Investigation of free-surface dynamics in an open-channel flow

Yoshimura, Hideto; Fujita, Ichiro

doi:10.1080/00221686.2018.1561531

Cited by 13 publications

(20 citation statements)

References 31 publications

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“…2017). Currently, the highest Reynolds number of DNS for OCF is (Bauer 2015; Yoshimura & Fujita 2020), substantially lower than the highest in experiments by Duan et al. (2020) (i.e.…”

Section: Introductionmentioning

confidence: 85%

“…Recent research on high-Reynolds-number wall turbulence shows numerous interesting features, such as the generation of large-scale motions (LSMs) and very-large-scale motions (VLSMs) (Kim & Adrian 1999;Balakumar & Adrian 2007;Cameron et al 2017). Currently, the highest Reynolds number of DNS for OCF is Re τ ≈ 600 (Bauer 2015;Yoshimura & Fujita 2020), substantially lower than the highest in experiments by Duan et al (2020) (i.e. Re τ ≈ 2400).…”

Section: Introductionmentioning

confidence: 99%

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Direct numerical simulation of turbulent open channel flows at moderately high Reynolds numbers

2022

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Well-resolved direct numerical simulations of turbulent open channel flows (OCFs) are performed for friction Reynolds numbers up to $Re_\tau =2000$ . Various turbulent statistics are documented and compared with the closed channel flows (CCFs). As expected, the mean velocity profiles of the OCFs match well with the CCFs in the near-wall region but diverge notably in the outer region. Interestingly, a logarithmic layer with Kárman constant $\kappa =0.363$ occurs for OCF at $Re_\tau =2000$ , distinctly different from CCF. Except for a very thin layer near the free surface, most of the velocity and vorticity variances match between OCFs and CCFs. The one-dimensional energy spectra reveal that the very-large-scale motions (VLSMs) with streamwise wavelength $\lambda _x>3 h$ or spanwise wavelength $\lambda _z>0.5 h$ contribute the most to turbulence intensity and Reynolds shear stress in the overlap and outer layers (where h is the water depth). Furthermore, the VLSMs in OCFs are stronger than those in CCFs, resulting in a slightly higher streamwise velocity variance in the former. Due to the footprint effect, these structures also have significant contributions to the mean wall shear stress, and the difference between OCF and CCF enlarges with increasing $Re_\tau$ . In summary, the free surface in OCFs plays an essential role in various flow phenomena, including the formation of stronger VLSMs and turbulent kinetic energy redistribution.

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“…2017). Currently, the highest Reynolds number of DNS for OCF is (Bauer 2015; Yoshimura & Fujita 2020), substantially lower than the highest in experiments by Duan et al. (2020) (i.e.…”

Section: Introductionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Direct numerical simulation of turbulent open channel flows at moderately high Reynolds numbers

2022

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“…In this case, the forced waves that moved at the mean flow velocity were seen together with freely propagating gravity-capillary waves. Acoustic measurements by Dolcetti et al (2017) suggested that forced waves can still be present (at least in the capillary range) alongside stationary waves, and that their amplitude increases faster with the increasing Froude number than the amplitude of free waves; a result later contradicted by Yoshimura and Fujita (2020) by means of a direct numerical simulation. The Froude number was also found decreasing the steepness of the surface frequency spectra by Dolcetti et al (2016), suggesting a relatively high contribution of shorter scales (therefore a rougher water surface) at higher Froude numbers.…”

Section: Stationary Waves As Primary Components In the Free-surface Pmentioning

confidence: 99%

Free-surface behaviour of shallow turbulent flows

Muraro

Dolcetti

Nichols

et al. 2021

Journal of Hydraulic Research

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Over the last two decades, interest in the free-surface behaviour of gravity-driven shallow turbulent flows has increased considerably. It is believed that observation of free-surface behaviour can provide useful information about the hydrodynamic characteristics of the flow and enable remote retrieval of these characteristics to non-invasively and rapidly monitor river flows. At the current state the literature presents scattered knowledge and also exhibits non-uniformity in the terminology used. This paper is a review of the state-of-art of this area of research and was created with two objectives: to gather the information relevant to understand the linkages between the free-surface behaviour and underpinning hydrodynamic processes while using a uniform terminology, and to analyse the gaps in our knowledge of this critical topic.

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“…Near the bottom of the channel and the free surface, the grid is clustered with a minimum vertical spacing , where the superscript denotes the length normalised by wall units ; the maximum vertical spacing at the centre of the channel is . The above grid resolutions, as listed in the last four columns in table 2, are comparable to those used in the DNS of turbulent rigid-lid open-channel flows (Yao, Chen & Hussain 2022) and turbulent free-surface open-channel flows (Yoshimura & Fujita 2020). Figure 3 compares the free-surface fluctuation magnitudes , where rms denotes the root-mean-sqaure value, in the present simulations with the literature (Yokojima & Nakayama 2002; Yoshimura & Fujita 2020).…”

Section: Methodsmentioning

confidence: 99%

“…The above grid resolutions, as listed in the last four columns in table 2, are comparable to those used in the DNS of turbulent rigid-lid open-channel flows (Yao, Chen & Hussain 2022) and turbulent free-surface open-channel flows (Yoshimura & Fujita 2020). Figure 3 compares the free-surface fluctuation magnitudes , where rms denotes the root-mean-sqaure value, in the present simulations with the literature (Yokojima & Nakayama 2002; Yoshimura & Fujita 2020). We find that the present results agree well with their data.…”

Section: Methodsmentioning

confidence: 99%

Reconstruction of three-dimensional turbulent flow structures using surface measurements for free-surface flows based on a convolutional neural network

Xuan

Shen

2023

J. Fluid Mech.

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A model based on a convolutional neural network (CNN) is designed to reconstruct the three-dimensional turbulent flows beneath a free surface using surface measurements, including the surface elevation and surface velocity. Trained on datasets obtained from the direct numerical simulation of turbulent open-channel flows with a deformable free surface, the proposed model can accurately reconstruct the near-surface flow field and capture the characteristic large-scale flow structures away from the surface. The reconstruction performance of the model, measured by metrics such as the normalised mean squared reconstruction errors and scale-specific errors, is considerably better than that of the traditional linear stochastic estimation (LSE) method. We further analyse the saliency maps of the CNN model and the kernels of the LSE model and obtain insights into how the two models utilise surface features to reconstruct subsurface flows. The importance of different surface variables is analysed based on the saliency map of the CNN, which reveals knowledge about the surface–subsurface relations. The CNN is also shown to have a good generalisation capability with respect to the Froude number if a model trained for a flow with a high Froude number is applied to predict flows with lower Froude numbers. The results presented in this work indicate that the CNN is effective regarding the detection of subsurface flow structures and by interpreting the surface–subsurface relations underlying the reconstruction model, the CNN can be a promising tool for assisting with the physical understanding of free-surface turbulence.

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Investigation of free-surface dynamics in an open-channel flow

Cited by 13 publications

References 31 publications

Direct numerical simulation of turbulent open channel flows at moderately high Reynolds numbers

Direct numerical simulation of turbulent open channel flows at moderately high Reynolds numbers

Free-surface behaviour of shallow turbulent flows

Reconstruction of three-dimensional turbulent flow structures using surface measurements for free-surface flows based on a convolutional neural network

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