Means of noise reduction in acoustic turbulence measurements

Blanckaert, Koen; Lemmin, Ulrich

doi:10.1080/00221686.2006.9521657

Cited by 107 publications

(86 citation statements)

References 25 publications

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“…In addition, the effect of the belt-drive motion on the measured TKE profiles has to be considered. Parasitical noise (here originating from the belt-drive system) is uniformly distributed over the investigated frequency and does not affect the timeaveraged velocities, although it has a negative effect on turbulence investigations (Blanckaert & Lemmin, 2006). It is noteworthy to mention that the simulations exhibit an offset to the measurements which can be described by a normalization multiplier that takes a constant value of 0.445 for all TKE profiles.…”

Section: Comparison Of Turbulence Models For a Racetrack Flumementioning

confidence: 99%

Accuracy and comparison of standard k-ϵ with two variants of k-ω turbulence models in fluvial applications

Farhadi

Mayrhofer

Tritthart

et al. 2017

Engineering Applications of Computational Fluid Mechanics

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Racetrack flumes are established experimental settings in ecohydraulics and sediment studies. Their experimental results are often coupled with numerical simulations. The two-equation turbulence closures of the Reynolds-Averaged Navier-Stokes equations are applied widely in such engineering applications. They are preferred for their ease of use and low computational costs compared with more sophisticated turbulence models involving large-scale fluvial simulations. Here, three variants of two-equation models, i.e. the standard k-ε model and two variants of k-ω models, were applied to tackle mean flow and turbulence in a racetrack flume and a river reach. Regarding model performances, we found an overall reasonable agreement between simulated and measured mean velocity values. Nonetheless, the simulated turbulent kinetic energy exhibited discrepancies to the measured values. The computational costs of all investigated model variants are comparable. Therefore, there is no significant preference among models. The findings confirm the application of these model approaches for mean flow-related investigations (e.g. habitat modeling) but suggest exercising caution for applications sensitive to turbulent processes. ARTICLE HISTORY

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Section: Comparison Of Turbulence Models For a Racetrack Flumementioning

confidence: 99%

Accuracy and comparison of standard k-ϵ with two variants of k-ω turbulence models in fluvial applications

Farhadi

Mayrhofer

Tritthart

et al. 2017

Engineering Applications of Computational Fluid Mechanics

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“…It has a limitation of 5 cm measuring distance downward from the probe location, which restricts the data collection at 5 cm vertical distance near to the water flow free surface. The ADV is equipped with the four-probe-receiver, which can significantly reduce the noise signal of the measurements as compared with the three-probe-receiver ADV [3]. Table 1 presents a summary of all conditions in the hydraulically smooth uniform and nonuniform flow experiments conducted in this study.…”

Section: Experimental Instrumentationsmentioning

confidence: 99%

Dominant features in three-dimensional turbulence structure: comparison of non-uniform accelerating and decelerating flows

Tait

Guo

et al. 2017

Environ Fluid Mech

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The results are presented from an experimental study to investigate three-dimensional turbulence structure profiles, including turbulence intensity and Reynolds stress, of different non-uniform open channel flows over smooth bed in subcritical flow regime. In the analysis, the uniform flow profiles have been used to compare with those of the nonuniform flows to investigate their time-averaged spatial flow turbulence structure characteristics. The measured non-uniform velocity profiles are used to verify the von Karman constant j and to estimate sets of log-law integration constant B r and wake parameter G, where their findings are also compared with values from previous studies. From j, B r and G findings, it has been found that the log-wake law can sufficiently represent the nonuniform flow in its non-modified form, and all j, B r and G follow universal rules for different bed roughness conditions. The non-uniform flow experiments also show that both the turbulence intensity and Reynolds stress are governed well by exponential pressure gradient parameter b equations. Their exponential constants are described by quadratic functions in the investigated b range. Through this experimental study, it has been observed that the decelerating flow shows higher empirical constants, in both the turbulence intensity and Reynolds stress compared to the accelerating flow. The decelerating flow also has stronger dominance to determine the flow non-uniformity, because it presents higher Reynolds stress profile than uniform flow, whereas the accelerating flow does not.

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“…For the flow conditions studied herein these resolutions correspond roughly to 10 wall units in all directions, allowing the determination of the Taylor microscale. The use of the Doppler noise decorrelation technique described in Hurther & Lemmin (2001) and Blanckaert & Lemmin (2006), permitted the estimate of the TKE dissipation peak in its spectrum. The Kolmogorov scales could not be resolved in the velocity spectra, but could be calculated from the TKE dissipation rate.…”

Section: Experimental Set-upmentioning

confidence: 99%

Turbulent transport in the outer region of rough-wall open-channel flows: the contribution of large coherent shear stress structures (LC3S)

2007

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Acoustic Doppler velocity profiler (ADVP) measurements of instantaneous threedimensional velocity profiles over the entire turbulent boundary layer height, δ, of rough-bed open-channel flows at moderate Reynolds numbers show the presence of large scale coherent shear stress structures (called LC3S herein) in the zones of uniformly retarded streamwise momentum. LC3S events over streamwise distances of several boundary layer thicknesses dominate the mean shear dynamics. Polymodal histograms of short streamwise velocity samples confirm the subdivision of uniform streamwise momentum into three zones also observed by Adrian et al. (J. Fluid Mech., vol. 422, 2000, p. 1). The mean streamwise dimension of the zones varies between 1δ and 2.5δ. In the intermediate region (0.2 < z/δ < 0.75), the contribution of conditionally sampled u w events to the mean vertical turbulent kinetic energy (TKE) flux as a function of threshold level H is found to be generated by LC3S events above a critical threshold level H max for which the ascendant net momentum flux between LC3S of ejection and sweep types is maximal. The vertical profile of H max is nearly constant over the intermediate region, with a value of 5 independent of the flow conditions. Very good agreement is found for all flow conditions including the free-stream shear flows studied in Adrian et al. (2000). If normalized by the squared bed friction velocity, the ascendant net momentum flux containing 90 % of the mean TKE flux is equal to 20 % of the shear stress due to bed friction. In the intermediate region this value is nearly constant for all flow conditions investigated herein. It can be deduced that free-surface turbulence in open-channel flows originates from processes driven by LC3S, associated with the zonal organization of streamwise momentum. The good agreement with mean quadrant distribution results in the literature implies that LC3S identified in this study are common features in the outer region of shear flows.

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Means of noise reduction in acoustic turbulence measurements

Cited by 107 publications

References 25 publications

Accuracy and comparison of standard k-ϵ with two variants of k-ω turbulence models in fluvial applications

Accuracy and comparison of standard k-ϵ with two variants of k-ω turbulence models in fluvial applications

Dominant features in three-dimensional turbulence structure: comparison of non-uniform accelerating and decelerating flows

Turbulent transport in the outer region of rough-wall open-channel flows: the contribution of large coherent shear stress structures (LC3S)

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