AI Summary & Key Takeaways
AI InsightTherefore, the proposed new equations will be useful for numerical simulation studies and the engineering design of heat transfer devices such as refrigeration systems, solar collectors, and heat exchangers.
📌 3 Key Takeaways
Abstract
A trial-error procedure is applied for the derivation of correlations to estimate the relative thermal conductivity (kr) and dynamic viscosity (µr) of nanofluids using MATLAB. Thermophysical properties of particles and base fluids, particle diameter (dp), sphericity, capping layer thickness, Brownian motion of a particle, temperature, and volume fraction (φ) are considered. The accuracy of predicting kr and µr of nanofluids is developed using dimensionless parameters involving base fluid and particle characteristics. The results reveal that the estimated values are in a good agreement with the experimental data with a standard deviation of 2.16% and 8.16% for kr and µr of nanofluids, respectively. Besides that, 97.5% of the predicted kr values suit experimental data of kr with a mean deviation of ±5%, whereas 90.4% of the estimated µr values match the data of µr with a mean deviation of ±10%. Therefore, the proposed new equations will be useful for numerical simulation studies and the engineering design of heat transfer devices such as refrigeration systems, solar collectors, and heat exchangers.