Optimal design of a segmented thermoelectric generator based on three-dimensional numerical simulation and multi-objective genetic algorithm

This paper proposes a general method to optimize the structure and load current for a segmented thermoelectric generator (TEG) module, where the bismuth telluride is selected as the cold side material, and the skutterudite is selected as the hot side material, respectively. Two objectives, minimum s...

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Vydáno v:	Energy (Oxford) Ročník 147; s. 1060 - 1069
Hlavní autoři:	Ge, Ya, Liu, Zhichun, Sun, Henan, Liu, Wei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Elsevier Ltd 15.03.2018 Elsevier BV
Témata:	Algorithms bismuth Bismuth tellurides cold Computer simulation Conduction Conduction heating Conductive heat transfer Design electric generators Electricity generation energy finite element analysis Finite element method Genetic algorithm Genetic algorithms heat Heat transfer Industrial applications Intermetallic compounds Mathematical models Multi-objective optimization Multiple objective analysis Numerical simulation Ohmic dissipation Optimization Peltier effects Performance assessment Semiconductors simulation models Studies Tellurides Thermoelectric generator Thermoelectric generators Thermoelectricity Numerical simulation Multi-objective optimization Thermoelectric generator Genetic algorithm
ISSN:	0360-5442, 1873-6785
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Abstract	This paper proposes a general method to optimize the structure and load current for a segmented thermoelectric generator (TEG) module, where the bismuth telluride is selected as the cold side material, and the skutterudite is selected as the hot side material, respectively. Two objectives, minimum semiconductor volume V′ and maximum output power P, are simultaneously considered to assess the performance of the TEG module. All the simulation models to be optimized by the multi-objective genetic algorithm are established and solved by finite element method, where the Thomson effect, in conjunction with Peltier effect, Joule heating, and Fourier heat conduction are simultaneously considered. In order to achieve the ultimate optimal design, TOPSIS (technique for order preference by similarity to an ideal solution) is employed to determine the best compromise solution. The results of Pareto solutions show that V′ varies from 432 mm3 to 3868 mm3, while P varies from 5.523 W to 56.293 W, respectively. Meanwhile, optimal design variables are investigated to provide practical guidance for the industrial applications. The mechanism of performance improvement has also been explained in this work by comparing the optimal segmented TEG and the skutterudite TEG. •Geometry and operating conditions of a thermoelectric generator are optimized.•Optimal solutions are obtained by coupling simulation and genetic algorithm.•TOPSIS technique is employed to determine the best compromised solution.•Effects of various input parameters on two objectives are reported.
AbstractList	This paper proposes a general method to optimize the structure and load current for a segmented thermoelectric generator (TEG) module, where the bismuth telluride is selected as the cold side material, and the skutterudite is selected as the hot side material, respectively. Two objectives, minimum semiconductor volume V′ and maximum output power P, are simultaneously considered to assess the performance of the TEG module. All the simulation models to be optimized by the multi-objective genetic algorithm are established and solved by finite element method, where the Thomson effect, in conjunction with Peltier effect, Joule heating, and Fourier heat conduction are simultaneously considered. In order to achieve the ultimate optimal design, TOPSIS (technique for order preference by similarity to an ideal solution) is employed to determine the best compromise solution. The results of Pareto solutions show that V′ varies from 432 mm3 to 3868 mm3, while P varies from 5.523 W to 56.293 W, respectively. Meanwhile, optimal design variables are investigated to provide practical guidance for the industrial applications. The mechanism of performance improvement has also been explained in this work by comparing the optimal segmented TEG and the skutterudite TEG. This paper proposes a general method to optimize the structure and load current for a segmented thermoelectric generator (TEG) module, where the bismuth telluride is selected as the cold side material, and the skutterudite is selected as the hot side material, respectively. Two objectives, minimum semiconductor volume V′ and maximum output power P, are simultaneously considered to assess the performance of the TEG module. All the simulation models to be optimized by the multi-objective genetic algorithm are established and solved by finite element method, where the Thomson effect, in conjunction with Peltier effect, Joule heating, and Fourier heat conduction are simultaneously considered. In order to achieve the ultimate optimal design, TOPSIS (technique for order preference by similarity to an ideal solution) is employed to determine the best compromise solution. The results of Pareto solutions show that V′ varies from 432 mm3 to 3868 mm3, while P varies from 5.523 W to 56.293 W, respectively. Meanwhile, optimal design variables are investigated to provide practical guidance for the industrial applications. The mechanism of performance improvement has also been explained in this work by comparing the optimal segmented TEG and the skutterudite TEG. •Geometry and operating conditions of a thermoelectric generator are optimized.•Optimal solutions are obtained by coupling simulation and genetic algorithm.•TOPSIS technique is employed to determine the best compromised solution.•Effects of various input parameters on two objectives are reported. This paper proposes a general method to optimize the structure and load current for a segmented thermoelectric generator (TEG) module, where the bismuth telluride is selected as the cold side material, and the skutterudite is selected as the hot side material, respectively. Two objectives, minimum semiconductor volume V′ and maximum output power P, are simultaneously considered to assess the performance of the TEG module. All the simulation models to be optimized by the multi-objective genetic algorithm are established and solved by finite element method, where the Thomson effect, in conjunction with Peltier effect, Joule heating, and Fourier heat conduction are simultaneously considered. In order to achieve the ultimate optimal design, TOPSIS (technique for order preference by similarity to an ideal solution) is employed to determine the best compromise solution. The results of Pareto solutions show that V′ varies from 432 mm³ to 3868 mm³, while P varies from 5.523 W to 56.293 W, respectively. Meanwhile, optimal design variables are investigated to provide practical guidance for the industrial applications. The mechanism of performance improvement has also been explained in this work by comparing the optimal segmented TEG and the skutterudite TEG.
Author	Ge, Ya Sun, Henan Liu, Wei Liu, Zhichun
Author_xml	– sequence: 1 givenname: Ya surname: Ge fullname: Ge, Ya – sequence: 2 givenname: Zhichun surname: Liu fullname: Liu, Zhichun email: zcliu@hust.edu.cn – sequence: 3 givenname: Henan surname: Sun fullname: Sun, Henan – sequence: 4 givenname: Wei surname: Liu fullname: Liu, Wei
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Snippet	This paper proposes a general method to optimize the structure and load current for a segmented thermoelectric generator (TEG) module, where the bismuth...
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SubjectTerms	Algorithms bismuth Bismuth tellurides cold Computer simulation Conduction Conduction heating Conductive heat transfer Design electric generators Electricity generation energy finite element analysis Finite element method Genetic algorithm Genetic algorithms heat Heat transfer Industrial applications Intermetallic compounds Mathematical models Multi-objective optimization Multiple objective analysis Numerical simulation Ohmic dissipation Optimization Peltier effects Performance assessment Semiconductors simulation models Studies Tellurides Thermoelectric generator Thermoelectric generators Thermoelectricity
Title	Optimal design of a segmented thermoelectric generator based on three-dimensional numerical simulation and multi-objective genetic algorithm
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