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

Wei Hsin Chen, Po Hua Wu, Yu Li Lin

研究成果: Article

22 引文 (Scopus)

摘要

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.

原文English
頁(從 - 到)211-223
頁數13
期刊Applied Energy
209
DOIs
出版狀態Published - 2018 一月 1

指紋

genetic algorithm
Genetic algorithms
heat flux
temperature
Heat flux
geometry
Temperature
Geometry
Heat pipes
Waste heat
pipe
industry
prediction
Industry

All Science Journal Classification (ASJC) codes

  • Building and Construction
  • Energy(all)
  • Mechanical Engineering
  • Management, Monitoring, Policy and Law

引用此文

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Performance optimization of thermoelectric generators designed by multi-objective genetic algorithm. / Chen, Wei Hsin; Wu, Po Hua; Lin, Yu Li.

於: Applied Energy, 卷 209, 01.01.2018, p. 211-223.

研究成果: Article

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