Thermal performance investigations by the use of nanofluids and phase change materials (PCM) in parabolic trough collectors (PTC) along with exergetic,economic,and application-based analyses

Abstract

This review article addressed the issues related to thermal efficiency increment and thermal performance investigation by usage of nanofluids and Phase Change Materials (PCM) as thermal performance enhancer and Thermal Energy Storage (TES) materials in Parabolic Trough Collectors (PTC). Also, the useful energy, exergy and the economic analysis utilizing PTC have also been explained and reviewed along with their applications. The different methods considered were experimental and some were numerical too. It was observed that, some of the nanofluids of metallic compounds like WS₂, Al₂O₃, SiC, Fe₃O₄ increased thermal efficiency by 31%, 35.7%, 30 and 40.41%. The usage of PCM like beeswax and paraffin wax as TES materials, led to increase in thermal efficiency by 54.04% and 85%. Exergetic efficiency increments of 5.49% was observed by the usage of Al₂O₃/SiO₂-Syltherm 800 hybrid nanofluid also by using MXene/Syltherm 800 hybrid nanofluid the levelized cost of energy was low and was 0.1324/kW. The applications of PTC as Concentrated Solar Power (CSP) technology for electricity and steam generation, and industrial process heat have also been summarized. This work aids by finding different kinds of optimal nanofluid and optimal PCM along with their types (organic, inorganic, and hybrid) and kind of work (experimental or numerical) in which they are used and heat transfer efficiency increase Mechanisms. The novelty of such compact review article is that in single review articles the recent progress and technical advancements of past decades work on PTC is getting fulfilled holistically, earlier reviews were based on a single or combination of such isolated topics. Finally, the emerging and current applications in the field of hybrid systems are also elucidated. The techniques like Artificial Intelligence, Machine Learning, and Deep Learning applied on PTC for thermal performance prediction and fault detection are also discussed.