A Web services accessible database of turbulent channel flow and its use for testing a new integral wall model for LES

Graham, John H.; Kanov, Kalin; Yang, Xiang; Lee, Myoungkyu; Malaya, Nicholas; Lalescu, Cristian; Burns, Randal; Eyink, Gregory L.; Szalay, Alexander S.; Moser, Robert D.; Meneveau, Charles

doi:10.1080/14685248.2015.1088656

Cited by 157 publications

(191 citation statements)

References 59 publications

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“…The inner layer of the wall-bounded region in the simulation yields minimum values of all components of the Reynolds stress tensor. Profiles of the stress field are seen in the associated documentation [32] with greater statistical convergence.…”

Section: A Turbulence Fieldmentioning

confidence: 99%

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Anisotropic character of low-order turbulent flow descriptions through the proper orthogonal decomposition

2017

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Proper orthogonal decomposition (POD) is applied to distinct data sets in order to characterize the propagation of error arising from basis truncation in the description of turbulence. Experimental data from stereo particle image velocimetry measurements in a wind turbine array and direct numerical simulation data from a fully developed channel flow are used to illustrate dependence of the anisotropy tensor invariants as a function of POD modes used in low-order descriptions. In all cases, ensembles of snapshots illuminate a variety of anisotropic states of turbulence. In the near wake of a model wind turbine, the turbulence field reflects the periodic interaction between the incoming flow and rotor blade. The far wake of the wind turbine is more homogenous, confirmed by the increased magnitude of the anisotropy factor. By contrast, the channel flow exhibits many anisotropic states of turbulence. In the inner layer of the wall-bounded region, one observes onecomponent turbulence at the wall; immediately above, the turbulence is dominated by two components, with the outer layer showing fully three-dimensional turbulence, conforming to theory for wall-bounded turbulence. The complexity of flow descriptions resulting from truncated POD bases can be greatly mitigated by severe basis truncations. However, the current work demonstrates that such simplification necessarily exaggerates the anisotropy of the modeled flow and, in extreme cases, can lead to the loss of three-dimensionality. Application of simple corrections to the low-order descriptions of the Reynolds stress tensor significantly reduces the residual root-mean-square error. Similar error reduction is seen in the anisotropy tensor invariants. Corrections of this form reintroduce three-dimensionality to severe truncations of POD bases. A threshold for truncating the POD basis based on the equivalent anisotropy factor for each measurement set required many more modes than a threshold based on energy. The mode requirement to reach the anisotropy threshold after correction is reduced by a full order of magnitude for all example data sets, ensuring that economical low-dimensional models account for the isotropic quality of the turbulence field.

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Section: A Turbulence Fieldmentioning

confidence: 99%

“…The second set of data comes from DNS (direct numerical simulation) of a fully developed channel flow hosted at Johns Hopkins University (JHU). The reader is referred to the documentation provided by JHU and summarized in Graham et al [32] (see also Refs. [33,34]).…”

Section: Example Datamentioning

confidence: 99%

Anisotropic character of low-order turbulent flow descriptions through the proper orthogonal decomposition

2017

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“…A concept to determine pressure using Taylor's hypothesis approach for 3C volumetric velocity data was developed and validated numerically, using the channel flow database from John's Hopkins University (Li et al, 2008;Perlman et al, 2007;Graham et al, 2013). Independent synthetic 3C volumetric PIV snapshots were created from the available data and pressure was estimated using a Taylor's hypothesis, an Eulerian and a pseudo-Lagrangian approach.…”

Section: Discussionmentioning

confidence: 99%

“…The next step is to assess the performance of the methods on synthetic data, therefore we apply them to the John's Hopkins University channel flow database (Li et al, 2008;Perlman et al, 2007;Graham et al, 2013). Since the ultimate goal of this work is to estimate pressure using experimental data, we used this DNS dataset to simulate synthetic 3D PIV velocity snapshots on which we then implemented the different approaches and assessed their accuracy using the provided DNS pressure fields.…”

Section: Numerical Assessmentmentioning

confidence: 99%

“…The three different methods are described and a linear uncertainty propagation analysis is performed to estimate the uncertainty on the resulting pressure fields. A numerical assessment is then carried out using the channel flow database from John's Hopkins University (Li et al, 2008;Perlman et al, 2007;Graham et al, 2013). The dependence of all methods on grid resolution and noise levels is tested, as well as the influence of time separation between frames on the Eulerian and pseudo-Lagrangian approaches and of different convection velocities on Taylor's hypothesis.…”

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confidence: 99%

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Full-field pressure from snapshot and time-resolved volumetric PIV

2016

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This paper deals with pressure estimation from snapshot and time-resolved three component (3C) volumetric PIV data using Taylor's hypothesis, an Eulerian and a pseudo-Lagrangian approach. The Taylor's hypothesis approach has been shown to provide accurate results for pressure in the case of 3C planar PIV data with an appropriate choice of convection velocity (de Kat and Ganapathisubramani, 2013) and here we extend its use on 3C volumetric velocity snapshots. Application of the techniques to synthetic data shows that the Taylor's hypothesis approach performs best using the streamwise mean as the convection velocity and is affected the least by noise, while the Eulerian approach suffers the most. In terms of resolution, the pseudoLagrangian approach is the most sensitive. Its accuracy can be improved by increasing the frame time-separation when computing the material derivative, at the expense of volume loss from fluid parcels leaving the FOV. Comparison of the techniques on turbulent boundary layer data with DNS supports these observations and shows that the Taylor's hypothesis approach is the only way we can get pressure when time information is not present.

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Recent Developments in Particle Tracking Diagnostics for Turbulence Research

Machicoane

Huck

Clark

et al. 2019

Soft and Biological Matter

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Flow velocity measurements based on the analysis of the motion of particles imaged with digital cameras have become the most commonly used measurement technique in contemporary fluid mechanics research [1, 2]. Particle image velocimetry (PIV) and particle tracking velocimetry (PTV) are two widely used methods that enable the characterisation of a flow based on the motion of particles, from Eulerian (PIV) or Lagrangian (PTV) points of view. Several aspects influence the accuracy and reliability of the measurements obtained with these techniques [2]: resolution (temporal and spatial), dynamical range, the capacity to measure 2D or 3D components of velocity in a 2D or 3D fluid domain, statistical convergence, etc. These imaging and analysis considerations depend on the hardware (camera resolution, repetition rate, on-board memory, optical system, etc.) but also on the software (optical calibration relating real-world coordinates to pixel coordinates, particle identification and tracking algorithms, image correlation, dynamical postprocessing, etc.) used in the measurements. In this context, particle tracking velocimetry can provide highly resolved, spatially and temporally, measurements of the flow velocity (if the particles are flow tracers) or of particle velocities (if the particles immersed in the flow have their own dynamics) in experimental

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A Web services accessible database of turbulent channel flow and its use for testing a new integral wall model for LES

Cited by 157 publications

References 59 publications

Anisotropic character of low-order turbulent flow descriptions through the proper orthogonal decomposition

Anisotropic character of low-order turbulent flow descriptions through the proper orthogonal decomposition

Full-field pressure from snapshot and time-resolved volumetric PIV

Recent Developments in Particle Tracking Diagnostics for Turbulence Research

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