Geometric optimization of two-stage thermoelectric generator using genetic algorithms and thermodynamic analysis

Multi-objective genetic algorithms are used to optimize the structure, assignment of configuration and load resistance of a two-stage thermoelectric generator, where Skutterudite and Bi2Te3 are chosen as upper stage and lower stage TE leg materials, respectively. Heat convection and radiation are co...

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Vydané v:Energy (Oxford) Ročník 171; s. 37 - 48
Hlavní autori: Sun, Henan, Ge, Ya, Liu, Wei, Liu, Zhichun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Elsevier Ltd 15.03.2019
Elsevier BV
Predmet:
Algorithms
Bismuth tellurides
Computer simulation
Conduction
Conduction heating
Conductive heat transfer
Convection
Destruction
Entropy
Entropy (Information theory)
Entropy generation rate
Exergy
Exergy destruction rate
Genetic algorithms
Genetic analysis
geometry
Leg
legs
Load distribution
Load resistance
Multi-objective genetic algorithm
Multiple objective analysis
Ohmic dissipation
Optimization
Peltier effects
Performance enhancement
Power efficiency
Resistance heating
Seebeck effect
simulation models
Specific power
Substrates
Thermodynamics
Thermoelectric generator
Thermoelectric generators
Thermoelectricity
Multi-objective genetic algorithm
Entropy generation rate
Specific power
Exergy destruction rate
Thermoelectric generator
ISSN:0360-5442, 1873-6785
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Popis
Shrnutí:Multi-objective genetic algorithms are used to optimize the structure, assignment of configuration and load resistance of a two-stage thermoelectric generator, where Skutterudite and Bi2Te3 are chosen as upper stage and lower stage TE leg materials, respectively. Heat convection and radiation are considered on the top of the upper substrate. In the optimization process, the specific power and entropy generation rate are considered synchronously as objective functions to maximize the power output per unit area and to minimize the irreversibilities. The FEM is adopted in the simulation model, and the Seebeck effect, together with the Peltier effect, Joule heating, Thomson effect, and Fourier heat conduction phenomena are all considered in the simulation process. Shannon's entropy method is applied to select the best solution from the Pareto Frontier. Besides, the exergy destruction rate is analyzed, the results show that the exergy destruction rate increases as the load resistance increases. In addition, the different relationships between the load resistance and the voltage, power output, efficiency and entropy generation rate are presented. The principle of performance enhancement is also explained by comparing the ZT value along the TE legs. The optimization is important to the development of more compact and high-efficiency thermoelectric generators. •MOGAs are used to optimize structure, configuration and load resistance of a 2-stage TEGs.•Heat convection and radiation are considered on the top of the upper substrate.•Different effects related TEGs are all considered in simulation process.•Shannon's entropy method is applied to select the optimized solution.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2019.01.003