Downstream relaxation of velocity profiles in pipe-flow with swirl disturbances

Graner, Simon; Hinz, Dietrich; Breitsamter, Christian

doi:10.1515/teme-2016-0029

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…For Re ¼ 4 Â 10 3 , all standard deviations are below 5% at 80D and for Re ¼ 4 Â 10 2 all standard deviations are below 5% at 20D. This is consistent with disturbances decaying faster for lower Reynolds numbers (see, for example, Graner et al [11]). However, for Re ¼ 4 Â 10 3 and high input uncertainties, both disturbers show higher standard deviation than for the Re ¼ 4 Â 10 4 case.…”

Section: Reproducibility Of Flow Disturbance Testssupporting

confidence: 74%

“…The swirl angle (21) is a commonly used integral metric to measure the intensity of swirling flow [8,10,11,35]. Experimental data from LDV measurements [11] in our laboratory are used as reference results to determine the comparison error (19). A comparison of the downstream decay of the swirl angle for different mesh resolutions with experimental measurements is shown in Fig.…”

Section: Modeling Error and Validation Uncertaintymentioning

confidence: 99%

“…Convergence of (a) the swirl angle at all downstream locations along with experimental data from Ref [11]. and (b) convergence of the swirl angle at z 5 10D.…”

mentioning

confidence: 91%

“…Turbulent swirling flow was analyzed in the 90 s, for example, by Kitoh [6], and Steenbergen and Voskamp [7]. More recent investigations by M€ uller et al [8], Tawackolian [9], Eichler and Lederer [10], Graner et al [11], and Turiso et al [12], focus specifically on swirling flow generated by standardized swirl disturbers used in flow disturbance tests. For a more complete review of swirling flow, we refer to Graner et al [11] and Turiso et al [12].…”

Section: Introductionmentioning

confidence: 99%

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Reproducibility of Experiments With Swirling Flow: Numerical Prediction With Polynomial Chaos

Hove

Skov²,

Hinz³

2018

Journal of Verification, Validation and Uncertainty Quantification

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Achieving good reproducibility in fluid flow experiments can be challenging, in particular in scenarios where the experimental boundary conditions are obscure. We use computational uncertainty quantification (UQ) to evaluate the influence of uncertain inflow conditions on the reproducibility of experiments with swirling flow. Using a nonintrusive polynomial chaos method in combination with a computational fluid dynamics (CFD) code, we obtain the expectation and variance of the velocity fields downstream from symmetric and asymmetric swirl disturbance generators. Our results suggest that the flow patterns downstream from the asymmetric swirl disturbance generator are more reproducible than the flow patterns downstream from the symmetric swirl disturbance generator. This confirms that the inherent breaking of symmetry eliminates instability mechanisms in the wake of the disturber, thereby creating more stable swirling patterns that make the experiments more reproducible.

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Section: Reproducibility Of Flow Disturbance Testssupporting

confidence: 74%

Section: Modeling Error and Validation Uncertaintymentioning

confidence: 99%

“…Convergence of (a) the swirl angle at all downstream locations along with experimental data from Ref [11]. and (b) convergence of the swirl angle at z 5 10D.…”

mentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reproducibility of Experiments With Swirling Flow: Numerical Prediction With Polynomial Chaos

Hove

Skov²,

Hinz³

2018

Journal of Verification, Validation and Uncertainty Quantification

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“…In past decades, special attention has been paid to the errors generated from installation issues. Martins and Ramos (2011), Graner et al (2016) and Carlander (2001) studied the bend effects and the downstream relaxation of velocity profiles through profile factors that were calculated based on the averaged flow distribution. Ruppel and Peters (2004) explored the UFM sensitivity of transducer installation angles for UFMs, and error shift in UFMs was used as the indicator.…”

Section: Introductionmentioning

confidence: 99%

Detection of turbulent error by helicity in an ultrasonic flowmeter

Geng

Dong

et al. 2018

Transactions of the Institute of Measurement and Control

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Turbulence generated by invading structures in pipes introduces uncertainties into measurements using ultrasonic flowmeters. A numerical model based on large eddy simulation (LES) is developed here to study the features of turbulent errors in a typical U-shaped ultrasonic flowmeter. According to the flow structures obtained by LES, a one-dimensional turbulent spectrum and probability density function of helicity along the effective ultrasonic propagation path are calculated. It can be seen that the turbulent error is dominated by the large-scale anisotropic vortices in a specific range on the sampling line, and the anisotropic vortices have the common largest fluctuating scales in the error-dominated area in which the Reynolds number ( Re) exceeds 15,000. The results provide possibilities for reducing the turbulent error by limiting the largest fluctuating scale through structure optimization. The probability density function of streamwise helicity is first introduced to unveil the continuous turbulent development in an ultrasonic flowmeter, which has the potential to contribute to turbulent analyses.

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