Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Bae, H. Jane"'
This work introduces a formulation of resolvent analysis that uses wavelet transforms rather than Fourier transforms in time. Under this formulation, resolvent analysis may extend to turbulent flows with non-stationary mean states; the optimal resolv
Externí odkaz:
http://arxiv.org/abs/2404.06600
Resolvent analysis provides a framework to predict coherent spatio-temporal structures of largest linear energy amplification, through a singular value decomposition (SVD) of the resolvent operator, obtained by linearizing the Navier-Stokes equations
Externí odkaz:
http://arxiv.org/abs/2404.06331
Autor:
Lopez-Doriga, Barbara, Atzori, Marco, Vinuesa, Ricardo, Bae, H. Jane, Srivastava, Ankit, Dawson, Scott T. M.
This research focuses on the identification and causality analysis of coherent structures that arise in turbulent flows in square and rectangular ducts. Coherent structures are first identified from direct numerical simulation data via proper orthogo
Externí odkaz:
http://arxiv.org/abs/2401.06295
In this work, we study the transient growth of the principal resolvent modes in the minimal flow unit using a reformulation of resolvent analysis in a time-localized wavelet basis. We target the most energetic spatial wavenumbers for the minimal flow
Externí odkaz:
http://arxiv.org/abs/2312.15465
Autor:
Zhou, Di, Bae, H. Jane
In this study, we conduct a parametric analysis to evaluate the sensitivities of wall-modeled large-eddy simulation (LES) with respect to subgrid-scale (SGS) models, mesh resolution, wall boundary conditions and mesh anisotropy. While such investigat
Externí odkaz:
http://arxiv.org/abs/2309.13555
Autor:
Zhou, Di, Bae, H. Jane
We propose a framework for developing wall models for large-eddy simulation that is able to capture pressure-gradient effects using multi-agent reinforcement learning. Within this framework, the distributed reinforcement learning agents receive off-w
Externí odkaz:
http://arxiv.org/abs/2305.02540
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 high-dimensional problems, especially in domains such as games. Despite i
Externí odkaz:
http://arxiv.org/abs/2302.14391
Publikováno v:
Physical Review Fluids 8, 064612 (2023)
We develop a framework for efficient streaming reconstructions of turbulent velocity fluctuations from limited sensor measurements with the goal of enabling real-time applications. The reconstruction process is simplified by computing linear estimato
Externí odkaz:
http://arxiv.org/abs/2301.06734
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 principal component analysis (PCA) algorithm, which formulates the as
Externí odkaz:
http://arxiv.org/abs/2212.02741
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 turbulent flows with non-stationary means in addition to statistically-stat
Externí odkaz:
http://arxiv.org/abs/2212.02660