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Nanofluids, characterized by the dispersion of nanoparticles in a base liquid, have attracted significant attention in recent years due to their exceptional thermal properties. Specifically, the specific heat capacity of nanofluids plays a crucial role in the design and optimization of heat transfer systems. Traditional experimental methods for determining the specific heat capacity of nanofluids are often limited in terms of cost, time, and operating condition ranges. To address these limitations, this research focuses on the development of a novel predictive model for estimating the specific heat capacity of nanofluids. This study aims to develop a machine learning regression model called Gradient Boost Regression (GBR) with Grid Search optimization (GSO) for accurately predicting the specific heat capacity of aluminum nitride (Al2N3) nanoparticles that are suspended in both water and an ethylene glycol (EG) solution. The GBR-GSO model capitalizes on the strengths of GBR, which can effectively capture complex relationships, and GSO, a metaheuristic optimization technique inspired by the law of gravity. By integrating these two approaches, we aim to create a robust and accurate predictive model for specific heat capacity in nanofluids. To develop and validate the GBR-GSO model, a diverse dataset based on experimental-specific heat capacity collected from the literature has been designed. The performance of the model has been evaluated by comparing its predictions with experimental data. The GBR-GSO model achieved 99.99% accuracy with the experimental data of specific heat capacity. This research contributes to the advancement of nanofluid-based heat transfer systems by providing an effective tool for predicting the specific heat capacity of nanofluids. The developed model can facilitate the design and optimization of various engineering applications, leading to the development of energy-efficient and sustainable technologies. [ABSTRACT FROM AUTHOR]

The suspension of the nanoparticles in the conventional heat transfer fluids results in the increment of the fluids' thermal conductivity, viscosity, and density of the fluid. These improvements in thermophysical properties have enormous technological benefits. The thermal fluid properties depend upon a host of parameters such as volume fraction of the nanoparticles, temperature, density of fluid base and nanoparticles, nanoparticles size, nanolayer, the thermal conductivity of base fluid and particles, and pH. Studying the properties of the thermal fluids via experiments is a laborious task that can be simplified using predictive modeling. In this study, a machine learning regressive model named Gradient Boost Regression (GBR) is developed to predict the density of the aluminum nitride (Al2N3), silicon nitride (Si3N4), and titanium nitride (TiN) nanoparticles suspended in ethylene glycol (EG) solution. The predictive model developed is used to predict the density of the nanofluid based on four parameters; the size of nanoparticles, the molecular weights of nanoparticles, the volume concentration of the particle, and temperature. The proposed predictive model predicted the density of nanofluids for the training dataset with 99.99 percent accuracy whereas the testing dataset was validated at 99.91% accuracy and a standard deviation of 0.0000783 from the experimental data. To highlight the accuracy of the proposed model, its predictive performance was compared with an existing model formulated by Pak and Cho. The Pak & Cho model has a mean absolute deviation (MAD) of 0.0397 whereas the proposed GBR model has a MAD value of 0.0000783 for the estimated density of the nanofluids. Finally, the developed GBR model was compared with experimental density values at different temperatures and mass concentrations (0–5%). The proposed (GBR) model agrees well with experimental results under the different experimental conditions. [ABSTRACT FROM AUTHOR]