Open AccessPerformance Optimization of Thermoelectric Generators using Taguchi Method
Author(s) -
P. Ragupathi,
Debabrata Barik,
S. Aravind,
G. Vignesh
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1059/1/012053
Subject(s) - thermoelectric generator , taguchi methods , materials science , bismuth telluride , orthogonal array , thermoelectric materials , thermoelectric effect , waste heat , thermal energy , mechanical engineering , computer science , process engineering , automotive engineering , thermal conductivity , composite material , thermodynamics , physics , heat exchanger , engineering
The thermoelectric generators (TEGs) are devices that are utilized to convert the heat energy into electrical energy directly and the working principle of this device is based on See beck effect. Thermoelectric power production is a smart method for the direct translation of heat energy into an electrical one. This work explores a method to get the optimum process parameters on the performance of various TEGs by finding the conversion efficiency to recover the waste heat and converts it into electricity. For this purpose, an experimental setup was designed and fabricated to determine the performance of TEGs. The TEGs made by Bismuth Telluride (Bi 2 Te 3 ), Lead Telluride (PbTe), and Aluminium Oxide (Al 2 O 3 ) were taken for the performance analysis. The process variables are heat input, TEG material and temperature difference. The experiments were conducted by using Taguchi’s L9 orthogonal array to reduce the number of experiments. The results found that the heat input of 90W, TEG material of Bi2Te3 and the temperature difference of 75°C gives the maximum conversion efficiency of 2.45% from thermal to electrical energy. The statistical analysis of variance (ANOVA) showed that the most influential parameter on the performance of TEGs was heat input. The R2 and R2 (adj) values were found to be 93.25% and 86.50%, this shows that the developed model is significant and can predict the optimal solution.