Artificial neural network and genetic algorithm for multi-objective optimization in drilling of glass fiber reinforce polymer-stainless steel stacks

2018 ◽  
Author(s):  
A. Sateria ◽  
B. O. P. Soepangkat ◽  
Suhardjono
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Glass Fiber ◽  
Multi Objective Optimization ◽  
Fiber Reinforce Polymer ◽  
Glass Fiber Reinforce Polymer ◽  
Multi Objective ◽  
Fiber Reinforce

Multi-Objective Optimization on Inlet Pipe of a Vertical Inline Pump Based on Genetic Algorithm and Artificial Neural Network

2018 ◽  
Author(s):  
Xingcheng Gan ◽  
Ji Pei ◽  
Shouqi Yuan ◽  
Wenjie Wang ◽  
Yajing Tang
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Standard Deviation ◽  
Multi Objective Optimization ◽  
Inlet Pipe ◽  
Multi Objective ◽  
Design Variables ◽  
Artificial Neural

In order to save the space for installation, a bent pipe is adopted for inlet of vertical inline pump. In this paper, to improve the performance of inlet pipe, a multi-objective optimization on the inlet pipe is proposed based on Genetic Algorithm (GA) and Artificial Neural Network (ANN) model. A 5th-order Bezier curve is applied to fit the mean line of the inlet pipe and 3rd-order Bezier curves are used for depicting the variation trend of shape of sections. As the outlet of inlet pipe is fixed, 11 design variables are utilized for optimization, and the three optimization objectives are efficiency, head and standard deviation of velocity at the outlet of inlet pipe. To get the surrogate model, 149 different models obtained from Latin hypercube sampling are solved with numerical simulation. The results showed the numerical simulation has a great agreement with the experiment. Artificial neural network can accurately fit the target functions and design variables. The deviation of efficiency, head and standard deviation of velocity between predicted value and actual value were 0.26%, 0.05m and −0.27m/s, respectively. After optimization, an improvement on flow condition and a decrease of standard deviation of velocity before impeller were obtained. The efficiency and head were improved by 1.16% and 0.2m, respectively.

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