Using surface flow image velocimetry to analyse flow approaching grated inlets

Large scale particle image velocimetry (LSPIV) is a nonintrusive environmental monitoring methodology that allows for continuous characterization of surface flows in natural catchments. Despite its promise, the implementation of LSPIV in natural environments is limited to areas accessible to human operators. In this work, we propose a novel experimental configuration that allows for unsupervised LSPIV over large water bodies. Specifically, we design, develop, and characterize a lightweight, low cost, and stable quadricopter hosting a digital acquisition system. An active gimbal maintains the camera lens orthogonal to the water surface, thus preventing severe image distortions. Field experiments are performed to characterize the vehicle and assess the feasibility of the approach. We demonstrate that the quadricopter can hover above an area of 1×1m2 for 4–5 minutes with a payload of 500g. Further, LSPIV measurements on a natural stream confirm that the methodology can be reliably used for surface flow studies.

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Seeding metrics for image velocimetry performances in rivers

10.5194/egusphere-egu21-9229 ◽

2021 ◽

Author(s):

Silvano Fortunato Dal Sasso ◽

Alonso Pizarro ◽

Sophie Pearce ◽

Ian Maddock ◽

Matthew T. Perks ◽

...

Keyword(s):

Spatial Distribution ◽

Optical Sensors ◽

Large Scale ◽

Surface Flow ◽

Surface Velocity ◽

Seeding Density ◽

Earth System ◽

Science Data ◽

Image Velocimetry ◽

Hydraulic Conditions

Optical sensors coupled with image velocimetry techniques are becoming popular for river monitoring applications. In this context, new opportunities and challenges are growing for the research community aimed to: i) define standardized practices and methodologies; and ii) overcome some recognized uncertainty at the field scale. At this regard, the accuracy of image velocimetry techniques strongly depends on the occurrence and distribution of visible features on the water surface in consecutive frames. In a natural environment, the amount, spatial distribution and visibility of natural features on river surface are continuously challenging because of environmental factors and hydraulic conditions. The dimensionless seeding distribution index (SDI), recently introduced by Pizarro et al., 2020a,b and Dal Sasso et al., 2020, represents a metric based on seeding density and spatial distribution of tracers for identifying the best frame window (FW) during video footage. In this work, a methodology based on the SDI index was applied to different study cases with the Large Scale Particle Image Velocimetry (LSPIV) technique. Videos adopted are taken from the repository recently created by the COST Action Harmonious, which includes 13 case study across Europe and beyond for image velocimetry applications (Perks et al., 2020). The optimal frame window selection is based on two criteria: i) the maximization of the number of frames and ii) the minimization of SDI index. This methodology allowed an error reduction between 20 and 39% respect to the entire video configuration. This novel idea appears suitable for performing image velocimetry in natural settings where environmental and hydraulic conditions are extremely challenging and particularly useful for real-time observations from fixed river-gauged stations where an extended number of frames are usually recorded and analyzed.&#160;References Dal Sasso S.F., Pizarro A., Manfreda S.,&#160;Metrics for the Quantification of Seeding Characteristics to Enhance Image Velocimetry Performance in Rivers.&#160;Remote Sensing,&#160;12, 1789 (doi: 10.3390/rs12111789), 2020.Perks M. T., Dal Sasso S. F., Hauet A., Jamieson E., Le Coz J., Pearce S., &#8230;Manfreda S, Towards harmonisation of image velocimetry techniques for river surface velocity observations. Earth System Science Data, https://doi.org/10.5194/essd-12-1545-2020, 12(3), 1545 &#8211; 1559, 2020.Pizarro A., Dal Sasso S.F., Manfreda S.,&#160;Refining image-velocimetry performances for streamflow monitoring: Seeding metrics to errors minimisation,&#160;Hydrological Processes, (doi: 10.1002/hyp.13919), 1-9, 2020.Pizarro A., Dal Sasso S.F., Perks M. and Manfreda S., Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow, Hydrology and Earth System Sciences, 24, 5173&#8211;5185, (10.5194/hess-24-5173-2020), 2020.

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On the Uncertainty of the Image Velocimetry Method Parameters

Hydrology ◽

10.3390/hydrology7030065 ◽

2020 ◽

Vol 7 (3) ◽

pp. 65

Author(s):

Evangelos Rozos ◽

Panayiotis Dimitriadis ◽

Katerina Mazi ◽

Spyridon Lykoudis ◽

Antonis Koussis

Keyword(s):

Confidence Intervals ◽

Statistical Approach ◽

Random Number Generator ◽

Surface Flow ◽

Surface Velocity ◽

A Value ◽

Image Velocimetry ◽

Interrogation Area ◽

Definition Of ◽

Parameter Values

Image velocimetry is a popular remote sensing method mainly because of the very modest cost of the necessary equipment. However, image velocimetry methods employ parameters that require high expertise to select appropriate values in order to obtain accurate surface flow velocity estimations. This introduces considerations regarding the subjectivity introduced in the definition of the parameter values and its impact on the estimated surface velocity. Alternatively, a statistical approach can be employed instead of directly selecting a value for each image velocimetry parameter. First, probability distribution should be defined for each model parameter, and then Monte Carlo simulations should be employed. In this paper, we demonstrate how this statistical approach can be used to simultaneously produce the confidence intervals of the estimated surface velocity, reduce the uncertainty of some parameters (more specifically, the size of the interrogation area), and reduce the subjectivity. Since image velocimetry algorithms are CPU-intensive, an alternative random number generator that allows obtaining the confidence intervals with a limited number of iterations is suggested. The case study indicated that if the statistical approach is applied diligently, one can achieve the previously mentioned threefold objective.

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Experimental Research and Numerical Analysis of Flow Phenomena in Discharge Object with Siphon

Water ◽

10.3390/w12123330 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3330

Author(s):

Milan Sedlář ◽

Pavel Procházka ◽

Martin Komárek ◽

Václav Uruba ◽

Vladislav Skála

Keyword(s):

Experimental Research ◽

Turbulence Models ◽

Free Surface Flow ◽

Stationary Flow ◽

Surface Flow ◽

Flow Structures ◽

Complex Flow ◽

Flow Topology ◽

Image Velocimetry ◽

Flow Phenomena

This article presents results of the experimental research and numerical simulations of the flow in a pumping system’s discharge object with the welded siphon. The laboratory simplified model was used in the study. Two stationary flow regimes characterized by different volume flow rates and water level heights have been chosen. The study concentrates mainly on the regions below and behind the siphon outlet. The mathematical modelling using advanced turbulence models has been performed. The free-surface flow has been carried out by means of the volume-of-fluid method. The experimental results obtained by the particle image velocimetry method have been used for the mathematical model validation. The evolution and interactions of main flow structures are analyzed using visualizations and the spectral analysis. The presented results show a good agreement of the measured and calculated complex flow topology and give a deep insight into the flow structures below and behind the siphon outlet. The presented methodology and results can increase the applicability and reliability of the numerical tools used for the design of the pump and turbine stations and their optimization with respect to the efficiency, lifetime and environmental demands.

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Real-Time Measurement of Flash-Flood in a Wadi Area by LSPIV and STIV

Hydrology ◽

10.3390/hydrology6010027 ◽

2019 ◽

Vol 6 (1) ◽

pp. 27 ◽

Cited By ~ 5

Author(s):

Mahmood Al-mamari ◽

Sameh Kantoush ◽

Sohei Kobayashi ◽

Tetsuya Sumi ◽

Mohamed Saber

Keyword(s):

Large Scale ◽

Flash Flood ◽

Early Warning Systems ◽

Flash Floods ◽

Surface Flow ◽

Unidirectional Flow ◽

Real Time Measurement ◽

Positive Performance ◽

Image Velocimetry ◽

Scale Particle

Flash floods in wadi systems discharge large volumes of water to either the sea or the desert areas after high-intensity rainfall events. Recently, wadi flash floods have frequently occurred in arid regions and caused damage to roads, houses, and properties. Therefore, monitoring and quantifying these events by accurately measuring wadi discharge has become important for the installation of mitigation structures and early warning systems. In this study, image-based methods were used to measure surface flow velocities during a wadi flash flood in 2018 to test the usefulness of large-scale particle image velocimetry (LSPIV) and space–time image velocimetry (STIV) techniques for the estimation of wadi discharge. The results, which indicated the positive performance of the image-based methods, strengthened our hypothesis that the application of LSPIV and STIV techniques is appropriate for the analysis of wadi flash flood velocities. STIV is suitable for unidirectional flow velocity and LSPIV is reliable and stable for two-dimensional measurement along the wadi channel, the direction of flow pattern which varies with time.

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Turbulence Measurements in Channel Flows With Rough and Hydrophobic Walls

Volume 2: Fora, Parts A and B ◽

10.1115/fedsm2007-37618 ◽

2007 ◽

Author(s):

N. Ahmad ◽

R. N. Parthasarathy

Keyword(s):

Shear Stress ◽

Rough Surface ◽

Test Section ◽

Reynolds Shear Stress ◽

Turbulent Channel Flow ◽

Surface Flow ◽

Wall Region ◽

Mean Velocity ◽

Image Velocimetry ◽

Coated Surface

Particle Image Velocimetry (PIV) measurements were made in a fully-developed turbulent channel flow. The channel test section was 1 ft wide and 1 inch in height and was constructed out of plexiglass. One wall of the test section was made removable. Four walls were used: a plexiglass smooth wall, and three hydrophobic walls: (i) a lotus paint coated plexiglass wall, (ii) a treated aluminum sheet attached to the plexiglass wall and (iii) a treated rough surface attached to the plexiglass wall. The bulk velocity was held constant to yield a Reynolds number (based on the channel half-height) of 5,500. Several images were averaged to obtain mean velocity and Reynolds shear stress and turbulence kinetic energy measurements. It was found that the mean velocities in the near-wall region were higher for the lotus-paint coated surface flow and the treated rough surface flow than the flows with the other two surfaces. The friction velocity estimated from the Reynolds shear stress measurements was significantly lower for these two flows as well. The reduction in the wall shear stress in these flows is attributed to the finite slip that occurs at the hydrophobic surfaces.

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Spatial distribution of tracers for optical sensing of stream surface flow

10.5194/hess-2020-188 ◽

2020 ◽

Cited By ~ 2

Author(s):

Alonso Pizarro ◽

Silvano F. Dal Sasso ◽

Matthew Perks ◽

Salvatore Manfreda

Keyword(s):

Large Scale ◽

Optical Sensing ◽

Surface Flow ◽

Seeding Density ◽

Stream Surface ◽

Image Velocimetry ◽

Level Of Aggregation ◽

Scale Particle ◽

The One

Abstract. River monitoring is of particular interest for our society that is facing increasing complexity in water management. Emerging technologies have contributed to opening new avenues for improving our monitoring capabilities, but also generating new challenges for the harmonised use of devices and algorithms. In this context, optical sensing techniques for stream surface flow velocities are strongly influenced by tracer characteristics such as seeding density and level of aggregation. Therefore, a requirement is the identification of how these properties affect the accuracy of such methods. To this aim, numerical simulations were performed to consider different levels of particle aggregation, particle colour (in terms of greyscale intensity), seeding density, and background noise. Two widely used image-velocimetry algorithms were adopted: i) Particle Tracking Velocimetry (PTV), and ii) Large-Scale Particle Image Velocimetry (LSPIV). A descriptor of the seeding characteristics (based on density and aggregation) was introduced based on a newly developed metric π. This value can be approximated and used in practice as π = ν0.1 / (ρ / ρcν1) where ν, ρ, and ρcν1 are the aggregation level, the seeding density, and the converging seeding density at ν = 1, respectively. A reduction of image-velocimetry errors was systematically observed by decreasing the values of π; and therefore, the optimal frame window was defined as the one that minimises π. In addition to numerical analyses, the Basento field case study (located in southern Italy) was considered as a proof-of-concept of the proposed framework. Field results corroborated numerical findings, and an error reduction of about 15.9 and 16.1 % was calculated – using PTV and PIV, respectively – by employing the optimal frame window.

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Infrared Micro-Particle Image Velocimetry of Fluid Flow in Silicon-Based Microdevices

Volume 4 ◽

10.1115/ht-fed2004-56385 ◽

2004 ◽

Cited By ~ 5

Author(s):

Dong Liu ◽

Suresh V. Garimella ◽

Steve T. Wereley

Keyword(s):

Particle Image Velocimetry ◽

Particle Image ◽

Capillary Tube ◽

Surface Flow ◽

Mems Devices ◽

Flow Measurements ◽

Micro Particle Image Velocimetry ◽

Micro Piv ◽

Image Velocimetry ◽

Silicon Based

A non-intrusive diagnostic technique, infrared micro-particle image velocimetry (IR-PIV), is developed for measuring flow fields within MEMS devices with micron-scale resolution. This technique capitalizes on the transparency of silicon in the infrared region, and overcomes the limitation posed by the lack of optical access with visible light to sub-surface flow in silicon-based micro-structures. Experiments with laminar flow of water in a circular micro-capillary tube of hydraulic diameter 255 μm demonstrate the efficacy of this technique. The experimental measurements agree very well with velocity profiles predicted from laminar theory. Cross-correlation and auto-correlation algorithms are employed to measure very-low and moderate-to-high velocities, respectively; the former approach is suitable for biomedical applications while the latter would be needed for measurements in electronics cooling. The results indicate that the IR-PIV technique effectively extends the application of regular micro-PIV techniques, and has great potential for flow measurements in silicon-based microdevices.

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Wavenumber-frequency analysis of river surface texture to improve accuracy of image-based velocimetry

E3S Web of Conferences ◽

10.1051/e3sconf/20184006012 ◽

2018 ◽

Vol 40 ◽

pp. 06012

Author(s):

Kojiro Tani ◽

Ichiro Fujita

Keyword(s):

Image Analysis ◽

Frequency Analysis ◽

Surface Texture ◽

Space Time ◽

Surface Flow ◽

Flow Measurements ◽

Image Velocimetry ◽

Wave Effects ◽

Floating Objects ◽

Time Image

In the unseeded image-based techniques for river surface flow measurements, advection speed of surface textures composed of surface ripples or floating objects is measured by image analysis. However, the methods would yield erroneous information when the surface texture is affected by gravity waves propagating in all directions. In order to improve the measurement accuracy, such wave effects have to be subtracted in the image analysis. For that purpose, a wavenumber-frequency analysis was applied to a space-time image (STI) generated in the space-time image velocimetry (STIV) analysis and succeeded in eliminating the wavegenerated pattern contained in the texture in STI. It was made clear that turbulence-generated texture propagates at the speed of surface flow.

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Image-velocimetry techniques under particle aggregation for streamflow monitoring: a numerical approach

10.5194/egusphere-egu2020-15525 ◽

2020 ◽

Author(s):

Alonso Pizarro ◽

Silvano Fortunato Dal Sasso ◽

Salvatore Manfreda

Keyword(s):

Large Scale ◽

Ad Hoc ◽

Numerical Approach ◽

Surface Flow ◽

Particle Aggregation ◽

Seeding Density ◽

Stream Flows ◽

Flow Monitoring ◽

Time Flow ◽

Image Velocimetry

Monitoring extreme events with high accuracy and consistency is still a challenge, even by using up-to-date approaches. On the one side, field campaigns are in general expensive and time-consuming, requiring the presence of high-qualified personnel and forward planning. On the other side, non-contact approaches (such as image velocimetry, radars, and microwave systems) have had promising signs of progress in recent years, making now possible real-time flow monitoring. This work focuses on the estimation of surface flow velocities for streamflow monitoring under particle aggregation, in which tracers are not necessarily uniformly distributed across the entire field of view. This issue is extremely relevant for the computing stream flows since velocity errors are transmitted to river discharge estimations. Ad-hoc numerical simulations were performed to consider different levels of particle aggregation, particle colour and shapes, seeding density, and background noise. Particle Tracking Velocimetry (PTV) and Large-Scale Particle Image Velocimetry (LSPIV) were used for image velocimetry estimations due to their widely used worldwide. Comparisons between the theoretical and computed velocities were carried out to determine the associated uncertainty and optimal experimental setup that minimises those errors.

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