Application of Genetic Algorithm for Optimization of Heat-Transfer Parameters

Yıl 2019, , 1123 - 1130, 01.12.2019

Mustafa Akpınar

https://doi.org/10.16984/saufenbilder.500643

Cited By: 4

Öz

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.

Anahtar Kelimeler

Heat transfer, genetic algorithm, optimization

Kaynakça

• J. P. Holman, Heat Transfer, 10th ed. New York, NY: McGraw-Hill, 2010.
• Y. Jaluria, Design and Optimization of Thermal Systems, Second Edition, vol. 20071213. CRC Press, 2007.
• M. Hayati, A. Rezaei, and M. Seifi, “Prediction of the heat transfer rate of a single layer wire-on-tube type heat exchanger using ANFIS,” Int. J. Refrig., vol. 32, no. 8, pp. 1914–1917, Dec. 2009.
• R. V. Rao, K. C. More, J. Taler, and P. Ocłoń, “Optimal design of Stirling heat engine using an advanced optimization algorithm,” Sādhanā, vol. 41, no. 11, pp. 1321–1331, 2016.
• H. Shi, T. Ma, W. Chu, and Q. Wang, “Optimization of inlet part of a microchannel ceramic heat exchanger using surrogate model coupled with genetic algorithm,” Energy Convers. Manag., vol. 149, pp. 988–996, Oct. 2017.
• H. Sadeghzadeh, M. A. Ehyaei, and M. A. Rosen, “Techno-economic optimization of a shell and tube heat exchanger by genetic and particle swarm algorithms,” Energy Convers. Manag., vol. 93, pp. 84–91, Mar. 2015.
• A. K. John and K. Krishnakumar, “Performing multiobjective optimization on perforated plate matrix heat exchanger surfaces using genetic algorithm,” Int. J. Simul. Multidiscip. Des. Optim., vol. 8, p. A3, Jan. 2017.
• Y. Ge, F. Shan, Z. Liu, and W. Liu, “Optimal Structural Design of a Heat Sink With Laminar Single-Phase Flow Using Computational Fluid Dynamics-Based Multi-Objective Genetic Algorithm,” J. Heat Transfer, vol. 140, no. 2, p. 022803, Sep. 2017.
• R. R. Madadi and C. Balaji, “Optimization of the location of multiple discrete heat sources in a ventilated cavity using artificial neural networks and micro genetic algorithm,” Int. J. Heat Mass Transf., vol. 51, no. 9–10, pp. 2299–2312, May 2008.
• H. Saruhan and İ. Uygur, “Design Optimization of Mechanical Systems Using Genetic Algorithms,” Sak. Univ. J. Sci., vol. 7, no. 2, pp. 77–84, 2003.[11] M. Melanie, An Introduction to Genetic Algorithms, 5th ed. London: MIT Press, 1999.
• M. Melanie, An Introduction to Genetic Algorithms, 5th ed. London: MIT Press, 1999.
• R. L. Haupt and S. E. Haupt, Practical Genetic Algorithms, 2nd ed. New York, NY, USA: Wiley, 2004.
• F. Glover and G. A. Kochenberger, Eds., Handbook of Metaheuristics, vol. 57. Boston: Kluwer Academic Publishers, 2003.
• F. Kreith, R. M. Manglik, and S. B. Mark, Principles of Heat Transfer, 7th ed. Stamford, CT: Cengage learning, 2011.
• G. L. Shires, “Nusselt Number,” in A-to-Z Guide to Thermodynamics, Heat and Mass Transfer, and Fluids Engineering, Begellhouse.
• G. L. Shires, “Prandtl Number,” in A-to-Z Guide to Thermodynamics, Heat and Mass Transfer, and Fluids Engineering, Begellhouse.