary Algorithms/Three-Dimensional Navier-Stokes Solver , 2004 .

[15]  David Lehký,et al.  Neural network ensemble-based parameter sensitivity analysis in civil engineering systems , 2017, Neural Computing and Applications.

[16]  Seokyoung Ahn,et al.  Swarm intelligence based on modified PSO algorithm for the optimization of axial-flow pump impeller , 2015 .

[17]  Multi-point optimization of transonic airfoils using an enhanced genetic algorithm , 2018 .

[18]  Ashkan Ojaghi,et al.  Numerical shape optimization of a centrifugal pump impeller using artificial bee colony algorithm , 2013 .

[19]  Farid Bakir,et al.  Efficiency of bio- and socio-inspired optimization algorithms for axial turbomachinery design , 2018, Appl. Soft Comput..

[20]  Antonios Tourlidakis,et al.  A PARALLEL GENETIC ALGORITHM APPLIED TO THE DESIGN OF BLADE PROFILES FOR CENTRIFUGAL PUMP IMPELLERS , 2001 .

[21]  Jack P. C. Kleijnen,et al.  Kriging Metamodeling in Simulation: A Review , 2007, Eur. J. Oper. Res..

[22]  Shun-ichi Amari,et al.  Network information criterion-determining the number of hidden units for an artificial neural network model , 1994, IEEE Trans. Neural Networks.