Multi-objective performance optimization analysis of biconical segmented annular thermoelectric generator based on grey wolf optimization algorithm

• Propose an optimization method combining RSM, MOGWO, and TOPSIS. • Analyze the effects and interactions of design variables on performance using RSM. • Achieve the optimal structure of BC-SATEG through multi-objective optimization. • The power and exergy efficiency of the optimized TEG increased by 32.18 % and 15.12 %. The low efficiency of thermoelectric generators has become a major obstacle to their widespread application. To improve the efficiency of the thermoelectric generator, this paper focuses on the multi-objective optimization for a segmented annular thermoelectric generator with the biconical structure. First, a numerical model of the thermoelectric generator is developed, and its accuracy is validated using experimental data. Second, through response surface methodology, the conical angle θ , leg angle β , leg angle ratio δ , and resistance ratio ω are selected as design variables, while power, exergy efficiency, and safety factor are chosen as response objectives to obtain the relationships between the response objectives and the design variables. Finally, multi-objective optimization is performed using the Grey Wolf Optimization algorithm, and the optimal structure is determined with the Technique for Order Preference by Similarity to Ideal Solution. The results show that the coefficient of determination R 2 of the regression equation constructed using the quadratic model in the prediction of thermoelectric generator performance is greater than 0.9, indicating that the model has a high goodness of fit. The findings indicate that the optimal values for θ , β , δ , and ω are 3.51°, 4.00°, 1.02, and 1.21. The performance of the optimized structure is P = 16.43 × 10 -3 W, η Ex =11.80 %, and S = 2.09. The optimal structure results in a 32.18 % increase in power and a 15.12 % improvement in exergy efficiency as opposed to the conventional thermoelectric generator. This study combines three methods to provide a new idea for the structural optimization design of thermoelectric generators.