Grey relational based Taguchi analysis on thermal and electrical performances of thermoelectric generator system with inclined fins hot heat exchanger

In the present study, the grey relational based Taguchi analysis (GTA) to optimize the thermal and electrical performances of the thermoelectric generator system with inclined fins hot heat exchanger is carried out. The coupled numerical approach used for GTA of the thermoelectric generator system with inclined fins hot heat exchanger is firstly validated with the experimental data for thermal and electrical performances of the thermoelectric generator system with the straight fins hot heat exchanger within 6%. Three L-16 orthogonal arrays with the hot gas inlet mass flow rate (M), hot gas inlet temperature (T), design (D) and gap between the fins (G) are considered as the influencing factors for the GTA of the thermoelectric generator system with inclined fins hot heat exchanger. The GTA optimizes the thermal performances namely temperature difference and pressure drop with the first orthogonal array, the electrical performances namely power and conversion efficiency with the second orthogonal array and the combined thermal-electrical performances namely net energy efficiency and net exergy efficiency with the third orthogonal array. As the results, the decreasing orders of influence of various factors are M > G > T > D for the thermal performances and T > M > G > D for the electrical and the combined thermal-electrical performances. The optimum combination of four factors with the GTA shows the highest temperature difference of 99.72 degrees C, lowest pressure drop of 0.175 kPa, highest maximum power of 45.65 W, highest maximum conversion efficiency of 4.543%, highest net energy efficiency of 2.316% and highest net exergy efficiency of 2.418%, respectively.

Automated summary

In this study, grey relational based Taguchi analysis was used to optimize the thermal and electrical performances of a thermoelectric generator system with an inclined fins hot heat exchanger. The experimentally validated model considered factors like mass flow rate, temperature, design, and gap between fins for optimizing the system. The results showed the specific order of influence of factors on thermal, electrical, and combined thermal-electrical performances, leading to the identification of the optimal combination with the highest performance parameters.