[HTML][HTML] Deep reinforcement learning for fluid mechanics: Control, optimization, and automation
I Kim, Y Jeon, J Chae, D You - Fluids, 2024 - mdpi.com
A comprehensive review of recent advancements in applying deep reinforcement learning
(DRL) to fluid dynamics problems is presented. Applications in flow control and shape …
(DRL) to fluid dynamics problems is presented. Applications in flow control and shape …
Turbulence closure modeling with machine learning: A foundational physics perspective
SS Girimaji - New Journal of Physics, 2024 - iopscience.iop.org
Turbulence closure modeling using (ML) is at an early crossroads. The extraordinary
success of ML in a variety of challenging fields had given rise to an expectation of similar …
success of ML in a variety of challenging fields had given rise to an expectation of similar …
Building-block-flow computational model for large-eddy simulation of external aerodynamic applications
Computational fluid dynamics is an essential tool for accelerating the discovery and
adoption of transformative designs across multiple engineering disciplines. Despite its many …
adoption of transformative designs across multiple engineering disciplines. Despite its many …
A recursive neural-network-based subgrid-scale model for large eddy simulation: application to homogeneous isotropic turbulence
We introduce a novel recursive procedure to a neural-network-based subgrid-scale (NN-
based SGS) model for large eddy simulation (LES) of high-Reynolds-number turbulent flow …
based SGS) model for large eddy simulation (LES) of high-Reynolds-number turbulent flow …
Pedestrian-level low-occurrence wind speeds in an urban area predicted by artificial neural networks from fundamental statistics
Promoting urban sustainability requires a comprehensive understanding of the interaction
between built and surrounding environments. Low-occurrence wind is a key factor affecting …
between built and surrounding environments. Low-occurrence wind is a key factor affecting …
Scale-resolving simulations of turbulent flows with coherent structures: Toward cut-off dependent data-driven closure modeling
Complex turbulent flows with large-scale instabilities and coherent structures pose
challenges to both traditional and data-driven Reynolds-averaged Navier–Stokes methods …
challenges to both traditional and data-driven Reynolds-averaged Navier–Stokes methods …
An artificial neural network model for recovering small-scale velocity in large-eddy simulation of isotropic turbulent flows
J Tan, G Jin - Physics of Fluids, 2024 - pubs.aip.org
Small-scale motions in turbulent flows play a significant role in various small-scale
processes, such as particle relative dispersion and collision, bubble or droplet deformation …
processes, such as particle relative dispersion and collision, bubble or droplet deformation …
Modeling of vibrational nonequilibrium effects on pressure Hessian tensor using physics-assisted deep neural networks
D Shikha, S Srivastava, SS Sinha - Physics of Fluids, 2024 - pubs.aip.org
This study focuses on modeling the effect of vibrational nonequilibrium on the pressure
Hessian tensor. The pressure Hessian tensor is one of the unclosed processes involved in …
Hessian tensor. The pressure Hessian tensor is one of the unclosed processes involved in …
[PDF][PDF] Building-block flow model for large-eddy simulation
G Arranza, Y Linga, S Costaa, K Gocb… - researchgate.net
We introduce a closure model for wall-modeled large-eddy simulation (WMLES), referred to
as the Buildingblock Flow Model (BFM). The foundation of the model rests on the premise …
as the Buildingblock Flow Model (BFM). The foundation of the model rests on the premise …
[引用][C] Simulating hydro dynamical turbulence and dynamo flows in planetary fluid layers
T GUERRY - 2024