Application of Genetic Algorithm for Optimization of Heat-Transfer Parameters

Authors : Mustafa Akpinar

Pages : 1123-1130

Doi:10.16984/saufenbilder.500643

View : 19 | Download : 8

Publication Date : 2019-12-01

Article Type : Other

Abstract :Nowadays, new materials are developed with the aim to reduce heat transfer and energy loss. Thus, energy can be reduced and heat energy can be transferred efficiently. Many researches on the field of heat transfer have been made in the literature. However, there are few studies on the determination of insulation material thickness using heuristic algorithms and there is no study on finding the thermal boundary layer thickness using heuristic algorithms. One of the heuristic algorithms used in the field of computer science is Genetic Algorithm (GA), which is frequently applied in optimization problems. We propose that GA could be used to solve heat transfer problems of insulation material selection and laminar thermal boundary layer thickness determination. The goal of the proposal is to estimate the optimal parameters using a GA. In the first case, the thickness of insulation material selection and the maximum amount of heat loss that can be caused by different thicknesses of the insulating material under the boundary conditions and assumptions are calculated using GAs. It is shown that, using the heat-transfer coefficient and unit length cylinder, GAs can be used in everyday problems, such as determining the thickness of the insulating material or the outer temperature of the insulating material. In the second case, the boundary layer thickness is determined using GA for air flow with a laminar flow, where its characteristics are constant, irradiation is neglected, and plate and air temperatures are constant in the continuous regime on the plate. For both cases, the GA results are repeated 5 times and it is observed that the results are very close to each other. The experimental results demonstrate that, for both cases GA gives optimal target, minimum and maximum values, thus, GAs are applicable in heat-transfer problems that require optimization.
Keywords : Heat transfer, genetic algorithm, optimization