Heat exchanger modeling has been widely employed in recent years for performance
calculation, design optimizations, real-time simulations for control analysis, as well as …

Abstract Machine learning's prowess in extracting insights from data has significantly
advanced fluid rheological behavior prediction. This machine-learning-based approach …

In this study, the thermal conductivity (k nf) of ZnO-TiO 2 (50%–50%)/Ethylene Glycol hybrid
nanofluid using Artificial Neural Networks (ANNs) was predicted. The nanofluid was …

Transcritical organic Rankine cycle is a great promising technology in the field of energy
saving and environment protection. Using trans-1, 3, 3, 3-tetrafluoropropene (R1234ze (E)) …

This study presents a simple correlation for describing the temperature and pressure
dependence of the liquid dynamic viscosity of low GWP refrigerants, namely …

Accurate prediction models for the viscosity and thermal conductivity of refrigerants are of
great importance and have drawn wide attention from scholars. Considering the great …

Fast and accurate prediction and correlation of thermophysical properties are always
important concerns for researchers and engineers. This work is the extension of our earlier …

Liquid Dialkylesters of adipic acid (adipates) have achieved prominence as alternative
green solvents due to their special properties. To enhance their utilization, accurate …

Accurately forecasting energy consumption is beneficial and pivotal for effectively managing
variable refrigerant flow (VRF) systems. Changes in energy consumption provide an intuitive …

In this paper, an artificial neural network (ANN) has been studied for the viscosity of
MWCNTs-ZnO/water-ethylene glycol (80: 20 vol.%) non-Newtonian nanofluid. To evaluate …