58 research outputs found
Application of Wavelet Techniques for the Detection of Energetic Flow Events Associated to Particle Entrainment
Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv
Flow hydrodynamics across open channel flows with riparian zones: implications for riverbank stability
Riverbank vegetation is of high importance both for preserving the form (morphology) and function (ecology) of natural river systems. Revegetation of riverbanks is commonly used as a means of stream rehabilitation and management of bank instability and erosion. In this experimental study, the effect of different riverbank vegetation densities on flow hydrodynamics across the channel, including the riparian zone, are reported and discussed. The configuration of vegetation elements follows either linear or staggered arrangements as vegetation density is progressively increased, within a representative range of vegetation densities found in nature. Hydrodynamic measurements including mean streamwise velocity and turbulent intensity flow profiles are recorded via acoustic Doppler velocimetry (ADV)āboth at the main channel and within the riverbank. These results show that for the main channel and the toe of riverbank, turbulence intensity for the low densities (Ī» ā 0 to 0.12 mā1) can increase up to 40% compared the case of high densities (Ī» = 0.94 to 1.9 mā1). Further analysis of these data allowed the estimation of bed-shear stresses, demonstrating 86% and 71% increase at the main channel and near the toe region, for increasing densities (Ī» = 0 to 1.9 mā1). Quantifying these hydrodynamic effects is important for assessing the contribution of physically representative ranges of riparian vegetation densities on hydrogeomorphologic feedback
Smart Pebble Design For Environmental Monitoring Applications
Sediment transport, due to primarily the action of water, wind and ice, is one of the most significant geomorphic processes responsible for shaping Earthās surface. It involves entrainment of sediment grains in rivers and estuaries due to the violently fluctuating hydrodynamic forces near the bed. Here an instrumented particle, namely a āsmart pebble , is developed to investigate the exact flow conditions under which individual grains may be entrained from the surface of a gravel bed. This could lead in developing a better understanding of the processes involved, while focusing on the response of the particle during a variety of flow entrainment events. The āsmart pebble is a particle instrumented with MEMS sensors appropriate for capturing the hydrodynamic forces a coarse particle might experience during its entrainment from the river bed. A 3-axial gyroscope and accelerometer registers data to a memory card via a microcontroller, embedded in a 3D-printed waterproof hollow spherical particle. The instrumented board is appropriately fit and centred into the shell of the pebble, so as to achieve a nearly uniform distribution of the mass which could otherwise bias its motion. The āsmart pebble is powered by an independent power to ensure autonomy and sufficiently long periods of operation appropriate for deployment in the field. Post-processing and analysis of the acquired data is currently performed offline, using scientific programming software. The performance of the instrumented particle is validated, conducting a series of calibration experiments under well-controlled laboratory conditions. Smart pebble allows for a wider range of environmental sensors (e.g. for environmental/pollutant monitoring) to be incorporated so as to extend the range of its application, enabling accurate environmental monitoring which is required to ensure infrastructure resilience and preservation of ecological health
Flood risk modeling of urbanized estuarine areas under uncertainty: a case study for Whitesands, UK
Aims: The impacts of catastrophic flooding have steadily increased over the last few decades. This work investigated the effectiveness of flood modeling, with low dimensionality models along with a wealth of soft (qualitative) and hard (quantitative) data. In the presence of very low resolution or qualitative data this approach has the potential of assessing a plethora of different scenarios with little computational cost, without compromise in prediction accuracy.
Study Design: A flood risk modeling approach was implemented for the urbanized and flood prone region of Whitesands, at the Scottish town of Dumfries. This involved collection of a wide range of data: a) topographical maps and data from field visits were used to complement existing cross-sectional data, for building the riverās geometry, b) appropriate hydrological data were employed to run the simulations, while historical information about the extent, depth and impacts of flooding were utilized for calibrating the hydraulic model, and c) a wealth of photographic data obtained during the most recent December 2013 flood, were used for the modelās validation.
Place and Duration of Study: Desk study: School of Engineering, University of Glasgow; September 2013 to May 2014. Field study: Dumfries; November 2013 to January 2014.
Methodology: The HEC-RAS 1D model has been used to represent the hydraulics of the system. Flood maps were produced considering the local topography and predicted inundation depths. Flood cost and risk takes further into account the type and value of inundated property as well as the extent and depth of flooding.
Results: The model predictions (inundation depths and flood extents presented in the flood maps) were in fairly good agreement with the observed results along the studied section of the river.
Conclusion: This study presented a flood modeling approach that utilized an appropriate range of accessible data in the absence of detailed information. As the level of performance was comparable to other inundation models the results can be used for identification of flood mitigation measures and to inform best management strategies for waterways and floodplains
Prediction Of Scour Depth Around Bridge Piers Using Adaptive Neuro-Fuzzy Inference Systems (ANFIS)
Earth\u27s surface is continuously shaped due to the action of geophysical flows. Erosion due to the flow of water in river systems has been identified as a key problem in preserving ecological health but also a threat to our built environment and critical infrastructure, worldwide. As an example, it has been estimated that a major reason for bridge failure is due to scour. Even though the flow past bridge piers has been investigated both experimentally and numerically, and the mechanisms of scouring are relatively understood, there still lacks a tool that can offer fast and reliable predictions. Most of the existing formulas for prediction of bridge pier scour depth are empirical in nature, based on a limited range of data or for piers of specific shape. In this work, the application of a Machine Learning model that has been successfully employed in Water Engineering, namely an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to estimate the scour depth around bridge piers. In particular, various complexity architectures are sequentially built, in order to identify the optimal for scour depth predictions, using appropriate training and validation subsets obtained from the USGS database (and pre-processed to remove incomplete records). The model has five variables, namely the effective pier width (b), the approach velocity (v), the approach depth (y), the mean grain diameter (D50) and the skew to flow. Simulations are conducted with data groups (bed material type, pier type and shape) and different number of input variables, to produce reduced complexity and easily interpretable models. Analysis and comparison of the results indicate that the developed ANFIS model has high accuracy and outstanding generalization ability for prediction of scour parameters. The effective pier width (as opposed to skew to flow) is amongst the most relevant input parameters for the estimation
Studying sediment transport dynamics by using the Smart sphere
A new method is introduced by using high precision accelerometer and gyroscope micro-electromechanical sensors (MEMS), which can record Lagrangian observations of sediments and shed light into the dynamics of sediment transport processes at above threshold conditions. The sensor can be used under a range of well-controlled flow conditions and can record measurements at high frequency (200 Hz), which can be used at the field. The smart sphere performance was evaluated by comparing its rotation and acceleration readings from the sensors to the video recordings of both top and underwater high-speed camera for a range of flow rates and sphere densities. Furthermore, an initial attempt to compare the smart-sphereās velocity is achieved, by transforming the particleās velocity from the Lagrangian frame of reference, obtained from the inertial sensor, to its velocity at the Eularian frame, obtained from the top camera
On the impulse criterion for entrainment of coarse grains in air
River hydrodynamicsTurbulent open channel flow and transport phenomen
The critical role of the boundary layer thickness for the Initiation of aeolian sediment transport
Here, we propose a conceptual framework of Aeolian sediment transport initiation that includes the role of turbulence. Upon increasing the wind shear stress Ļ above a threshold value Ļā²t , particles resting at the bed surface begin to rock in their pockets because the largest turbulent fluctuations of the instantaneous wind velocity above its mean value uĀÆ induce fluid torques that exceed resisting torques. Upon a slight further increase of Ļ , rocking turns into a rolling regime (i.e., rolling threshold ĻtāĻā²t ) provided that the ratio between the integral time scale TiāĪ“/uĀÆ (where Ī“ is the boundary layer thickness) and the time Teāād/[(1ā1/s)g] required for entrainment (where d is the particle diameter and s the particleāairādensity ratio) is sufficiently large. Rolling then evolves into mean-wind-sustained saltation transport provided that the mean wind is able to compensate energy losses from particle-bed rebounds. However, when Ti/Te is too small, the threshold ratio scales as Ļt/Ļā²tāTe/Tiāsd2/Ī“2 , consistent with experiments. Because Ī“/d controls Ti/Te and the relative amplitude of turbulent wind velocity fluctuations, we qualitatively predict that Aeolian sediment transport in natural atmospheres can be initiated under weaker (potentially much weaker) winds than in wind tunnels, consistent with indirect observational evidence on Earth and Mars
The design and calibration of instrumented particles for assessing water infrastructure hazards
The highly dynamical entrainment and transport processes of solids due to geophysical flows is a major challenge studied by water infrastructure engineers and geoscientists alike. A miniaturised instrumented particle that can provide a direct, non-intrusive, low-cost and accessible method compared to traditional approaches for the assessment of coarse sediment particle entrainment is developed, calibrated and tested. The instrumented particle presented here is fitted with inertial microelectromechanical sensors (MEMSs), such as a triaxial accelerometer, a magnetometer and angular displacement sensors, which enable the recording of the particleās three-dimensional displacement. The sensor logs nine-axis data at a configurable rate of 200ā1000 Hz and has a standard mode of deployment time of at least one hour. The data can be obtained and safely stored in an internal memory unit and are downloadable to a PC in an accessible manner and in a usable human-readable state. A plethora of improved design specifications have been implemented herein, including increased frequency, range and resolution of acceleration and gyroscopic sensing. Improvements in terms of power consumption, in comparison to previous designs, ensure longer periods of data logging. The embedded sensors are calibrated using simple physical motions to validate their operation. The uncertainties in the experiments and the sensorsā readings are quantified and an appropriate filter is used for inertial sensor fusion and noise reduction. The instrumented particle is tested under well-controlled lab conditions, where the beginning of the destabilisation of a bed surface in an open channel flow, is showcased. This is demonstrative of the potential that specifically designed and appropriately calibrated instrumented particles have in assessing the initiation and occurrence of water infrastructure hazards
Assessing and modelling the interactions of instrumented particles with bed surface at low transport conditions
Sediment transport at near threshold to low transport stages (below the continuous transport) can still be affected by flow turbulence and its dynamics can benefit from further comprehensive studies. This study uses an instrumented particle embedded with micro electromechanical sensors (MEMS) to allow tracking the motions and forces acting on it, leading to and during its transport. Instrumented particle transport experiments were carried out at laboratory flume under a range of flow conditions. The probability distributions functions (PDFs) of bed load particle instantaneous velocities, hop distances and associated travel times (measured from start to stop of transport) were obtained for all the performed experiments with varying flow rates and particle density. The modelled distributions are useful and enable a deeper understanding of bed load sediment transport dynamics from a Lagrangian perspective. Furthermore, the results analyzed from the instrumented particle (including the particleās transport mode) were validated using visual particle tracking methods (top and side cameras). The findings of this study demonstrate that for the range of turbulent flows trialed herein, the instrumented particle can be a useful, accessible, and low-cost tool for obtaining particle transport dynamics, having demonstrated satisfactory potential for field deployment in the near future
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