Improved Solar Thermal Applications
Advanced Research: University of South Carolina Aiken
Exploring Thermophysical Properties of Ionic Liquids (ILs) based Nanofluids using Machine Learning Techniques for Solar Thermal Applications
Ionic liquids (ILs) are organic salts that are liquid at room temperature and consist of organic cations and organic or inorganic anions. ILs are of great interest for thermal storage, dye-sensitized solar cells, heat transfer fluids (HTFs), advanced battery technologies, and lubricants because of their suitable thermophysical properties. These properties include negligible vapor pressure and volatility, high thermal stability, low melting point, high ionic conductivity, and high solvating capability. We have evidence showing that the heat transfer efficiency of ILs increases when embedded with nanoparticles (ILs-based nanofluids) even at high temperatures. ILs based nanofluids are considered one of the potential HTFs for concentrated solar power (CSP) applications. However, the thermophysical properties in the current literature are scattered based on different types of base fluids and nanoparticles. The proposed research aims to develop a robust model using a machine learning approach to predict the thermophysical properties of ILs based nanofluids and validate the model with experimentally measured data. This will be the first attempt to use a machine learning approach to predict the thermophysical properties of ILs based nanofluids. A novel Gaussian process regression (GPR) model will be utilized for viscosity and thermal conductivity prediction. The proposed model will help to predict the optimized thermophysical properties which will eventually reduce the experimental cost for the design of advanced HTFs for next-generation solar thermal applications. The research will be integrated with high-impact practice through undergraduate research where undergraduate students will be directly involved in the proposed project.
