Data-enabled prediction of streak breakdown in pressure-gradient boundary layers

Streaks in pre-transitional boundary layers are analysed and their properties are extracted from direct numerical simulation data. Streaks that induce breakdown to turbulence via secondary instability are shown to differ from the remainder of the population in various attributes. Conditionally avera...

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Veröffentlicht in:Journal of fluid mechanics Jg. 801; S. 43 - 64
Hauptverfasser: Hack, M. J. Philipp, Zaki, Tamer A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cambridge, UK Cambridge University Press 25.08.2016
Schlagworte:
Amplitude
Artificial neural networks
Boundary layer
Boundary layer transition
Boundary layers
Breakdown
Computational fluid dynamics
Computer applications
Computer simulation
Direct numerical simulation
Fluids
Information storage
Instability
Mathematical models
Mechanical engineering
Momentum
Neural networks
Pressure
Pressure gradients
Probability density functions
Probability theory
Properties
Simulation
Spots
Stability
Studies
Transportation networks
Turbulence
Velocity
Vortices
boundary layer stability
transition to turbulence
instability
ISSN:0022-1120, 1469-7645
Online-Zugang:Volltext
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Zusammenfassung:Streaks in pre-transitional boundary layers are analysed and their properties are extracted from direct numerical simulation data. Streaks that induce breakdown to turbulence via secondary instability are shown to differ from the remainder of the population in various attributes. Conditionally averaged flow fields establish that they are situated farther away from the wall, and have a larger cross-sectional area and higher peak amplitude. The analysis also shows that the momentum thickness acts as a similarity parameter for the properties of the streaks. Probability density functions of the streak amplitude, area, and shear along the streaks, collapse among the various pressure gradients when plotted as a function of the momentum thickness. A prediction scheme for laminar–turbulent transition based on artificial neural networks is presented, which can identify the streaks that will eventually induce the formation of turbulent spots. In comparison to linear stability theory, the approach achieves a higher prediction accuracy at considerably lower computational cost.
Bibliographie:ObjectType-Article-1
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ISSN:0022-1120
1469-7645
DOI:10.1017/jfm.2016.441