Optimization of Two-Stage Combined Thermoelectric Devices by a Three-Dimensional Multi-Physics Model and Multi-Objective Genetic Algorithm

• Published in: Energies (Basel) 2019-07, Vol.12 (14), p.2832
• Main Authors: Meng, Jing-Hui, Wu, Hao-Chi, Wang, Tian-Hu
• Format: Article
• Language: English
• Subjects: Algorithms; Alternative energy; Alternative energy sources; Business metrics; combined thermoelectric device; Confined spaces; Cooling; Efficiency; Electric currents; Electric potential; Electric power; Electricity; Electricity distribution; Energy resources; Genetic algorithms; Geothermal energy; Heat; Heat conductivity; Mountains; multi-objective optimization; Multiple objective analysis; numerical simulation; Optimization; Parameter sensitivity; Physics; Power supplies; Power supply; Sensitivity analysis; Solar power; Sun; Temperature gradients; Thermal resistance; thermoelectric cooler; Thermoelectric cooling; thermoelectric generator; Thermoelectric generators
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en12142832

Due to their advantages of self-powered capability and compact size, combined thermoelectric devices, in which a thermoelectric cooler module is driven by a thermoelectric generator module, have become promising candidates for cooling applications in extreme conditions or environments where the room is confined and the power supply is sacrificed. When the device is designed as two-stage configuration for larger temperature difference, the design degree is larger than that of a single-stage counterpart. The element number allocation to each stage in the system has a significant influence on the device performance. However, this issue has not been well-solved in previous studies. This work proposes a three-dimensional multi-physics model coupled with multi-objective genetic algorithm to optimize the optimal element number allocation with the coefficient of performance and cooling capacity simultaneously as multi-objective functions. This method increases the accuracy of performance prediction compared with the previously reported examples studied by the thermal resistance model. The results show that the performance of the optimized device is remarkably enhanced, where the cooling capacity is increased by 23.3% and the coefficient of performance increased by 122.0% compared with the 1# Initial Solution. The mechanism behind this enhanced performance is analyzed. The results in this paper should be beneficial for engineers and scientists seeking to design a combined thermoelectric device with optimal performance under the constraint of total element number.