Minimize pressure drop and maximize heat transfer coefficient by the new proposed multi-objective optimization/statistical model composed of "ANN plus Genetic Algorithm" based on empirical data of CuO/paraffin nanofluid in a pipe

被引：29

作者：

Bagherzadeh, Seyed Amin ^{[1
]}

Sulgani, Mohsen Tahmasebi ^{[1
]}

Nikkhah, Vahid ^{[2
]}

Bahrami, Mehrdad ^{[1
]}

Karimipour, Arash ^{[1
]}

Jiang, Yu ^{[3
]}

机构：

[1] Islamic Azad Univ, Dept Mech Engn, Najafabad Branch, Najafabad, Iran

[2] Semnan Univ, Sch Chem Gas & Oil Engn, Semnan, Iran

[3] China Univ Min & Technol, Sch Mechatron Engn, Xuzhou 211006, Jiangsu, Peoples R China

来源：

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2019年 / 527卷

基金：

美国国家科学基金会;

关键词：

Multi-objective optimization; Artificial neural network; Genetic algorithm; Empirical results; CuO/liquid paraffin nanofluid; LID-DRIVEN CAVITY; MAGNETO-NATURAL CONVECTION; ARTIFICIAL NEURAL-NETWORK; SILVER-WATER NANOFLUID; SOLID VOLUME FRACTION; THERMAL-CONDUCTIVITY; HYBRID NANOFLUID; SLIP VELOCITY; LATTICE BOLTZMANN; FLUID-FLOW;

D O I：

10.1016/j.physa.2019.121056

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

A new multi-objective optimization model composed of the artificial neural network (ANN) and the genetic algorithm (GA) methods based on the empirical thermo-physical characteristics of CuO/liquid paraffin nanofluid flow in a pipe is presented for the first time. It means a new optimization /statistical approach is achieved based on ANN together with GA; so that at first ANN is employed to predict the nanofluid thermo-physical properties and then the heat transfer coefficient and the pressure drop ratios of the nanofluid to the basefluid, are optimized as well as to minimize the pressure drop ratio and maximize the heat transfer coefficient ratio by using the multi-objective optimization approach of GA. The results of the multi-objective optimization via the GA show that the Pareto optimal front quantifies the trade-offs in satisfying the two fitness function of heat transfer coefficient and the pressure drop ratios. (C) 2019 Elsevier B.V. All rights reserved.

引用

页数：13

共 1 条

[1] Present a new multi objective optimization statistical Pareto frontier method composed of artificial neural network and multi objective genetic algorithm to improve the pipe flow hydrodynamic and thermal properties such as pressure drop and heat transfer coefficient for non-Newtonian binary fluids
Wu, Huawei
Bagherzadeh, Seyed Amin
D'Orazio, Annunziata
Habibollahi, Navid
Karimipour, Arash
Goodarzi, Marjan
Quang-Vu Bach
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2019, 535

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