Recent research published in ‘Digital Chemical Engineering’ has unveiled significant advancements in the understanding and application of nanofluids, which are fluids enhanced with nanoparticles to improve their thermal and viscous properties. This study, led by Nik Eirdhina Binti Nik Salimi from Mazda Malaysia Sdn. Bhd. and the Department of Chemical Engineering at Universiti Teknologi PETRONAS, focuses on the predictions of viscosity for various non-Newtonian nanofluids, specifically those based on water and ethylene glycol (EG).
Nanofluids have garnered attention for their potential to enhance heat transfer in a range of applications, including heating and cooling systems, electronics, and transportation. As industries increasingly seek sustainable manufacturing solutions, the ability to optimize the flow characteristics of these fluids becomes crucial. The research utilized four different experimental datasets to predict the effective viscosity of various nanofluid combinations, including Fe3O4-Ag/EG and MWCNT-SiO2/EG-water.
The study employed artificial neural networks (ANN) alongside traditional correlation methods to analyze the relationship between key input parameters—particle concentrations, temperatures, and shear rates—and the output parameter of viscosity. The results were impressive, with the ANN model achieving a coefficient of determination (R2) greater than 0.99 across all datasets. In contrast, traditional correlation methods yielded much lower accuracy, with one dataset showing an R2 value as low as 0.72.
Salimi noted, “The precision of the ANN model in predicting the viscosity of nanofluids can significantly enhance their application in thermal systems.” This enhanced predictive capability not only aids in the design of more efficient thermal management systems but also opens up commercial opportunities in sectors where heat transfer efficiency is critical.
For industries such as automotive, electronics, and HVAC (heating, ventilation, and air conditioning), the implementation of optimized nanofluids could lead to substantial energy savings and improved performance. As businesses strive towards more sustainable practices, the insights from this research can drive innovations that align with environmental goals while enhancing operational efficiency.
In summary, the findings from this research present a promising avenue for improving thermal applications through the use of nanofluids, leveraging advanced modeling techniques like ANN to foster better performance in various industrial sectors.
