Characterization of Flow Structures Induced by Highly Rough Surface Using Particle Image Velocimetry, Proper Orthogonal Decomposition and Velocity Correlations

Andersson, Lars-Olof; Larsson, I. A. Sofia; Hellström, J. Gunnar I.; Andreasson, Patrik; Andersson, Anders G.; Lundström, T. Staffan

doi:10.4236/eng.2018.107028

Cited by 4 publications

(5 citation statements)

References 30 publications

(28 reference statements)

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“…The temporally averaged velocity shows recirculation at the same position. These results, along with results presented by Andersson et al (2018), suggest vortex shedding occurring at the roughness element. When the flow is accelerated over the roughness element, the pressure will decline accordingly and fluid will congregate in the lowpressure zone downstream of the roughness element.…”

Section: Vortex Sheddingsupporting

confidence: 85%

“…Hence, second quadrant events are dominant in this area, associated with strong turbulent production, ejection of fluid away from the wall and rapid oscillations in the flow (Kim et al, 1987). Similarly, quadrant analysis at ū max showed an overwhelming dominance of Q2 events according to Bennet and Best (1995) and Andersson et al (2018). The peaks in the flatness are caused by surges in both velocity components, connected to the vortex shedding occurring at the roughness element.…”

Section: Higher-order Statisticsmentioning

confidence: 87%

See 1 more Smart Citation

Localized roughness effects in non-uniform hydraulic waterways

Andersson

Larsson

Hellström

et al. 2020

Journal of Hydraulic Research

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Hydropower tunnels are generally subject to a degree of rock falls. Studies explaining this are scarce and the current industrial standards offer little insight. To simulate tunnel conditions, high Reynolds number flow inside a channel with a rectangular cross-section is investigated using particle image velocimetry and pressure measurements. For validation, the flow is modelled using large-eddy simulation (LES) and a Reynolds-averaged Navier-Stokes (RANS) approach with the k − ε turbulence model. One wall of the channel has been replaced with a rough surface captured using laser scanning. The results indicate flow-roughness effects deviating from the standard non-asymmetric channel flow, and hence cannot be properly predicted using spatially averaged relations. These effects manifest as localized bursts of velocity connected to individual roughness elements. The bursts are large enough to affect both temporally and spatially averaged quantities. Both turbulence models show satisfactory agreement for the overall flow behaviour, where LES also provided information for in-depth analysis.

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Section: Vortex Sheddingsupporting

confidence: 85%

Section: Higher-order Statisticsmentioning

confidence: 87%

Localized roughness effects in non-uniform hydraulic waterways

Andersson

Larsson

Hellström

et al. 2020

Journal of Hydraulic Research

View full text Add to dashboard Cite

show abstract

“…The extremes range from 10 to 1500 depending on the position of the tunnel wall. Earlier studies [7,8] have shown that the majority of the frictional losses come from the large scale roughness, hence, numerical wall-functions will have a small effect on the resulting flow. Numerically, the k-ε model is relatively cheap compared to e.g., LES and it is known to capture the main flow behaviour for similar applications [8].…”

Section: Discretization and Simulation Setupmentioning

confidence: 99%

“…It has been shown that, indeed, the roughness may have a dominant effect on the flow in the tunnel i.e., the flow parameters are highly dependent on where you measure inside of the tunnel [7]. In this work, the relation between the static pressure and the cross-section of the tunnel is evaluated.…”

Section: Introductionmentioning

confidence: 99%

Numerical Investigation of a Hydropower Tunnel: Estimating Localised Head-Loss Using the Manning Equation

Andersson

Hellström

Andreasson

et al. 2019

Water

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The fluid dynamics within a water tunnel is investigated numerically using a RANS approach with the k-ε turbulence model. The computational model is based on a laser scan of a hydropower tunnel located in Gävunda, Sweden. The tunnel has a typical height of 6.9 m and a width of 7.2 m. While the average cross-sectional shape of the tunnel is smooth the local deviations are significant, where some roughness elements may be in the size of 5 m implying a large variation of the hydraulic radius. The results indicate that the Manning equation can successfully be used to study the localised pressure variations by taking into account the varying hydraulic radius and cross-sectional area of the tunnel. This indicates a dominant effect of the tunnel roughness in connection with the flow, which has the potential to be used in the future evaluation of tunnel durability. ANSYS-CFX was used for the simulations along with ICEM-CFD for building the mesh.

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“…Therefore, it has been widely employed in the optimization design of airfoil and turbomachinery [4][5][6][7], flow field efficiency and stability of eigenvalue solution and having better applicability in data prediction. Therefore, it has been widely employed in the optimization design of airfoil and turbomachinery [4][5][6][7], flow field analysis [8][9][10][11], and data mining [12]. The third author [13][14][15] of this paper applied the POD method to the inverse problem of a centrifugal pump impeller, with a modal analysis of the flow field and a reconstruction of the gas-liquid two-phase flow field of a liquid ring pump, which extends the ideas and methods of flow characteristic analysis and optimization design of a centrifugal pump.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction and Prediction of Flow Field Fluctuation Intensity and Flow-Induced Noise in Impeller Domain of Jet Centrifugal Pump Using Gappy POD Method

Ren

Zhang

2018

Energies

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To analyze the interrelations among impeller blade geometry, flow field fluctuation intensity and impeller-induced hydrodynamic noise of jet centrifugal pump (JCP), a Gappy proper orthogonal decomposition (POD) method combined with computational fluid dynamics/computational fluid acoustics (CFD/CFA) technique was proposed to reconstruct and predict the unsteady flow field fluctuation intensity and flow-induced noise in impeller region. The snapshot sets were composed of blade profile parameters, flow field fluctuation intensity data and the data of sound pressure level of hydrodynamic noise in the frequency domain. Similar mesh reconstruction and flow field interpolation were carried out to have the same number of flow field data. The snapshot sets were decomposed into a linear combination of orthogonal bases using the singular value decomposition (SVD) method. The orthogonal basis coefficients corresponding to the objective variables were fitted by the least square method. The results show that the proposed method has a good accuracy in predicting the flow field fluctuation intensity and flow-induced noise of the JCP impeller domain. The relative error of pressure fluctuation intensity field is less than 4.0%, relative velocity fluctuation intensity field is less than 3.0%, turbulent kinetic energy fluctuation intensity field is less than 4.5%, and impeller-induced hydrodynamic noise is less than 10%. Taking the method as a surrogate model to predict the flow field fluctuation intensity and the radiation level of hydrodynamic noise in the optimization process of centrifugal pump impeller, it could not only reduce the calculation amount and time significantly and improve optimization speed and efficiency greatly but could also provide a reference for vibration characteristics of the models.

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Characterization of Flow Structures Induced by Highly Rough Surface Using Particle Image Velocimetry, Proper Orthogonal Decomposition and Velocity Correlations

Cited by 4 publications

References 30 publications

Localized roughness effects in non-uniform hydraulic waterways

Localized roughness effects in non-uniform hydraulic waterways

Numerical Investigation of a Hydropower Tunnel: Estimating Localised Head-Loss Using the Manning Equation

Reconstruction and Prediction of Flow Field Fluctuation Intensity and Flow-Induced Noise in Impeller Domain of Jet Centrifugal Pump Using Gappy POD Method

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