A comprehensive study of high-efficiency optimization method on designing segmented annular thermoelectric module

To address the limitations of traditional numerical simulation methods in determining the optimal structure parameters of thermoelectric module, such as complex modeling procedures, low computational efficiency, and poor adaptability to multi-objective design, this paper introduces an efficient structural optimization approach of segmented annular thermoelectric module that combines the uniformly equivalent element integral method and multi-parameter and multi-objective optimization algorithm under both constant temperature and heat flux boundary conditions. The optimization results show that the optimal resistance ratio is independent of the boundary conditions, and the optimal thermoelectric leg ratios remain approximately 1.2 across all studied cases in this study. Notably, the optimal segment ratios are highly sensitive to the temperatures at the two ends of the optimized segmented annular thermoelectric module under all conditions and can be directly calculated using the proposed fitting formulas. In addition, an optimal total thermoelectric leg angle exists for the segmented annular thermoelectric module to achieve the maximum temperature difference within the operating temperature range of the thermoelectric materials. The output power and efficiency of the optimized segmented annular thermoelectric module can be predicted using the parameter-based fitting formulas, with relative errors below 3 % when compared to the direct optimization results. The proposed method in this paper offers significant advantages in terms of modeling simplicity, computational efficiency, and highly compatible with machine learning frameworks, thereby enabling artificial intelligence-assisted design and optimization pipelines for segmented annular thermoelectric modules.