Data-driven analysis of molten-salt nanofluids for specific heat enhancement using unsupervised machine learning methodologies
Section snippets
Introduction and motivation
Intermittency in renewable energy sources is a challenge for energy-harvesting technologies. In this context, solar thermal power plants' electricity production gets disrupted by day-night, seasonal, and weather fluctuations. To mitigate this disruption, thermal energy storages (TES) are commonly employed in Concentrated Solar Power Plants (CSP). TES preserves energy in the form of latent/sensible heat which is collected during the pick available period for later use. Typical sensible heat
Dataset and methodologies
The data set of nanofluid samples for the present study is selected from previously published researches (Andreu-Cabedo et al., 2014, Chen et al., 2019, Chen et al., 2018, Chieruzzi et al., 2017, Chieruzzi et al., 2013, Dudda and Shin, 2013, Ho and Pan, 2014, Hu et al., 2017, Jo and Banerjee, 2015; 2014; Lasfargues et al., 2015, Li et al., 2019, Qiao et al., 2017, Schuller et al., 2015, Seo and Shin, 2016, Shin and Banerjee, 2015, Shin and Banerjee, 2014, 2013, 2011a, 2011b; Song et al., 2018,
Results and discussion
Fig. 4 displays the results of HCA in the form of a dendrogram/hierarchical tree. Here, 141 samples are hierarchically connected depending on the Euclidean distance measure and complete linkage/farthest neighbor clustering in a 5-dimensional space. The clusters are identified by cutting the hierarchical tree horizontally near four Euclidean measure to ensure that univariate outliers (samples having an extreme value in one variable only) concerning the system variables are accurately detected.
Conclusion
In this study, two unsupervised machine learning algorithms (HCA and PCA) are introduced to identify unseen clustering structures in 141 molten salt nanofluid samples, published in earlier experimental works. HCA effectively extracted four major clusters, two minor clusters, and two singletons by considering correlations between system parameters of nanofluid samples. The minor clusters and the singletons were the outling samples owing to univariate extreme values (larger nanoparticle size
CRediT authorship contribution statement
Dipti Ranjan Parida: Conceptualization, Data curation, Analysis, Writing – original draft. Nikhil Dani: Data curation, Writing – original draft. Saptarshi Basu: Research, Supervision, Writing – review & editing, Funding acquisition.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This work is supported by Ministry of Human Resource Development, and Ministry of New and Renewable Energy, Government of India, under the IMPRINT initiative (Project No. 4424)
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