Abstract
[Display omitted]
•Energy/exergy analysis of a thermoelectric module and phase change material.•Surrogate machine learning to replace costly numerical computation is presented.•Novel numerical model utilizing actual climatic weather data is utilized.•Proposed system generates thermoelectric power during the day and night.•Optimum machine learning model provides accurate and quick performance evaluation.
Despite the merits of incorporating phase change materials in concentrating solar thermoelectric generating systems, the following research gaps still need to be filled to make the technology viable. Past research efforts focused on pulsed rectangular and sinusoidal heat flux forms which differ from actual transient weather data obtainable under field exposure. Additionally, a comprehensive exergy performance modeling of the system is yet to be presented in the literature, hence, there is no known thermodynamic implication of incorporating phase change materials in thermoelectric systems from a first and second law perspective. Finally, optimization insights obtained for designing these systems are based on inefficient, computationally expensive, and time-consuming numerical solvers or experimental methods which significantly hinder the ease at which useful design insights are obtained. This work aims to fill in these gaps by presenting the first-ever exergy performance investigation of a paraffin wax integrated thermoelectric generating system using data-driven surrogate machine learning models trained with numerically generated data computed from actual 12 h transient timeseries weather data collected every minute and averaged for a 10-year period (2010–2020). Results show that for an optical concentration of 10, the incorporated system with phase change material provides a 25%, 57%, 25%, and 56% improvement in the thermoelectric temperature difference, power generation, exergy efficiency, and thermodynamic irreversibility of the stand-alone system, respectively, without phase change material. Furthermore, the incorporated system generates electricity for 2 more hours into the night when solar irradiance is unavailable. Finally, the cheap surrogate machine learning model accurately predicts the thermodynamic performance of the stand-alone and incorporated systems with an almost zero error and almost unity coefficient of determination in just 5 s after the numerical method has taken 24 h to generate the data, indicating a 17,280-computation time saving.