DocumentCode
3180315
Title
Notice of Retraction
Blade optimization of Multiphase Rotodynamic pump based on neural network and genetic algorithm
Author
Xijin Ma ; Xinkai Li ; Zhonghui Hu ; Dengfeng Yang ; Nan Wang
Author_Institution
Sch. of Energy & Power Eng., Lanzhou Univ. of Tech, Lanzhou, China
fYear
2011
fDate
8-10 Aug. 2011
Firstpage
1979
Lastpage
1982
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
A new method based on neural network and genetic algorithm to optimizate the Multiphase Rotodynamic pump is given. Using cubic B-spline surface to parametric the blade profile. Based on the ability of highly nonlinear fitting of BP neural network, the nonlinear relation between the blade parameter and the pump performance parameters is build. Let the trained neural network as a fitness function of the genetic algorithm, using the characteristic of nonlinear global optimization of genetic algorithms to optimize multiphase rotodynamic pump. Through the fluent numerical calculation of the genetic algorithms output value, the results show that the capability of multiphase pump blade is improved, and then proved the feasibility of the optimization method.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
A new method based on neural network and genetic algorithm to optimizate the Multiphase Rotodynamic pump is given. Using cubic B-spline surface to parametric the blade profile. Based on the ability of highly nonlinear fitting of BP neural network, the nonlinear relation between the blade parameter and the pump performance parameters is build. Let the trained neural network as a fitness function of the genetic algorithm, using the characteristic of nonlinear global optimization of genetic algorithms to optimize multiphase rotodynamic pump. Through the fluent numerical calculation of the genetic algorithms output value, the results show that the capability of multiphase pump blade is improved, and then proved the feasibility of the optimization method.
Keywords
backpropagation; blades; genetic algorithms; mechanical engineering computing; neural nets; nonlinear programming; pumps; rotors; splines (mathematics); BP neural network; blade optimization; blade profile; cubic B-spline surface; genetic algorithm; multiphase rotodynamic pump; neural network training; nonlinear fitting; nonlinear global optimization; nonlinear relation; numerical calculation; pump performance parameter; Biological neural networks; Blades; Genetic algorithms; Impellers; Numerical models; Optimization; Training; Genetic Algorithm; multiphase rotodynamic pump; neural network; optimization design;
fLanguage
English
Publisher
ieee
Conference_Titel
Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), 2011 2nd International Conference on
Conference_Location
Dengleng
Print_ISBN
978-1-4577-0535-9
Type
conf
DOI
10.1109/AIMSEC.2011.6010930
Filename
6010930
Link To Document