期刊
RENEWABLE ENERGY
卷 180, 期 -, 页码 1266-1277出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2021.09.033
关键词
Thermoelectric generator; Numerical simulation; Multiphysics; Experimental validation; Numerical modeling
This work proposes a novel fluid-thermal-electric multiphysics numerical model to predict the performance of thermoelectric generator systems applied to fluid waste heat recovery by considering the multiphysics coupling effects. The output power predicted by COMSOL coupled solver is 8.52% higher than that predicted by COMSOL separate solver. The TEG system's output performance predicted by ANSYS is less affected by inlet air boundary conditions than by COMSOL.
This work proposes a novel fluid-thermal-electric multiphysics numerical model to predict the performance of thermoelectric generator systems applied to fluid waste heat recovery, with the consideration of multiphysics coupling effects of fluid, thermal, and electric fields. The comprehensive numerical simulations of the thermoelectric generator system are performed via COMSOL coupled solver. Besides, the effect of the neglect of parasitic heat on the output performance is investigated through the comparison with numerical results predicted by ANSYS and COMSOL separate solver, wherein the fluid thermal field is computed first, then the thermal-electric field. The results show that the output power predicted by COMSOL separate solver is 8.52% lower than that predicted by COMSOL coupled solver at the inlet air temperature of 550 K and inlet air velocity of 30 m/s due to the neglect of parasitic heat. The output performance of the TEG system predicted by ANSYS is less affected by inlet air boundary conditions than that predicted by COMSOL. Finally, the experimental results show that the fluid-thermal electric multiphysics model solved by the COMSOL coupled solver shows the lowest output power deviation of 2.81%. The proposed model can guide the numerical modeling of the thermoelectric generator system applied to fluid waste heat recovery. (c) 2021 Elsevier Ltd. All rights reserved.
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