Flow control in wings and discovery of novel approaches via deep reinforcement learning

R Vinuesa, O Lehmkuhl, A Lozano-Durán, J Rabault - Fluids, 2022 - mdpi.com
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 …

An overview of hybrid RANS–LES models developed for industrial CFD

F Menter, A Hüppe, A Matyushenko, D Kolmogorov - Applied Sciences, 2021 - mdpi.com
An overview of scale-resolving simulation (SRS) methods used in ANSYS Computational
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 …

[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)] …

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 …

Wall model based on neural networks for LES of turbulent flows over periodic hills

Z Zhou, G He, X Yang - Physical Review Fluids, 2021 - APS
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 …

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 …

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 …

Data-driven wall modeling for turbulent separated flows

D Dupuy, N Odier, C Lapeyre - Journal of Computational Physics, 2023 - Elsevier
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 …

[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 …