Turbulent flow is a complex and vital phenomenon in fluid dynamics, as it is the most
common type of flow in both natural and artificial systems. Traditional methods of studying …

The deep operator network (DeepONet) structure has shown great potential in
approximating complex solution operators with low generalization errors. Recently, a …

This study aimed to simulate straining turbulent flow empirically, having direct similarities
with vast naturally occurring flows and engineering applications. The flow was generated in …

Since the stagnation point is subject to straining motion, this 3D experiment is an effort to
simulate the stagnation plane, which applies to studying the particle erosion in rotary …

This study aimed to apply empirical data to assess wind energy production at the Búrfell site
in Iceland based on the E44 turbine model. The empirical data are 5 years of recordings at …

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 …

The current work proposes a method for the active control of flow over a National Advisory
Committee of Aeronautics 0012 airfoil under turbulent condition based on time-resolved …

Aiming at the incomplete data and difficult prediction in the prediction of firewater system
water level, a data filling method is proposed based on the reinforcement learning approach …

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 …