Optimal design of the heat pipe using TLBO (teaching–learning-based optimization) algorithm

Heat pipe is a highly efficient and reliable heat transfer component. It is a closed container designed to transfer a large amount of heat in system. Since the heat pipe operates on a closed two-phase cycle, the heat transfer capacity is greater than for solid conductors. Also, the thermal response...

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Published inEnergy (Oxford) Vol. 80; pp. 535 - 544
Main Authors Rao, R.V., More, K.C.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.02.2015
Subjects
Algorithms
Conductors
Derivatives
Design optimization
Genetic algorithms
Heat pipe
Heat pipes
Heat transfer
Micro-grooves
Multi-objective optimization
Optimization
Teaching–learning-based optimization algorithm
Teaching–learning-based optimization algorithm
Multi-objective optimization
Heat pipe
Micro-grooves
Online AccessGet full text

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Abstract Heat pipe is a highly efficient and reliable heat transfer component. It is a closed container designed to transfer a large amount of heat in system. Since the heat pipe operates on a closed two-phase cycle, the heat transfer capacity is greater than for solid conductors. Also, the thermal response time is less than with solid conductors. The three major elemental parts of the rotating heat pipe are: a cylindrical evaporator, a truncated cone condenser, and a fixed amount of working fluid. In this paper, a recently proposed new stochastic advanced optimization algorithm called TLBO (Teaching–Learning-Based Optimization) algorithm is used for single objective as well as multi-objective design optimization of heat pipe. It is easy to implement, does not make use of derivatives and it can be applied to unconstrained or constrained problems. Two examples of heat pipe are presented in this paper. The results of application of TLBO algorithm for the design optimization of heat pipe are compared with the NPGA (Niched Pareto Genetic Algorithm), GEM (Grenade Explosion Method) and GEO (Generalized External optimization). It is found that the TLBO algorithm has produced better results as compared to those obtained by using NPGA, GEM and GEO algorithms. •The TLBO (Teaching–Learning-Based Optimization) algorithm is used for the design and optimization of a heat pipe.•Two examples of heat pipe design and optimization are presented.•The TLBO algorithm is proved better than the other optimization algorithms in terms of results and the convergence.
AbstractList Heat pipe is a highly efficient and reliable heat transfer component. It is a closed container designed to transfer a large amount of heat in system. Since the heat pipe operates on a closed two-phase cycle, the heat transfer capacity is greater than for solid conductors. Also, the thermal response time is less than with solid conductors. The three major elemental parts of the rotating heat pipe are: a cylindrical evaporator, a truncated cone condenser, and a fixed amount of working fluid. In this paper, a recently proposed new stochastic advanced optimization algorithm called TLBO (Teaching-Learning-Based Optimization) algorithm is used for single objective as well as multi-objective design optimization of heat pipe. It is easy to implement, does not make use of derivatives and it can be applied to unconstrained or constrained problems. Two examples of heat pipe are presented in this paper. The results of application of TLBO algorithm for the design optimization of heat pipe are compared with the NPGA (Niched Pareto Genetic Algorithm), GEM (Grenade Explosion Method) and GEO (Generalized External optimization). It is found that the TLBO algorithm has produced better results as compared to those obtained by using NPGA, GEM and GEO algorithms.
Heat pipe is a highly efficient and reliable heat transfer component. It is a closed container designed to transfer a large amount of heat in system. Since the heat pipe operates on a closed two-phase cycle, the heat transfer capacity is greater than for solid conductors. Also, the thermal response time is less than with solid conductors. The three major elemental parts of the rotating heat pipe are: a cylindrical evaporator, a truncated cone condenser, and a fixed amount of working fluid. In this paper, a recently proposed new stochastic advanced optimization algorithm called TLBO (Teaching–Learning-Based Optimization) algorithm is used for single objective as well as multi-objective design optimization of heat pipe. It is easy to implement, does not make use of derivatives and it can be applied to unconstrained or constrained problems. Two examples of heat pipe are presented in this paper. The results of application of TLBO algorithm for the design optimization of heat pipe are compared with the NPGA (Niched Pareto Genetic Algorithm), GEM (Grenade Explosion Method) and GEO (Generalized External optimization). It is found that the TLBO algorithm has produced better results as compared to those obtained by using NPGA, GEM and GEO algorithms. •The TLBO (Teaching–Learning-Based Optimization) algorithm is used for the design and optimization of a heat pipe.•Two examples of heat pipe design and optimization are presented.•The TLBO algorithm is proved better than the other optimization algorithms in terms of results and the convergence.
Author More, K.C.
Rao, R.V.
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Keywords Teaching–learning-based optimization algorithm
Multi-objective optimization
Heat pipe
Micro-grooves
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Snippet Heat pipe is a highly efficient and reliable heat transfer component. It is a closed container designed to transfer a large amount of heat in system. Since the...
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StartPage 535
SubjectTerms Algorithms
Conductors
Derivatives
Design optimization
Genetic algorithms
Heat pipe
Heat pipes
Heat transfer
Micro-grooves
Multi-objective optimization
Optimization
Teaching–learning-based optimization algorithm
Title Optimal design of the heat pipe using TLBO (teaching–learning-based optimization) algorithm
URI https://dx.doi.org/10.1016/j.energy.2014.12.008
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