Publication detail

Multi-objective optimization of elliptical tube fin heat exchangers based on neural networks and genetic algorithm

Zhang, Tianyi Chen, Lei Wang, Jin

Original Title

Multi-objective optimization of elliptical tube fin heat exchangers based on neural networks and genetic algorithm

Type

journal article in Web of Science

Language

English

Original Abstract

The application of machine learning based on neural networks (NNs) and genetic algorithm (GA) in multi-objective optimization of heat exchangers is studied. Taking the tube fin heat exchanger (TFHE) as the research object, the inlet air velocity and the ellipticity of tubes are taken as the optimization variables. In order to obtain the optimal heat transfer performance and pressure drop performance, Computational Fluid Dynamics (CFD) simulation is carried out for different Reynolds based on the hydraulic diameter numbers (150-750) and tube ellipticity (0.2-1). Then use simulation data to train the Back-Propagation neural networks and establish the prediction model of heat transfer coefficient and pressure drop. The non-dominated multi-objective genetic al-gorithm with elitist retention strategy (NSGA-II) is used to optimize two prediction results of NNs. Finally, the optimal heat transfer coefficient and pressure drop are given in the form of Pareto front. The optimization results show that when the Reynolds number is 541 and the ellipticity is 0.34, the pressure drop of the TFHE decreases 20%, and the heat transfer coefficient is basically unchanged, whose j/f is 1.28 times as much as that of the original heat exchanger.

Keywords

CFD; Genetic algorithm; Neural networks; Multi-objective optimization; PRESSURE-DROP CHARACTERISTICS; AIR SIDE PERFORMANCE; FRICTION CHARACTERISTICS; PLATE-FIN; CONFIGURATION; PARAMETERS; VORTICES

Authors

Zhang, Tianyi; Chen, Lei; Wang, Jin

Released

15. 4. 2023

Publisher

PERGAMON-ELSEVIER SCIENCE LTDTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND

Location

PERGAMON-ELSEVIER SCIENCE LTDTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND

ISBN

0360-5442

Periodical

Energy

Year of study

269

Number

1

State

United Kingdom of Great Britain and Northern Ireland

Pages count

9

URL

https://www.sciencedirect.com/science/article/pii/S0360544223001238?via%3Dihub

BibTex

@article{BUT187333,
  author="Zhang, Tianyi and Chen, Lei and Wang, Jin",
  title="Multi-objective optimization of elliptical tube fin heat exchangers based on neural networks and genetic algorithm",
  journal="Energy",
  year="2023",
  volume="269",
  number="1",
  pages="9",
  doi="10.1016/j.energy.2023.126729",
  issn="0360-5442",
  url="https://www.sciencedirect.com/science/article/pii/S0360544223001238?via%3Dihub"
}