Analysis of unsteady behaviour in shockwave turbulent boundary layer interaction

By M. Grilli, Peter J. Schmid, Stefan Hickel, Nikolaus A. Adams. Journal of Fluid Mechanics, 2012

227 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computational Mechanics

View Paper: https://www.semanticscholar.org/paper/2b3000f85f4e8f2b47f1243decd98bd216f660fd

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In Aerospace Engineering

In Computational Mechanics

Citations in the corpus

Confinement effects in shock wave/turbulent boundary layer interactions through wall-modelled large-eddy simulations (2014, methodology): “The dynamic mode decomposition (DMD) performed by Grilli et al. (2012) also discarded the upstream coherent structure of the incoming boundary layer as the relevant cause in the observed low-frequency dynamics of their simulations.”
Generalizing Koopman Theory to Allow for Inputs and Control (2016, background): “In a very short period of time since, DMD theory has been applied with great success to a broad set of domain sciences including complex fluid flows [45, 43, 44, 13, 2, 48, 47], foreground/background separation in video streams [14], epidemiology [37], and neuroscience [3].”
Parallel data-driven decomposition algorithm for large-scale datasets: with application to transitional boundary layers (2016, methodology): “This methodology has recently been used in investigations of complex flows to either provide an explanation of the essential behavior of the system, or to generate reduced-order models based on a few dominant modes that optimally capture the full dynamics [4,9,17,19, 20].”
Data-driven discovery of Koopman eigenfunctions for control (2017, methodology): “at approximates the Koopman operator with a best-t linear model that advances measurements from one time step to the next [97,101,115,69]. DMD was originally introduced in the uid dynamics community [101,102,44,8,81,21] to decompose 8 E. KAISER, J. N. KUTZ, AND S. L. BRUNTON large data sets into dom”
Model Reduction for Flow Analysis and Control (2017, background): “…water jet experiment Seena & Sung (2011) Turbulent cavity flow (DNS) Duke et al. (2012a) Annular liquid sheet instabilities (experiment) Grilli et al. (2012) Shockwave turbulent boundary layer interaction (DNS) Jardin & Bury (2012) Flow past a cylinder, with forcing near the mean…”
Low-frequency dynamics in a shock-induced separated flow (2016, methodology): “In the context of STBLIs, the DMD method has previously been applied to LES data of a reflected shock interaction (Pirozzoli et al. 2010) and a compression-ramp interaction (Grilli et al. 2012).”
Unsteady effects of strong shock-wave/boundary-layer interaction at high Reynolds number (2017, methodology): “Our analyses support a mechanism proposed by Touber & Sandham (2011) and Grilli et al. (2012), where the low-frequency unsteadiness is an intrinsic property of the interaction.”
Dynamic Mode Decomposition with Control (2014, background): “DMD has already been applied to a significant number of applications in the fluid dynamics community [33], [35], [14], [2], [39], [37] and is expanding to a variety of other applications like background subtraction in video processing [15].”
On dynamic mode decomposition: Theory and applications (2013, background): “For instance, DMD has been applied in the study of the wake behind a flexible membrane [8], the flow around high-speed trains [9], instabilities in annular liquid sheets [10], shockwave-turbulent boundary layer interactions [11], detonation waves [12], cavity flows [8,13], and various jets [3,8,14,1”