In this article, the ability of GMDH artificial neural networks (ANNs) to predict the rheological
behavior (RB) of nanofluids (NFs) containing CuO NPs is studied. ANNs are a powerful …

For conducting an analysis of the experimental data, it is imperative to establish a
mathematical correlation between the input and output variables. This entails executing a …

One way to enhance the thermal conduction process in heating systems is to attach
substances with high thermal conductivity (TC) to the base fluids. The interesting properties …

This study predicts the parameters such as viscosity and thermal conductivity in silica-
alumina-MWCN/water nanofluid using the artificial intelligence method and using design …

This paper investigates the impact of Solid Volume Fraction (SVF) and temperature on the
dynamic viscosity of a hybrid antifreeze composed of MWCNTs and aluminum oxide in a …

In this study, a new porous nanocomposite adsorbent for water conservancy was
synthesized using the freeze-drying technique to adsorb a cationic dye (Methylene Blue) in …

The optimization and prediction of SAE40 based nanofluid (NF) viscosity with a knowledge
management approach have been discussed using Response surface methodology (RSM) …

This study investigates the impact of incorporating phase change materials (PCMs), nano-
particles (NPs) enhanced PCMs (nePCMs), and porous foam gradients on the thermal …

Water quality indicators (WQIs), such as chlorophyll-a (Chl-a) and dissolved oxygen (DO),
are crucial for understanding and assessing the health of aquatic ecosystems. Precise …

Thermophysical properties such as thermal conductivity (k nf) make the use of fluid suitable
for heat transfer. Fluids such as water have limited applications due to their low thermal …