Performance optimization of thermoelectric generators designed by multi-objective genetic algorithm

[Display omitted] •Output power and efficiency of a TEG system using waste heat from heat pipes are studied.•Geometry of TEG elements is optimized using a multi-objective genetic algorithm (MOGA).•The output power the TEG with optimization can be increased by about 51.9%.•The output power can be fur...

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Bibliographic Details
Published in:Applied energy Vol. 209; pp. 211 - 223
Main Authors: Chen, Wei-Hsin, Wu, Po-Hua, Lin, Yu-Li
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.01.2018
Subjects:
algorithms
ambient temperature
electric generators
geometry
Heat pipes
heat transfer
Impedance matching
industry
Multi-objective genetic algorithm (MOGA)
Optimization
Output power and efficiency
pipes
prediction
Thermoelectric generator
wastes
Multi-objective genetic algorithm (MOGA)
Impedance matching
Thermoelectric generator
Heat pipes
Optimization
Output power and efficiency
ISSN:0306-2619, 1872-9118
Online Access:Get full text
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Summary:[Display omitted] •Output power and efficiency of a TEG system using waste heat from heat pipes are studied.•Geometry of TEG elements is optimized using a multi-objective genetic algorithm (MOGA).•The output power the TEG with optimization can be increased by about 51.9%.•The output power can be further enhanced by up to 4.40% when impedance matching is used.•The maximized output power and efficiency are much higher than those reported in other studies. The purpose of this study is to investigate the output power and efficiency of a TEG system using waste heat from heat pipes, and then optimize its performance. The TEG material is Bi0.4Sb1.6Te3 and its figure-of-merit (ZT) is 1.18 at room temperature. The predictions indicate that a longer length of the elements has greater power output and efficiency based on a fixed heat flux on the hot side surface, whereas a shorter length has greater output power based on a fixed temperature difference. The geometry of the TEG is designed through a multi-objective genetic algorithm (MOGA) to maximize its efficiency. When the temperature difference is fixed at 40 °C, the output power and efficiency of the TEG with optimization is increased by about 51.9% and 5.4%, compared to the TEG without optimization. Once the impedance matching, namely, the internal resistance is equal to the external load resistance, is used, the output power can be further enhanced by about 3.85–4.40%. When the heat flux is fixed at 20,000 Wm−2 along with the temperature difference of 40 °C, the output power and efficiency of a pair of elements can be increased to 7.99 mW and 9.52%, respectively. These results are much higher than those reported in other studies. Accordingly, it is concluded that the MOGA is a powerful tool to design the geometry of a TEG for maximizing its performance and real applications in industry.
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2017.10.094