Flow control in wings and discovery of novel approaches via deep reinforcement learning
In this review, we summarize existing trends of flow control used to improve the aerodynamic
efficiency of wings. We first discuss active methods to control turbulence, starting with flat …
efficiency of wings. We first discuss active methods to control turbulence, starting with flat …
An overview of hybrid RANS–LES models developed for industrial CFD
An overview of scale-resolving simulation (SRS) methods used in ANSYS Computational
Fluid Dynamics (CFD) software is provided. The main challenges, especially when …
Fluid Dynamics (CFD) software is provided. The main challenges, especially when …
Scientific multi-agent reinforcement learning for wall-models of turbulent flows
HJ Bae, P Koumoutsakos - Nature Communications, 2022 - nature.com
The predictive capabilities of turbulent flow simulations, critical for aerodynamic design and
weather prediction, hinge on the choice of turbulence models. The abundance of data from …
weather prediction, hinge on the choice of turbulence models. The abundance of data from …
[HTML][HTML] Grid-point and time-step requirements for direct numerical simulation and large-eddy simulation
XIA Yang, KP Griffin - Physics of Fluids, 2021 - pubs.aip.org
We revisit the grid-point requirement estimates in Choi and Moin [“Grid-point requirements
for large eddy simulation: Chapman's estimates revisited,” Phys. Fluids 24, 011702 (2012)] …
for large eddy simulation: Chapman's estimates revisited,” Phys. Fluids 24, 011702 (2012)] …
Machine learning building-block-flow wall model for large-eddy simulation
A Lozano-Durán, HJ Bae - Journal of Fluid Mechanics, 2023 - cambridge.org
A wall model for large-eddy simulation (LES) is proposed by devising the flow as a
combination of building blocks. The core assumption of the model is that a finite set of simple …
combination of building blocks. The core assumption of the model is that a finite set of simple …
Wall model based on neural networks for LES of turbulent flows over periodic hills
In this work, a data-driven wall model for turbulent flows over periodic hills is developed
using the feedforward neural network (FNN) and data from wall-resolved large-eddy …
using the feedforward neural network (FNN) and data from wall-resolved large-eddy …
Information-theoretic formulation of dynamical systems: causality, modeling, and control
A Lozano-Durán, G Arranz - Physical Review Research, 2022 - APS
The problems of causality, modeling, and control for chaotic, high-dimensional dynamical
systems are formulated in the language of information theory. The central quantity of interest …
systems are formulated in the language of information theory. The central quantity of interest …
Conditioning and accurate solutions of Reynolds average Navier–Stokes equations with data-driven turbulence closures
BP Brener, MA Cruz, RL Thompson… - Journal of Fluid …, 2021 - cambridge.org
The possible ill conditioning of the Reynolds average Navier–Stokes (RANS) equations
when an explicit data-driven Reynolds stress tensor closure is employed is a discussion of …
when an explicit data-driven Reynolds stress tensor closure is employed is a discussion of …
Data-driven wall modeling for turbulent separated flows
The large-eddy simulation of wall-bounded turbulent flows at high Reynolds numbers is
made more efficient by the use of wall models that predict the wall shear stress, allowing …
made more efficient by the use of wall models that predict the wall shear stress, allowing …
[HTML][HTML] Directly irradiated liquid metal film in an ultra-high temperature solar cavity receiver. Part 2: Coupled CFD and radiation analysis
TI Abdelsalam, Z Tian, A Robinson - Solar Energy, 2023 - Elsevier
A novel solar cavity receiver was proposed in Part 1 to facilitate operation at ultra-high
temperatures (> 1300 K). The concept featured enclosing a directly irradiated liquid metal …
temperatures (> 1300 K). The concept featured enclosing a directly irradiated liquid metal …