Title

Using dual mutation particle swarm method to optimize the variable cross-section of a thermoelectric generator based on a comprehensive thermodynamic model

Document Type

Article

Publication Date

12-1-2022

Publication Title

Cleaner Engineering and Technology

Volume

11

Abstract

The shape design for a thermoelectric generator (TEG) plays an important role in its performance. In this paper, a hyperbolic function was introduced to design a variable cross-section TEG module to optimize the configuration for maximum power generation and efficiency was sought. A comprehensive thermodynamic model was applied to establish the governing equations for the newly designed TEG module. The mutation particle swarm optimization (MPSO) method was invoked to solve the thermodynamic model. The thermodynamic model's output results include the temperatures at both ends of the TE element, thus making it possible to evaluate the actual performance of the TEG module. The results indicate that both the power generation and efficiency of the hyperbolic TEG are superior to those obtained based on a traditional design. The studies also disclosed that the hyperbolic structure can increase the thermal resistance of the TE couple making it possible to enlarge the temperature difference. This is the main mechanism to improve the performance of a hyperbolic TEG. Besides, the four non-dimensional parameters (shape parameter (β), area ratio (μ), temperature ratio (θ), and resistance ratio (rx)) related to the geometric structure and working conditions have notable effects on the TEG performance. It is thus worthwhile optimizing the TEG power generation and efficiency in the variable searching space of these parameters. However, differing from the traditional optimization, it is necessary to solve the governing equations in every iteration when searching for an optimal configuration based on the comprehensive model. In order to overcome the challenge, the Dual-MPSO algorithm was used in this research.

DOI

10.1016/j.clet.2022.100581

E-ISSN

26667908

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