Using statistical analysis to create a new database of Nanofluids' specific heat capacity

Abstract

Nowadays, heat transfer fluids (HTFs) with high thermal properties are needed to develop more efficient and compact energy systems to achieve sustainable development goals. Nanofluids (NFs), through the incorporation of nanoparticles in conventional HTFs, become one of the most suitable techniques to improve their thermophysical properties. However, despite its potential industrial applications, there is not only a lack of a theoretical framework but also a clear trend about its behavior. Therefore, this work aims to perform a critical review and statistical analysis to understand the NFs heat capacity (Cp). To this end, a wide variety of NFs from the literature was processed using Principal Component Analysis (PCA) and Response Surface Methodology (RSM). Finally, a database with Ansys Granta Constructor 2021 software was created and an analysis with Ansys Granta Selector 2021 was performed. As a result, the key parameters that impact the Cp of several nanofluids are obtained as well as: their high-temperature dependence, the nature of the liquid medium, and the type of nanoparticles. In addition, the results allow to identify and design nanofluids with specific properties for specific working conditions.