From bypass transition to flow control and data-driven turbulence modeling: An input-output viewpoint
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Abstract (arXiv)
Transient growth and resolvent analyses are routinely used to assess non-asymptotic properties of fluid flows. In particular, resolvent analysis can be interpreted as a special case of viewing flow dynamics as an open system in which free-stream turbulence, surface roughness, and other irregularities provide sources of input forcing. We offer a comprehensive summary of the tools that can be employed to probe the dynamics of fluctuations around a laminar or turbulent base flow in the presence of such stochastic or deterministic input forcing and describe how input-output techniques enhance resolvent analysis. Specifically, physical insights that may remain hidden in the resolvent analysis are gained by detailed examination of input-output responses between spatially-localized body forces and selected linear combinations of state variables. This differentiating feature plays a key role in quantifying the importance of different mechanisms for bypass transition in wall-bounded shear flows and in explaining how turbulent jets generate noise. We highlight the utility of a stochastic framework, with white or colored inputs, in addressing a variety of open challenges including transition in complex fluids, flow control, and physics-aware data-driven turbulence modeling. Applications with time- or spatially-periodic base flows are discussed and future research directions are outlined.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). First posted on arXiv 23 Mar 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 0; 2025: 0; 2026: 1.
4 papers.
- 2021Modern Koopman Theory for Dynamical Systemscited by 273
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- 2021Transient growth of accelerated optimization algorithmscited by 2
- 2026A neural operator framework for data-driven discovery of stability and receptivity in physical systemscited by 0
- 2026A neural operator framework for data-driven discovery of stability and receptivity in physical systems21 Apr 2026
- 2023How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learning23 Apr 2023
- 2021Transient growth of accelerated optimization algorithms14 Mar 2021
- 2021Modern Koopman Theory for Dynamical Systems24 Feb 2021