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Thermoelectric generator

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Finite element method

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Applied Energy

The rising levels of global warming in the environment owing to emissions from fossil-fuel-based engines has increased the search for efficient clean energy systems. Thermoelectric generators (TEGs) standout as a promising energy conversion device which can directly convert heat to electricity. Several optimization studies have been carried out on these devices to improve their power generation rate and efficiencies while guaranteeing long lifespan. However, the limitations of finite element methods (FEMs) in easily providing optimization guidelines at a fast rate has hindered the manufacture of TEGs with high thermo-mechanical performance. This is why this paper presents the first ever artificial intelligence enabled optimization of a TEG conducted via deep neural networks (DNNs). Previous research on this topic completely neglected the mechanical performance and consequently, the service lifetime of the TEG when exposed to thermal operating conditions. To fill this gap, the effects of strategic parameters on the power output, efficiency and thermal stress performances of the TEG are investigated. These parameters are the hot and cold junction temperatures/heat transfer coefficients, incident heat flux, external load resistance, TE leg height, area, and area ratio. The DNN is fed with verified three-dimensional FEM simulations carried out on the ANSYS Workbench platform. The FEM results exhibit almost perfect correlation with experimental data which establishes the precision of the model. Results are that the DNN model is able to provide the necessary optimization guidelines for maximum thermo-mechanical performance in just 8 seconds compared to the 8 hours required by the FEM. The TEGs that were modelled using the DNN optimization guidelines improved the power, efficiency and mechanical performance of the unoptimized TEG by 11.94%, 14.17%, and 91%, respectively. These results are sufficient to provide useful guidelines at a fast rate for the fabrication of high-efficiency TEGs that will operate for a long time.

Thermo-mechanical optimization of thermoelectric generators using deep learning artificial intelligence algorithms fed with verified finite element simulation data