مقاله انگلیسی رایگان در مورد بهینه سازی هندسی مولدهای ترموالکتریک دو مرحله ای با استفاده از الگوریتم های ژنتیکی – الزویر ۲۰۱۹

۱- Introduction

۲- Modeling

۳- Theoretical analysis and optimization

۴- Results and discussion

۵- Conclusions

References

Abstract

Multi-objective genetic algorithms are used to optimize the structure, assignment of configuration and load resistance of a two-stage thermoelectric generator, where Skutterudite and Bi2Te3 are chosen as upper stage and lower stage TE leg materials, respectively. Heat convection and radiation are considered on the top of the upper substrate. In the optimization process, the specific power and entropy generation rate are considered synchronously as objective functions to maximize the power output per unit area and to minimize the irreversibilities. The FEM is adopted in the simulation model, and the Seebeck effect, together with the Peltier effect, Joule heating, Thomson effect, and Fourier heat conduction phenomena are all considered in the simulation process. Shannon’s entropy method is applied to select the best solution from the Pareto Frontier. Besides, the exergy destruction rate is analyzed, the results show that the exergy destruction rate increases as the load resistance increases. In addition, the different relationships between the load resistance and the voltage, power output, efficiency and entropy generation rate are presented. The principle of performance enhancement is also explained by comparing the ZT value along the TE legs. The optimization is important to the development of more compact and high-efficiency thermoelectric generators.

Introduction

In recent years, there has been a significant increase in the energy demand, which corresponds to economic development. The development of alternative energy is encouraged because of the limited storage of traditional energy. To solve issues related to energy safety and environmental problems, many countries have focused on solar energy, owing to its clean and renewable characteristics [1]. Apart from solar cells and solar thermal systems, solar thermoelectric generators (TEGs) are considered an alternative technology that can convert heat flux directly into electric power by employing a phenomenon called the Seebeck effect. In despite of its low efficiency, it is mainly used for waste heat recovery systems and power supply systems of space detectors, owing to its long lifespan, small volume, solid-state components, the absence of moving parts, its stable operation, and as well as the absence of pollutant [2]. Because of its comparatively low thermal efficiency, many studies have focused on how to improve the performance of TEGs. The performance can be evaluated by a dimensionless quantitycalled the figure of merit (ZT), which is defined as ZT ¼ a2sT=l, where the Seebeck coefficient(a), electrical conductivity(s) and thermal conductivity(l) are functions of temperature T. There are two main ways of improving the performance of TEGs, including making improvements to materials technology. Ways in which this may be realized include employing a sufficiently wider temperature range, a reduction in the thermal conductivity of the lattice, improved thermoelectric properties through doping, removal of impurities, and improved microstructure design [3e10]. Another approach is to optimize the geometric configurations [11]. This paper focuses on the impact of the geometric configuration on the TEG performance as well as on thermodynamic analysis. G. Fraisse et al. [12] compared the different modeling approaches for thermoelectric coolers (TECs) and TEGs, an overestimation of about 9% at the maximum power point occurred in a standard simplified model, and the finite-element method (FEM) model is the most accurate one in performance prediction. Haider Ali et al.