Data-driven analysis of molten-salt nanofluids for specific heat enhancement using unsupervised machine learning methodologies

Ranjan Parida, D and Dani, N and Basu, S (2021) Data-driven analysis of molten-salt nanofluids for specific heat enhancement using unsupervised machine learning methodologies. In: Solar Energy, 227 . pp. 447-456.

PDF sol_ene_227_447-456_2021.pdf - Published Version Restricted to Registered users only Download (3MB) | Request a copy

Abstract

High specific heat molten-salt is essential for sensible heat thermal energy storage. Current scientific researches focus on Molten-salt nanofluid as a potential solution. However, the causality between system parameters introduced in nanofluid preparation and specific heat enhancement is not clearly understood. Since difficulties are associated with identifying the explicit relations due to complex molecular interactions between molten-salt and nanoparticles, we inquired whether there is a common pattern/clusters in the nanofluid samples reported in earlier studies. The data-driven correlations among samples are explored by employing unsupervised machine learning methods: Hierarchical cluster analysis (HCA) and Principal component analysis (PCA). Three principal components, capturing 81.3 variation of the entire dataset, revealed that the descending order of contribution of the system parameters in the specific heat enhancement percent is concentration, temperature, density ratio, and nanoparticle size. The multivariate clusters emerging from HCA showed the interdependency of density ratio on the temperature, which significantly affects nanofluid's stability at higher concentration, causing a decrease in specific heat enhanced percent. Furthermore, the variation in nanoparticle size was found to have a negligible effect on specific heat enhancement. © 2021 International Solar Energy Society

Item Type:	Journal Article
Publication:	Solar Energy
Publisher:	Elsevier Ltd
Additional Information:	The copyright for this article belongs to Elsevier Ltd.
Keywords:	Cluster analysis; Digital storage; Fused salts; Heat storage; Hierarchical systems; Machine learning; Nanofluidics; Nanoparticles; Principal component analysis; Solar energy; Specific heat, Concentrated solar power; Heat enhancement; Hier-archical clustering; Hierarchical Clustering; Molten salt; Nanofluids; Principal component analyse; Principal-component analysis; Systems parameters; Unsupervised machine learning, Thermal energy, cluster analysis; energy storage; heat transfer; machine learning; nanoparticle
Department/Centre:	Division of Mechanical Sciences > Mechanical Engineering
Date Deposited:	08 Mar 2023 09:04
Last Modified:	08 Mar 2023 09:04
URI:	https://eprints.iisc.ac.in/id/eprint/80805

Actions (login required)

View Item