169 citations · 686 across the 19 of their papers we have counts for
19 papers
Log-law recovery through reinforcement-learning wall model for large-eddy simulation
Aurélien Vadrot, Xiang I. A. Yang, H. Jane Bae +1
This paper focuses on the use of reinforcement learning (RL) as a machine-learning (ML) modeling tool for near-wall turbulence. RL has demonstrated its effectiveness in solving hig…
Towards real-time reconstruction of velocity fluctuations in turbulent channel flow
Rahul Arun, H. Jane Bae, Beverley J. McKeon
We develop a framework for efficient streaming reconstructions of turbulent velocity fluctuations from limited sensor measurements with the goal of enabling real-time applications.…
A sparsity-promoting resolvent analysis for the identification of spatiotemporally-localized amplification mechanisms
Barbara Lopez-Doriga, Eric Ballouz, H. Jane Bae +1
This work introduces a variant of resolvent analysis that identifies forcing and response modes that are sparse in both space and time. This is achieved through the use of a sparse…
Wavelet-based resolvent analysis for statistically-stationary and temporally-evolving flows
Eric Ballouz, Barbara Lopez-Doriga, Scott T. M. Dawson +1
This work introduces a formulation of resolvent analysis that uses wavelet transforms rather than Fourier transforms in time. This allows resolvent analysis to be extended to turbu…
Multi-agent reinforcement learning for wall modeling in LES of flow over periodic hills
Di Zhou, Michael P. Whitmore, Kevin P. Griffin +1
We develop a wall model for large-eddy simulation (LES) that takes into account various pressure-gradient effects using multi-agent reinforcement learning (MARL). The model is trai…
Investigating nonlinearity in wall turbulence: regenerative versus parametric mechanisms
B. F. Farrell, E. Kim, H. J. Bae +2
Both linear growth processes associated with non-normality of the mean flow and nonlinear interaction transferring energy among fluctuations contribute to maintaining turbulence. H…
Machine learning building-block-flow wall model for large-eddy simulation
Adrián Lozano-Durán, H. Jane Bae
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 c…
Numerical and modeling error assessment of large-eddy simulation using direct-numerical-simulation-aided large-eddy simulation
H. Jane Bae, Adrian Lozano-Duran
We study the numerical errors of large-eddy simulation (LES) in isotropic and wall-bounded turbulence. A direct-numerical-simulation (DNS)-aided LES formulation, where the subgrid-…
A local dynamic gradient Smagorinsky model for large-eddy simulation
Wybe Rozema, H. Jane Bae, Roel W. C. P. Verstappen
This paper proposes a local dynamic model for large-eddy simulation (LES) without averaging in homogeneous directions. It is demonstrated that the widely-used dynamic Smagorinsky m…
Scientific multi-agent reinforcement learning for wall-models of turbulent flows
H. Jane Bae, Petros Koumoutsakos
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 f…
Life-cycle of streaks in the buffer layer of wall-bounded turbulence
H. Jane Bae, Myoungkyu Lee
Streaks in the buffer layer of wall-bounded turbulence are tracked in time to study their life-cycle. Spatially and temporally resolved direct numerical simulation data is used to…
Resolvent analysis of stratification effects on wall-bounded shear flows
M. A. Ahmed, H. J. Bae, A. F. Thompson +1
The interaction between shear driven turbulence and stratification is a key process in a wide array of geophysical flows with spatio-temporal scales that span many orders of magnit…
Self-critical machine-learning wall-modeled LES for external aerodynamics
Adrián Lozano-Durán, Hyunji Jane Bae
The prediction of aircraft aerodynamic quantities of interest remains among the most pressing challenges for computational fluid dynamics. The aircraft aerodynamics are inherently…
Nonlinear mechanism of the self-sustaining process in the buffer and logarithmic layer of wall-bounded flows
Hyunji Jane Bae, Adrian Lozano-Duran, Beverley J. McKeon
The nonlinear mechanism in the self-sustaining process (SSP) of wall-bounded turbulence is investigated. Resolvent analysis is used to identify the principal forcing mode which pro…
DNS-aided explicitly filtered LES
H. Jane Bae, Adrián Lozano-Durán
The equations for LES are formally derived by low-pass filtering the NS equations with the effect of the small scales on the larger ones captured by a SGS model. However, it is kno…
Causality of energy-containing eddies in wall turbulence
Adrián Lozano-Durán, H. Jane Bae, Miguel P. Encinar
Turbulent flows in the presence of walls may be apprehended as a collection of momentum- and energy-containing eddies (energy-eddies), whose sizes differ by many orders of magnitud…
Characteristic scales of Townsend's wall attached eddies
Adrián Lozano-Durán, Hyunji Jane Bae
Townsend (1976) proposed a structural model for the logarithmic layer (log-layer) of wall turbulence at high Reynolds numbers, where the dominant momentum-carrying motions are orga…
Dynamic slip wall model for large-eddy simulation
H. Jane Bae, Adrián Lozano-Durán, Sanjeeb T. Bose +1
Wall modelling in large-eddy simulation (LES) is necessary to overcome the prohibitive near-wall resolution requirements in high-Reynolds-number turbulent flows. Most existing wall…
Error scaling of large-eddy simulation in the outer region of wall-bounded turbulence
Adrián Lozano-Durán, Hyunji Jane Bae
We study the error scaling properties of large-eddy simulation (LES) in the outer region of wall-bounded turbulence at moderately high Reynolds numbers. In order to avoid the addit…