Thermal and electrical contact resistances of thermoelectric generator: Experimental study and artificial neural network modelling

Ying Li, Yong Shi, Xuehui Wang, Ding Luo, Yuying Yan

Research output: Journal PublicationArticlepeer-review

5 Citations (Scopus)

Abstract

Thermal and electrical contact resistances (TCR and ECR) of thermoelectric generator (TEG) exert essential impacts on its performance. In this study, through a series of experiments these two important properties have been estimated in a wide range of thermal and mechanical conditions, and with different interfacial materials. The magnitudes of the overall TCR were found in the range of (1.12–2.00) × 10–3 m2⋅K/W with air, (0.82–1.81) × 10–3 m2⋅K/W with graphene sheet, and (3.61–8.37) × 10–4 m2⋅K/W with thermal grease as interfacial materials when the heat-source temperature varied from 348.15 K to 598.15 K and the imposed pressure load from 266 kPa to 1266 kPa. Importantly, the detailed TCRs at different locations across the TEG system were also analyzed. The dominant components, which occupy more than 80 % of the overall TCR, have been identified at the interfaces of the thermoelectric module contacting the heat source and heat sink. In our experiment, the corresponding ECRs under the same working conditions were (1.03–1.52) × 10–9 Ω⋅m2, (0.56–9.60) × 10–10 Ω⋅m2, and (1.05–6.23) × 10–10 Ω⋅m2, respectively. Moreover, it is revealed that the TEG system delivered better performance at relatively low TCR and ECR when it operated at a high heat-source temperature, a large pressure load and using thermal grease as interfacial material. In addition to these experimental findings, a novel fully-connected feed-forward artificial neural network (ANN) model was also proposed to predict the overall TCR. It is shown that such an ANN model, as a promising approach, can achieve a cost-effective TCR prediction in good accuracy, with the mean square error and correlation coefficient being 2.36 × 10–9 and 99.4 %, respectively. These numerical and experimental results in this study will be of particular value for future TEG design and optimization.

Original languageEnglish
Article number120154
JournalApplied Thermal Engineering
Volume225
DOIs
Publication statusPublished - 5 May 2023

Keywords

  • Artificial neural network
  • Electrical contact resistance
  • Performance assessment of thermoelectric generator
  • Thermal contact resistance
  • Thermoelectric generator

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Industrial and Manufacturing Engineering

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