Multi-objective optimization of micro-fin helical coil tubes based on the prediction of artificial neural networks and entropy generation theory

The heat transfer, flow resistance and entropy generation characteristics of micro-fin helical coil tubes (MFHCTs) are investigated numerically. MFHCT with different fin numbers (2≤N≤6), coil pitches (150mm≤P≤450mm), coil diameters (600mm≤D≤1200mm) and Reynolds numbers (10945≤Re≤30845) are examined....

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jiaming Cao, Xuesheng Wang, Yuyang Yuan, Zhao Zhang, Yanbin Liu
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Multi-objective optimization Artificial neural network Entropy generation Micro-fin helical coil tube Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/79b0e9a790084ac8ba2b7a734430550c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:79b0e9a790084ac8ba2b7a734430550c
record_format	dspace
spelling	oai:doaj.org-article:79b0e9a790084ac8ba2b7a734430550c2021-12-04T04:34:17ZMulti-objective optimization of micro-fin helical coil tubes based on the prediction of artificial neural networks and entropy generation theory2214-157X10.1016/j.csite.2021.101676https://doaj.org/article/79b0e9a790084ac8ba2b7a734430550c2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2214157X2100839Xhttps://doaj.org/toc/2214-157XThe heat transfer, flow resistance and entropy generation characteristics of micro-fin helical coil tubes (MFHCTs) are investigated numerically. MFHCT with different fin numbers (2≤N≤6), coil pitches (150mm≤P≤450mm), coil diameters (600mm≤D≤1200mm) and Reynolds numbers (10945≤Re≤30845) are examined. The effects of these geometric parameters on the Nusselt number (Nu), friction factor (f) and improved entropy generation number (Ns′) are discussed. The performance of MFHCT is then compared to that of a smooth helical coil tube (SHCT). The results show that MFCHT always performs better than SHCT, especially in the lower Reynolds number region. Moreover, artificial neural networks (ANNs) are established to predict Nu, f and Ns′, which are trained by simulation data. This model fits the simulation results better than the multiple linear regression, and the maximum error is no greater than 8%. With the prediction of the network, the micro-fin helical coil tubes are optimized by the entropy minimization method and NSGA-III algorithm. Through optimization, the distribution of design variables is examined. The results demonstrate that a higher Reynolds number and a larger coil diameter and coil pitch lead to a better performance. Additionally, the optimal Pareto points can be utilized to guide the design and operation conditions of micro-fin helical coil tubes.Jiaming CaoXuesheng WangYuyang YuanZhao ZhangYanbin LiuElsevierarticleMulti-objective optimizationArtificial neural networkEntropy generationMicro-fin helical coil tubeEngineering (General). Civil engineering (General)TA1-2040ENCase Studies in Thermal Engineering, Vol 28, Iss , Pp 101676- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Multi-objective optimization Artificial neural network Entropy generation Micro-fin helical coil tube Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Multi-objective optimization Artificial neural network Entropy generation Micro-fin helical coil tube Engineering (General). Civil engineering (General) TA1-2040 Jiaming Cao Xuesheng Wang Yuyang Yuan Zhao Zhang Yanbin Liu Multi-objective optimization of micro-fin helical coil tubes based on the prediction of artificial neural networks and entropy generation theory
description	The heat transfer, flow resistance and entropy generation characteristics of micro-fin helical coil tubes (MFHCTs) are investigated numerically. MFHCT with different fin numbers (2≤N≤6), coil pitches (150mm≤P≤450mm), coil diameters (600mm≤D≤1200mm) and Reynolds numbers (10945≤Re≤30845) are examined. The effects of these geometric parameters on the Nusselt number (Nu), friction factor (f) and improved entropy generation number (Ns′) are discussed. The performance of MFHCT is then compared to that of a smooth helical coil tube (SHCT). The results show that MFCHT always performs better than SHCT, especially in the lower Reynolds number region. Moreover, artificial neural networks (ANNs) are established to predict Nu, f and Ns′, which are trained by simulation data. This model fits the simulation results better than the multiple linear regression, and the maximum error is no greater than 8%. With the prediction of the network, the micro-fin helical coil tubes are optimized by the entropy minimization method and NSGA-III algorithm. Through optimization, the distribution of design variables is examined. The results demonstrate that a higher Reynolds number and a larger coil diameter and coil pitch lead to a better performance. Additionally, the optimal Pareto points can be utilized to guide the design and operation conditions of micro-fin helical coil tubes.
format	article
author	Jiaming Cao Xuesheng Wang Yuyang Yuan Zhao Zhang Yanbin Liu
author_facet	Jiaming Cao Xuesheng Wang Yuyang Yuan Zhao Zhang Yanbin Liu
author_sort	Jiaming Cao
title	Multi-objective optimization of micro-fin helical coil tubes based on the prediction of artificial neural networks and entropy generation theory
title_short	Multi-objective optimization of micro-fin helical coil tubes based on the prediction of artificial neural networks and entropy generation theory
title_full	Multi-objective optimization of micro-fin helical coil tubes based on the prediction of artificial neural networks and entropy generation theory
title_fullStr	Multi-objective optimization of micro-fin helical coil tubes based on the prediction of artificial neural networks and entropy generation theory
title_full_unstemmed	Multi-objective optimization of micro-fin helical coil tubes based on the prediction of artificial neural networks and entropy generation theory
title_sort	multi-objective optimization of micro-fin helical coil tubes based on the prediction of artificial neural networks and entropy generation theory
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/79b0e9a790084ac8ba2b7a734430550c
work_keys_str_mv	AT jiamingcao multiobjectiveoptimizationofmicrofinhelicalcoiltubesbasedonthepredictionofartificialneuralnetworksandentropygenerationtheory AT xueshengwang multiobjectiveoptimizationofmicrofinhelicalcoiltubesbasedonthepredictionofartificialneuralnetworksandentropygenerationtheory AT yuyangyuan multiobjectiveoptimizationofmicrofinhelicalcoiltubesbasedonthepredictionofartificialneuralnetworksandentropygenerationtheory AT zhaozhang multiobjectiveoptimizationofmicrofinhelicalcoiltubesbasedonthepredictionofartificialneuralnetworksandentropygenerationtheory AT yanbinliu multiobjectiveoptimizationofmicrofinhelicalcoiltubesbasedonthepredictionofartificialneuralnetworksandentropygenerationtheory
_version_	1718372976641966080

Multi-objective optimization of micro-fin helical coil tubes based on the prediction of artificial neural networks and entropy generation theory

Ejemplares similares