研究目的
To analyze the impact of the input factors (product of solar radiation and optical concentration, external load resistance, leg height of TE and ambient temperature) most affecting the electrical efficiency of CPV-TE system.
研究成果
The study successfully developed a polynomial statistical model to forecast the electrical efficiency of CPV-TE system. The influence of the parameters in order of importance on the electrical efficiency are respectively: product of solar radiation and optical concentration, the leg height of TE, external electrical resistance load, and ambient temperature. The maximum electrical efficiency of the proposed CPVTE (17.448%) is obtained for optimum operating parameters at 229.698 W/m2 value of product of solar radiation and optical concentration, 303.353 K value of ambient temperature, 2.681Ω value of resistance electrical load and at 3.083 mm value of height of TE module.
研究不足
The study focuses on the combined interactions between the four input factors and does not consider other potential factors that might affect the electrical efficiency of the CPV-TE system. The numerical model requires considerable resolution time, particularly for fine meshes.
1：Experimental Design and Method Selection:
A multi-physics numerical model coupling radiative, conductive and convective heat transfers, Seebeck and photoelectrical conversion physical phenomena inside the CPV-TE collector was developed. COMSOL
2：4 Multiphysics software was used for the three-dimensional numerical study based on finite element method. Sample Selection and Data Sources:
The study uses a commercial thermoelectric generator (GM250-71-14-16) consisting of 71 pairs of different semiconductor materials.
3：List of Experimental Equipment and Materials:
The photovoltaic module consists of five different layers, i.e., glazing, upper and bottom of EVA (Ethylene Vinyl Acetate), silicon solar cell and TPT back sheet layers. The bismuth telluride (Bi2Te3) thermoelectric modules are thermally attached to the TPT backside.
4：Experimental Procedures and Operational Workflow:
The governing heat balance equations were solved by the three-dimensional finite element method (FEM). The heat transfer in solid interface, electric current interface and electrical circuit interface were all used to perform the numerical investigation.
5：Data Analysis Methods:
The results from the numerical model were analyzed using the statistical tool, response surface methodology (RSM). The analysis of variance (ANOVA) was conducted to develop the quadratic regression model and examine the statistical significance of each input factor.
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