Nanoparticle dispersions or more popularly “nanofluids” have been extensively researched for their candidature as working fluid in direct-volumetric-absorption solar thermal systems. Flexibility in carving out desired thermophysical and optical properties has lend the nanofluids to be engineered for solar thermal and photovoltaic applications. The key feature which delineates nanofluid-based direct absorption volumetric systems from their surface absorption counterparts is that here the working fluid actively (directly) interacts with the solar irradiation and hence enhances the overall heat transfer of the system. In this work, a host of nanoparticle materials have been evaluated for their solar-weighted absorptivity and heat transfer enhancements relative to the basefluid. It has been found that solar-weighted absorptivity is the key feature that makes nanoparticle dispersions suitable for solar thermal applications (maximum enhancement being for the case of amorphous carbon nanoparticles). Subsequently, thermal conductivity measurements reveal that enhancements on the order of 1–5% could only be achieved through addition of nanoparticles into the basefluid. Furthermore, dynamic light scattering (DLS) and optical measurements (carried out for as prepared, 5 h old and 24 h old samples) reveal that nanoclustering and hence soft agglomeration does happen but it does not have significant impact on optical properties of the nanoparticles. Finally, as a proof-of-concept experiment, a parabolic trough collector employing the amorphous carbon-based nanofluid and distilled water has been tested under the sun. These experiments have been carried out at no flow condition so that appreciable temperatures could be reached in less time. It was found that for the same exposure time, increase in the temperature of amorphous carbon based nanofluid is approximately three times higher as compared to that in the case of distilled water.
Amorphous carbon based nanofluids for direct radiative absorption in solar thermal concentrators – Experimental and computational study
