The gas dynamics of single-phase nonreacting fluids whose thermodynamic states are close
to vapor-liquid saturation, close to the vapor-liquid critical point, or in supercritical conditions …

Machine learning has seen increasing implementation as a predictive tool in the chemical
and physical sciences in recent years. It offers a route to accelerate the process of scientific …

The computational fluid dynamics (CFD) method is computationally intensive and costly, and
evaluating aerodynamic performance through CFD is time-consuming and labor-intensive …

Moisture diffusion is a common phenomenon in geotechnical engineering, and its induced
deformation seriously affects the stability of the engineering structure, such as embankment …

This study introduces a new physics-informed neural networks (PINN)-based prediction
method to determine the temperature pattern of fluid and fins when flow passes over plate …

Traditional Riemann solvers fall into two broad categories: exact solvers, which require
multiple iterations to achieve high accuracy, and approximate linearized solvers, which …

Carbon dioxide presents many unique advantages for cooling and power cycles under
supercritical or near-critical conditions, where the characterization of thermophysical …

Sedimentation directly affects the thermal performance and efficiency of thermal systems
such as boilers, heat exchangers, and solar collectors. This work investigates the effect of …

Physics-informed neural networks (PINNs) provide a new approach to solving partial
differential equations (PDEs), while the properties of coupled physical laws present potential …

A predictive method using physics-informed machine learning (PIML) and large eddy
simulation (LES) is developed to capture the transient flow field through microscale porous …