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research-article

Thermal-hydraulic performance and optimization of tube ellipticity in fin-and-tube heat exchanger

[+] Author and Article Information
Zhu Hua

Department of Energy Engineering, Zhejiang University, Hangzhou 310027, China
zhuhua@zju.edu.cn

Zhuo Yang

Department of Energy Engineering, Zhejiang University, Hangzhou 310027, China; Co-Innovation Center for Advanced Aero-Engine, Zhejiang University, Hangzhou 310027, China
zyang7@zju.edu.cn

Tariq Amin Khan

Department of Mechanical Engineering, NFC-Institute of Engineering and Technology, P.O., Fertilizer Project, Khanewal Road, Multan, Pakistan
tariqamin4u@yahoo.com

Wei Li

Department of Energy Engineering, Zhejiang University, Hangzhou 310027, China
weili96@zju.edu.cn

Sun Zhijian

Department of Energy Engineering, Zhejiang University, Hangzhou 310027, China
zjsun@zju.edu.cn

Du Jincai

Department of Energy Engineering, Zhejiang University, Hangzhou 310027, China
dujc@zju.edu.cn

Zhang Zhengjiang

Department of Energy Engineering, Zhejiang University, Hangzhou 310027, China
zhangzj@zju.edu.cn

Jianxin Zhou

Hangzhou Yuhu Technology Co., LTD, Hangzhou, Zhejiang, China
447984029@qq.com

1Corresponding author.

ASME doi:10.1115/1.4043482 History: Received November 15, 2018; Revised April 11, 2019

Abstract

The flow field inside the heat exchangers is associated with maximum heat transfer and minimum pressure drop. Designing a heat exchanger and employing various techniques to enhance its overall performance has been widely investigated and is still an active research. The application of elliptic tube is an effective alternative to circular tube which can reduce the pressure drop significantly. In this study, numerical simulation and optimization of variable tube ellipticity is studied. The three-dimensional numerical analysis and a multi-objective genetic algorithm (MOGA) with surrogate modelling is performed. Tubes in staggered arrangement in fin-and-tube heat exchanger is investigated for combination of various elliptic ratio and Reynolds number. Results show that increasing elliptic ratio increases the friction factor due increased flow blocking area, however, the effect on the Colburn factor is not significant. Moreover, tube with lower elliptic ratio followed by higher elliptic ratio tube has better thermal-hydraulic performance. To achieve the best overall performance, the Pareto optimal strategy is adopted for which the CFD results, Artificial neural network (ANN) and Multi-objective genetic algorithm (MOGA) are combined. The tubes elliptic ratio and Reynolds number are the design variables. The objective functions include Colburn factor (j) and friction factor (f). The CFD results are input into ANN model. Once the ANN is computed, it is then used to estimate the model responses as a function of inputs. The final trained ANN is used to drive the MOGA to obtain the Pareto optimal solution. The optimal values of these parameters are finally presented.

Copyright (c) 2019 by ASME
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