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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F23%3APU150337" target="_blank" >RIV/00216305:26210/23:PU150337 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0360544223001238?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0360544223001238?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.energy.2023.126729" target="_blank" >10.1016/j.energy.2023.126729</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20303 - Thermodynamics

Projekt

<a href="/cs/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Laboratoř integrace procesů pro trvalou udržitelnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Energy

ISSN

0360-5442

e-ISSN

1873-6785

Svazek periodika

269

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

9

Strana od-do

„“-„“

Kód UT WoS článku

000963188300001

EID výsledku v databázi Scopus

2-s2.0-85147303104