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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Veröffentlicht in:Energy (Oxford) Jg. 147; S. 1060 - 1069
Hauptverfasser: Ge, Ya, Liu, Zhichun, Sun, Henan, Liu, Wei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier Ltd 15.03.2018
Elsevier BV
Schlagworte:
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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Zusammenfassung: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.
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ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2018.01.099