Remote monitoring of shallow turbulent flows based on the Doppler spectra of airborne ultrasound

Traditional flow monitoring techniques where the sensors are immersed in the flow are costly, and often need frequent maintenance. Non-contact measurement techniques can be used to determine the hydraulic conditions of free surface flows based on a characterisation of the air-water interface. They are robust, relatively cheap, and can be safely operated, but their applications in shallow turbulent flows such as rivers and open channels are limited by the limited understanding of the free surface roughness behaviour. This research aims at characterising the rough moving surface of shallow turbulent water flows and its interaction with airborne acoustic waves. The purpose of this work is to facilitate the development of accurate and reliable non-contact sensors that can measure the mean surface velocity of shallow turbulent flows from the Doppler spectrum of airborne backscattered ultrasonic waves. The dynamics of the free surface were characterised experimentally in a laboratory flume with a homogeneously rough flat bed, over a range of subcritical flow conditions. The three-dimensional patterns on the free surface can be represented by a model of gravity-capillary waves with random phase. These patterns and their statistics are dominated by the spatial and temporal scales of the stationary waves generated by the interaction with the rough bed in all conditions where the mean surface velocity is larger than the minimum phase velocity of gravity-capillary waves. When the mean surface velocity is smaller than the minimum phase velocity of gravity-capillary waves, the surface shows patterns that travel at the mean surface velocity and can be generated by the non-resonant interaction with turbulence inside the flow. In these conditions, the effects of coherent turbulent structures on the surface dynamics are negligible. A simplified linear model of the free surface dynamics was implemented in two different models of acoustic scattering based on the Kirchhoff approximation. The numerical predictions of the acoustic Doppler spectra in the backscattering and in the forward scattering configuration were compared with the experimental measurements in the same flow conditions. The comparison allows the rigorous interpretation of the measured Doppler spectra, and helps to identify the factors that link them to the behaviour of the free surface. The results can inform the design of more reliable non-contact measurement sensors with applications in shallow turbulent flows. There are important implications for modelling of the interaction between a homogeneously rough bed and the free surface, and for the study of transport and mixing phenomena near the interface

Wavelet spectral analysis of the free surface of turbulent flows

This work demonstrates the applicability of the wavelet directional method as a means of characterizing the free surface dynamics in shallow turbulent flows using a small number of sensors. The measurements are obtained with three conductance wave probes in a laboratory flume, in a range of subcritical flow conditions where the Froude number was smaller than one, and the bed was homogeneously rough. The characteristic spatial scale of the surface elevation is found to correspond to the wavelength of stationary waves oriented against the flow. The spectrum of the dominant distribution of waves is characterized in terms of an angular spreading function. A new procedure to estimate the mean surface velocity based on measurements of the surface elevation at only two locations is proposed. The results can inform the development of more accurate models of the surface behaviour, with applications for the remote sensing of rivers and open channel flows

Measured temporal variations of CO2 concentration and atmospheric emissions in a hydropeaking-impacted river

Rivers are increasingly recognised as active players in the global carbon cycle. They are able to transport, transform, and exchange organic matter, and can emit considerable fluxes of greenhouse gases (e.g., CO2) into the atmosphere, with a magnitude comparable to the global carbon input to the oceans. However, the quantification of these processes is still affected by considerable uncertainties, driven by an incomplete understanding of the interplay between physical, geochemical, and biological parameters, and by a lack of spatially and temporally resolved high-quality data. For instance, and despite a potentially strong impact on kilometres of rivers worldwide, the effects of hydropeaking on riverine CO2 emissions have been almost completely neglected until recently (Calamita et al., Unaccounted CO2 leaks downstream of a large tropical hydroelectric reservoir, PNAS 2020). As a contribution to filling this knowledge gap, we present the results of a field-measurement campaign performed in a single-thread Alpine river (River Noce, Italy) during multiple hydropeaking events. Data of water-dissolved CO2, water temperature, and flow discharge, were collected sub-hourly both downstream and upstream of the outlets of a hydropower plant, revealing a complex pattern of variation in time at both locations. Water released from the hydropower plant during hydropeaking had oversaturated CO2 concentrations relative to the atmosphere, in close agreement with water samples collected in the hypolimnion of the upstream reservoir. Higher flow rates during hydropeaking events were associated with higher rates of gas exchange through the water-air interface. Higher exchange rates and higher CO2 concentrations in water during hydropeaking events enhanced CO2 fluxes, as confirmed by measurements with a floating CO2 flux chamber. Meanwhile, the CO2 concentration upstream of the outlets displayed strong diel fluctuations around the atmospheric equilibrium concentration, which were likely driven by primary production within the residual flow during the day. It is shown that the residual flow can have a previously unacknowledged added value as a CO2 sink during the day, fueled by its biological activity. Hydropower releases bypassed the residual flow and discharged hypolimnetic water oversaturated with CO2 at high flow rates during hydropeaking, offsetting CO2 concentration and fluxes downstream of the outlets and increasing emissions on average. These results highlight the ubiquity of hydropeaking impacts also with respect to greenhouse gas emissions. They illustrate the complexity of the riverine carbon cycle and demonstrate the importance of temporally and spatially-resolved data for the accurate assessment of the riverine carbon balance

Doppler spectra of airborne sound backscattered by the free surface of a shallow turbulent water flow

Measurements of the Doppler spectra of airborne ultrasound backscattered by the rough dynamic surface of a shallow turbulent flow are presented in this paper. The interpretation of the observed acoustic signal behavior is provided by means of a Monte Carlo simulation based on the Kirchhoff approximation and on a linear random-phase model of the water surface elevation. Results suggest that the main scattering mechanism is from capillary waves with small amplitude. Waves that travel at the same velocity of the flow, as well as dispersive waves that travel at a range of velocities, are detected, studied and used in the acoustic Doppler analysis. The dispersive surface waves are not observed when the flow velocity is slow compared to their characteristic velocity. Relatively wide peaks in the experimental spectra also suggest the existence of nonlinear modulations of the short capillary waves, or their propagation in a wide range of directions. The variability of the Doppler spectra with the conditions of the flow can affect the accuracy of the flow velocity estimations based on backscattering Doppler. A set of different methods to estimate this velocity accurately and remotely at different ranges of flow conditions is suggested

TIA-1 Cytotoxic Granule-Associated RNA Binding Protein Improves the Prognostic Performance of CD8 in Mismatch Repair-Proficient Colorectal Cancer

Evidence suggests a confounding effect of mismatch repair (MMR) status on immune response in colorectal cancer. The identification of innate and adaptive immune cells, that can complement the established prognostic effect of CD8 in MMR-proficient colorectal cancers patients, representing 85% of all cases, has not been performed

Identification of localised water surface patterns using unsupervised learning

Water surface deformations in rivers can have very different shapes and dynamics depending on which mechanism drives them: turbulence, wind, and/or bed morphology. While some surface patterns can be treated as effective tracers for image-based velocimetry, others, such as gravity waves, affect the accuracy of the measurements. Emerging analysis techniques attempt to filter out the contribution of waves, or conversely to enhance them in order to infer the flow properties based on their dynamics. These approaches are typically based on Fourier analysis, which decomposes the images in a homogeneous distribution of sinusoidal waves. In contrast, water surface patterns such as vortices, scars, wakes, or even groups of ripples, are localised in space and in time. Identifying them, or distinguishing them from other types of waves, requires a more targeted approach. This work proposes the use of unsupervised machine learning to identify a collection of wave patterns that can represent the water deformations. Unlike Fourier bases, the wave patterns learned are able to capture more sophisticated dynamics with increased efficiency. Provided enough training data, it is in principle possible to distinguish different types of surface deformations, and to determine their scale and speed with a higher resolution than is currently allowed by Fourier analysis. This work shows preliminary results including learned components from two video recordings of the river Sheaf (Sheffield, UK) obtained with a fixed camera at different flow conditions. The learned components resemble a combination of wave-groups localised in space-time, confirming the importance of moving beyond the assumption of stationarity.QC 20220404</p

Identification of localised water surface patterns using unsupervised learning

Water surface deformations in rivers can have very different shapes and dynamics depending on which mechanism drives them: turbulence, wind, and/or bed morphology. While some surface patterns can be treated as effective tracers for image-based velocimetry, others, such as gravity waves, affect the accuracy of the measurements. Emerging analysis techniques attempt to filter out the contribution of waves, or conversely to enhance them in order to infer the flow properties based on their dynamics. These approaches are typically based on Fourier analysis, which decomposes the images in a homogeneous distribution of sinusoidal waves. In contrast, water surface patterns such as vortices, scars, wakes, or even groups of ripples, are localised in space and in time. Identifying them, or distinguishing them from other types of waves, requires a more targeted approach. This work proposes the use of unsupervised machine learning to identify a collection of wave patterns that can represent the water deformations. Unlike Fourier bases, the wave patterns learned are able to capture more sophisticated dynamics with increased efficiency. Provided enough training data, it is in principle possible to distinguish different types of surface deformations, and to determine their scale and speed with a higher resolution than is currently allowed by Fourier analysis. This work shows preliminary results including learned components from two video recordings of the river Sheaf (Sheffield, UK) obtained with a fixed camera at different flow conditions. The learned components resemble a combination of wave-groups localised in space-time, confirming the importance of moving beyond the assumption of stationarity.QC 20220404</p

kOmega - a Matlab script to estimate river discharge remotely based on water surface dynamics

Matlab scriptkOmega is a Matlab script to calculate the average flow velocity and water depth of rivers and open-channel flows from sequences of images of the water surface recorded with a camera. The analysis is based on the method described in Dolcetti et al., 2022, Using noncontact measurement of water surface dynamics to estimate river discharge, Water Resources Research, 58 (9), e2022WR032829. https://doi.org/10.1029/2022WR032829. The script computes the frequency-wavenumber spectra of the input set of images, and runs an optimisation routine to compare the measurements with the theoretical dispersion relation of water waves and to identify the set of flow parameters that provide the best fit with the measured data. The method allows the estimation of the average flow velocity without requiring the presence of artificial tracers. It implements a robust analytical model of the water surface dynamics, therefore the accuracy is not undermined by the presence of gravity waves (including standing/stationary waves). The method is best suited for the analysis of videos or sets of images where surface deformations such as gravity waves are clearly visible, although it can also be applied in the absence of visible waves in the presence of artificial or natural floating tracers with suitable density.</p

Doppler spectra of airborne ultrasound forward scattered by the rough surface of open channel turbulent water flows

Experimental data are presented on the Doppler spectra of airborne ultrasound forward scattered by the rough dynamic surface of an open channel turbulent flow. The data are numerically interpreted based on a Kirchhoff approximation for a stationary random water surface roughness. The results show a clear link between the Doppler spectra and the characteristic spatial and temporal scales of the water surface. The decay of the Doppler spectra is proportional to the velocity of the ow near the surface. At higher Doppler frequencies the measurements show a less steep decrease of the Doppler spectra with the frequency compared to the numerical simulations. A semi-empirical equation for the spectrum of the surface elevation in open channel turbulent flows over a rough bed is provided. The results of this study suggest that the dynamic surface of open channel turbulent flows can be characterized remotely based on the Doppler spectra of forward scattered airborne ultrasound. The method does not require any equipment to be submerged in the flow and works remotely with a very high signal to noise ratio