Numerical modeling and multi-objective optimization of direct absorption solar collectors using mono and hybrid nanofluids

Abstract

Over the years, the development of nanofluids has created new possibilities for research in the field of renewable energy. There has been rapid progress in the study of the optical characteristics of nanofluids for their potential use in Direct Absorption Solar Collectors (DASCs). Nanofluids may significantly improve photothermal conversion because of their enhanced optical properties. This study introduces a numerical model aimed at assessing the performance of DASC using mono and hybrid nanofluids. The model achieves this goal by solving the coupled radiation transfer equations in a participating medium together with the thermal energy equations. The realistic incident solar spectrum and effective optical properties of copper and alumina nanoparticles are also considered. The mathematical model is validated with experimental results from the literature. A parametric study is then carried out to study the sensitivity of the DASC performance on some design and operation parameters, such as the aspect ratio, heat transfer coefficient, and incident angle. The influence of copper and alumina nanoparticles on the DASC efficiency is also studied and compared. Due to their optical characteristics, it is established that the useful gain in copper-based nanofluid is much higher than in alumina-based nanofluid. Moreover, the use of Cu–Al2O3 hybrid nanofluid is studied at different combinations of volume fractions to understand the techno-economic impact on the DASC technology. The use of response surface methodology allows studying how modifications in the concentration of individual nanoparticles used in DASC can influence both its cost and performance. A multi-objective optimization is then performed at various loadings of the nanoparticles to maximize the DASC efficiency and minimize the total cost of the working fluid. Finally, Pareto front solutions are obtained as a guide for selecting the optimum combinations of nanoparticles that optimize the cost and performance of the DASC.