Prediction and Optimization of Thermal Conductivity and Viscosity of Stable Plasmonic TiN Nanofluid Using Response Surface Method For Solar Thermal Application

Abstract

Abstract Nanofluids open a new dimension in solar thermal applications due to their enormous thermophysical properties. The preparation of stable, efficient, and low-cost nanofluids is an emerging area of research. According to NIMS (National Institute of Material Science) research, Titanium nitride (TiN) nanoparticles have localized surface plasmon resonance properties. It enables a superior photoabsorption feature. Titanium nitride (TiN) particles of 40–50 nm sizes were selected to prepare distilled water-based nanofluid at a 0-0.1% volume concentration range. The Thermal conductivity and viscosity of TiN nanofluids and base fluid are measured experimentally at temperatures 30℃ to 55℃. Determination of thermal conductivity and viscosity of nanofluid through experimentation is cumbersome. The present study deals with thermal conductivity and viscosity modeling of water-based stable plasmonic TiN nanofluid using the surface response method. ANOVA is used to determine the significance of input variables and their interaction. The performance of both predictive models was measured in terms of correlation coefficient (R2) and mean square error (MSE) to acknowledge the best fit. The surface response method optimizes process parameters using reliable and efficient model results for maximum heat transfer enhancement. The maximum thermal conductivity (0.8848 W/mK) and minimum viscosity (0.7822 cP) obtained at 55℃ and 0.0535% volume concentration.