Experimental 3D Analysis of the Large Scale Behaviour of a Plane Turbulent Mixing Layer

Druault, Philippe; Delville, J.; Bonnet, Jean-Paul

doi:10.1007/s10494-005-4136-0

Cited by 26 publications

(9 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using this property, POD has been also intensively used to formulate a low order dynamical system (Holmes et al, 1996). Furthermore, POD procedure can be also used as an advanced mathematical tool for the reconstruction of missing experimental data, not only in the space domain (Druault and Delville, 2000;Druault et al, 2005a;Druault and Chaillou, 2007) but also in the temporal domain (Druault et al, 2005b). After recalling the mathematical concept of the POD, its application from 2D PIV database is presented.…”

Section: Proper Orthogonal Decomposition (Pod) Mathematical Toolmentioning

confidence: 99%

Time-Resolved PIV investigations of the flow field around cod-end net structures

2011

Self Cite

View full text Add to dashboard Cite

a b s t r a c tFlow field measurements past a fixed rigid cod-end structure and past a porous fishing net structure are conducted using Time-Resolved PIV method. The rigid cod-end is first used to characterize finely the wake flow. Proper Orthogonal Decomposition (POD) is then applied in order to extract the large scale energetic vortices of the flow from the measured velocity field. It is then observed that the first POD modes are associated to the Karman's type flow structure of vortex shedding. It is shown that the characteristics of the wake flow behind the rigid cod-end flow configuration compare quite well with previous ones obtained from bluff cylinder or sphere wake analyzes. Second, PIV measurements are performed around a non-rigid bottom trawl which is free to move. Preliminary analyses show that the frequencies associated with the oscillatory motion of the porous structure are the same as the ones detected in the near wake flow demonstrating the lock-in regime. It is then expected that these preliminary results provide some interesting informations about future investigations on the drag force acting on fishing net structure.

show abstract

Section: Proper Orthogonal Decomposition (Pod) Mathematical Toolmentioning

confidence: 99%

Time-Resolved PIV investigations of the flow field around cod-end net structures

2011

Self Cite

View full text Add to dashboard Cite

show abstract

“…The latter employed an unconditional estimate of the raw velocity field in a two-dimensional subsonic mixing layer via linear stochastic estimation (LSE), followed by a low-dimensional description of the model estimate via POD. Since then, the joint implementation of these analysis tools (POD and LSE) have taken many different forms in the experimental community using a variety of different flows (backward facing ramps (Taylor & Glauser 2004), axisymmetric sudden expansions (Tinney et al 2002), axisymmetric backward facing steps (Hudy, Naguib & Humphreys 2007) plane mixing layers (Druault, Delville & Bonnet 2005), cavity flows (Murray & Ukeiley 2006), axisymmetric jet flows (Ewing & Citriniti 1999;Picard & Delville 2000;Taylor, Ukeiley & Glauser 2001;Tinney et al 2005Tinney et al , 2006Iqbal & Thomas 2007), pressure fields in the periphery of axisymmetric jets (Coiffet et al 2004), wall jets (Hall & Ewing 2006) and incipiently separated airfoils (Glauser et al 2004;Pinier et al 2007)).…”

Section: Introductionmentioning

confidence: 99%

Low-dimensional characteristics of a transonic jet. Part 2. Estimate and far-field prediction

2008

View full text Add to dashboard Cite

Complementary low-dimensional techniques are modified to estimate the most energetic turbulent features of a Mach 0.85 axisymmetric jet in the flow's near-field regions via spectral linear stochastic estimation. This model estimate is three-dimensional, comprises all three components of the velocity field and is time resolved. The technique employs the pressure field as the unconditional input, measured within the hydrodynamic periphery of the jet flow where signatures (pressure) are known to comprise a reasonable footprint of the turbulent large-scale structure. Spectral estimation coefficients are derived from the joint second-order statistics between coefficients that are representative of the low-order pressure field (Fourier-azimuthal decomposition) and of the low-order velocity field (proper orthogonal decomposition). A bursting-like event is observed in the low-dimensional estimate and is similar to what was found in the low-speed jet studies of others. A number of low-dimensional estimates are created using different velocity–pressure mode combinations from which predictions of the far-field acoustics are invoked using Lighthill's analogy. The overall sound pressure level (OASPL) directivity is determined from the far-field prediction, which comprises qualitatively similar trends when compared to direct measurements at r/D=75. Retarded time topologies of the predicted field at 90° and 30° are also shown to manifest, respectively, high- and low-frequency wave-like motions when using a combination of only the low-order velocity modes (m=0, 1, 2). This work thus constitutes a first step in developing low-dimensional and dynamical system models from hydrodynamic pressure signatures for estimating and predicting the behaviour of the energy-containing events that govern many of the physical constituents of turbulent flows.

show abstract

“…Several structure eduction methodologies based on different flow structure concepts are now well established. For instance, the decomposition of experimental or numerical turbulent flow data can be performed from phase averaging methods or turbulence filterings [2,3], wavelet decomposition [4], stochastic estimation [5,6], proper orthogonal decomposition (POD) [7], etc. But these methods are generally used to characterize only the coherent part of the flow field.…”

Section: Introductionmentioning

confidence: 99%

Proper orthogonal decomposition of in-cylinder engine flow into mean component, coherent structures and random Gaussian fluctuations

Roudnitzky

Druault

Guibert

2006

Journal of Turbulence

Self Cite

View full text Add to dashboard Cite

A snapshot proper orthogonal decomposition (POD) is performed from 2D time-resolved PIV measurements obtained in the tumble plane of a Spark Ignition engine flow. Based on this filtering approach, the in-cylinder flow field is decomposed into a mean part, a coherent part and a turbulent incoherent part. The analysis of the one-point statistical moments of orders 3 and 4 (skewness and flatness coefficients) as well as the analysis of the probability density function of the velocity field show that the POD extracts an incoherent random velocity field having Gaussian distribution properties. These small amplitude background fluctuations are also homogeneous. We then demonstrate the ability of the POD procedure, based on an energy criterion, in separating the coherent part and random Gaussian fluctuations. Based on this POD flow partitioning, providing a new triple decomposition of the instantaneous turbulent flow, the flow engine cycle-to-cycle variations associated with each velocity field contribution are then accessed. Thus, one of the first full field quantitative analyses of the cyclic variabilities associated with each part of the in-cylinder flow field is provided from the analysis of the corresponding POD temporal coefficients.

show abstract

Experimental 3D Analysis of the Large Scale Behaviour of a Plane Turbulent Mixing Layer

Cited by 26 publications

References 36 publications

Time-Resolved PIV investigations of the flow field around cod-end net structures

Time-Resolved PIV investigations of the flow field around cod-end net structures

Low-dimensional characteristics of a transonic jet. Part 2. Estimate and far-field prediction

Proper orthogonal decomposition of in-cylinder engine flow into mean component, coherent structures and random Gaussian fluctuations

Contact Info

Product

Resources

About