169 citations · 686 across the 19 of their papers we have counts for
6 papers · 1 filter
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-…