Atomically precise metal nanoclusters (MNCs) represent a fascinating class of ultrasmall
nanoparticles with molecule-like properties, bridging conventional metal–ligand complexes …

An accurate and fast prediction of particle-laden flow fields is of particular relevance for a
wide variety of industrial applications. The motivation for this research is to evaluate the …

Micro-scale positioning techniques have become essential in numerous engineering
systems. In the field of fluid mechanics, particle tracking velocimetry (PTV) stands out as a …

Turbulence, the ubiquitous dynamical phenomenon involving random flows and particle
motion, is a most challenging research topic that has received persistent attention in both the …

This study proposes a computational model to define the wind velocity of the environment on
the photovoltaic (PV) module via heat transfer concepts. The effect of the wind velocity and …

Time-marching of turbulent flow fields is computationally expensive using traditional
Computational Fluid Dynamics (CFD) solvers. Machine Learning (ML) techniques can be …

This study presents a novel approach to using a gated recurrent unit (GRU) model, a deep
neural network, to predict turbulent flows in a Lagrangian framework. The emerging velocity …

This study suggests employing a deep learning model trained on on-site wind speed
measurements to enhance predictions for future wind speeds. The model uses a gated …

The transport of passive particles in turbulent flow can be studied with a combination of
Direct Numerical Simulation (DNS) and Lagrangian scalar tracking (LST). While such …