| Abstrakt: |
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] |
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