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Prediction of heat transfer and fluid flow in a cross-corrugated tube using numerical methods, artificial neural networks and genetic algorithms

Prediction of heat transfer and fluid flow in a cross-corrugated tube using numerical methods,... In this paper, multi-objective optimization of geometric parameters of spirally-cross-corrugated (SCC) tubes is carried out using numerical methods, genetic algorithms (GAs), and artificial neural networks (ANNs). First, the turbulent flow is numerically characterized in various SCC tube geometries using a finite volume method with the realizable k−ε turbulence model. In this approach, the heat transfer coefficient and friction factor f in tubes are calculated. First, two parameters (corrugation pitch-to-diameter ratio (PR = p/D) and corrugation depth-to-diameter ratio (DR = e/D)) are examined in a turbulent flow regime that affects the strength of quadruple longitudinal vortex flows and thermal characteristics. At the final step, using the obtained polynomials for neural networks, multi-objective genetic algorithms (NSGA II) are employed for Pareto based multi-objective optimization of flow parameters in such tubes. This analysis considers two conflicting parameters, f Re and Nusselt number Nu with respect to three design variables, Reynolds number Re, values of PR and DR. Some interesting and important relationships between the parameters and variables mentioned above emerge as useful optimal design principles involved in the heat transfer of such tubes through Pareto based multi-objective optimization. Such important optimal principles would not have been obtained without the use of a combination of numerical techniques, ANN modeling, and the Pareto optimization. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Thermophysics and Aeromechanics Springer Journals

Prediction of heat transfer and fluid flow in a cross-corrugated tube using numerical methods, artificial neural networks and genetic algorithms

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Publisher
Springer Journals
Copyright
Copyright © S. Eiamsa-ard, V. Chuwattanakul, H. Safikhani, and P. Promthaisong 2022
ISSN
0869-8643
eISSN
1531-8699
DOI
10.1134/s0869864322020081
Publisher site
See Article on Publisher Site

Abstract

In this paper, multi-objective optimization of geometric parameters of spirally-cross-corrugated (SCC) tubes is carried out using numerical methods, genetic algorithms (GAs), and artificial neural networks (ANNs). First, the turbulent flow is numerically characterized in various SCC tube geometries using a finite volume method with the realizable k−ε turbulence model. In this approach, the heat transfer coefficient and friction factor f in tubes are calculated. First, two parameters (corrugation pitch-to-diameter ratio (PR = p/D) and corrugation depth-to-diameter ratio (DR = e/D)) are examined in a turbulent flow regime that affects the strength of quadruple longitudinal vortex flows and thermal characteristics. At the final step, using the obtained polynomials for neural networks, multi-objective genetic algorithms (NSGA II) are employed for Pareto based multi-objective optimization of flow parameters in such tubes. This analysis considers two conflicting parameters, f Re and Nusselt number Nu with respect to three design variables, Reynolds number Re, values of PR and DR. Some interesting and important relationships between the parameters and variables mentioned above emerge as useful optimal design principles involved in the heat transfer of such tubes through Pareto based multi-objective optimization. Such important optimal principles would not have been obtained without the use of a combination of numerical techniques, ANN modeling, and the Pareto optimization.

Journal

Thermophysics and AeromechanicsSpringer Journals

Published: Mar 1, 2022

Keywords: heat transfer; longitudinal vortex flow; spirally-cross-corrugated tube; multi-objective optimization

References