Experimental investigation and optimization of a low-temperature thermoelectric module with different operating conditions

Abstract

Purpose This paper aims to present an experimental investigation and optimization of a low-temperature thermoelectric module to examine the influence of the main operating conditions. Design/methodology/approach In this work, a comparison was made by varying the various operating parameters such as heat source temperature, the flow rate of the cold fluid and the external load resistance. A Taguchi method was applied to optimize the parameters of the system. Three factors, including the external load resistance, mass flow rate of water (at the heat sink side) and heater temperature (at the heat source side) along with different levels were taken into account. Analysis of variance was used to determine the significance and percentage contribution of each parameter. Findings The experimental results show that the maximum power output 8.22W and the maximum conversion efficiency 1.11 per cent were obtained at the heater temperature of 240°C, the cold fluid mass flow rate of 0.017 kg/s, module temperature difference of 45°C and the load resistance of 5 O. It was observed that the optimum parameter levels for maximum power output determined as 5 O external load resistance, 0.17 kg/s mass flow rate of water and 240°C heater temperature (A1B3C3). It reflects that these parameters influence on the optimum conditions. The heater temperature is the most significant parameter on the power output of the thermoelectric module. Originality/value It is clear from the confirmation test that experimental values and the predicted values are in good agreement.

Water-Cooled Thermoelectric Generators for Improved Net Output Power: A Review

“…The most widely used geometry for water cooling is the conventional channel, due to its ease of fabrication. Water cooled channel was used in different applications, such as waste heat recovery of an air flow [22,23], of a water flow [24,25], of a hot surface, i.e., a CPU [26][27][28], of automobile exhaust gas [29][30][31][32][33][34][35][36][37][38][39][40] and of industrial production [41]. However, it also finds application in the utilization of primary energy such as combustion [42,43],…”

“…Energies 2021, 14, x FOR PEER REVIEW 5 of 31 waste heat recovery of an air flow [22,23], of a water flow [24,25], of a hot surface, i.e., a CPU [26][27][28], of automobile exhaust gas [29][30][31][32][33][34][35][36][37][38][39][40] and of industrial production [41]. However, it also finds application in the utilization of primary energy such as combustion [42,43], geothermal energy [44] and solar energy [45][46][47][48].…”

Physics and applications of Raman distributed optical fiber sensing

“…Traditional electronic sensors which have high production costs are extremely prone to errors during measurements, resulting in an ineffective monitoring 160 , 161 , whereas, the Raman distributed optical fiber sensing can safely monitor the leakage of a dam. The advanced optical sensing can monitor the temperature field of the dam and feedback the information of the structure’s leakage field, so as to complete the detection and location of the fault within the structure.…”

“…For instance, Beck et al used the Raman distributed optical fiber sensing to analyze the monitored temperature data of the dam, realizing the seepage and leakage monitoring of the dam 160 . Furthermore, Henault et al conducted experimental researches on dam leakage detection and concrete structure strain monitoring based on the distributed optical fiber technology, simulating the strain between the optical fiber and the concrete structure 161 . Studying the location, direction and routing method of the sensing optical fibers in the dam are fundamental issues concerning dam leakage monitoring, and some researchers have elaborated on the application of optical fiber sensing instruments and their installation in actual projects 162 – 164 .…”

Multiresponse optimization of exhaust thermoelectric generator using Taguchi‐based gray relation analysis

“…The use of analysis of variance (ANOVA) method is an influential method that uses orthogonal arrays (OAs) to reduce the number of parameters 19 . The ANOVA analysis is used to find the percent contribution of the parameters 20 . Many multiattribute decision‐making techniques can be used for optimization, such as the gray relational analysis (GRA) and the data envelopment analysis 21 .…”