ADV data processing - FenixEduruif/SUMMER_SCHOOL/presentations/Seditrans_… · ADV data processing Noise correction, despiking ... • errors due to the methodology used to compute

ADV

Sontek

Vectrino (Nortek)

Vectrino Profiler (Nortek)

Rita F. Carvalho, DEC-UC

ADV data processing Noise correction, despiking and reducing filtering effects; Calculation of turbulence variables from point measurements by Rita F. Carvalho MARE, DEC-UC, Coimbra, Portugal Acknowledgments: Martin Romagnoli, Jorge Leandro, Pedro Lopes, Ricardo Martins, Patrícia Páscoa, Joana Pião (Universidade de Coimbra);

Summer school on Measuring techniques for turbulent open-channel flows, IST 29th July 2015

• Introduction (ADV,examples,technique) • Noise and despiking • Turbulence • Filtering ef. • Application

ADV – Acoustic Doppler Velocimeter Sontek and Nortek Velocimeters

Single point 3D velocity meters Pulse coherent doppler technology High sampling frequency in a small sampling volume Bi-static measurement systems - means that the transmitter and

receiver are separated physically)


SYSTEM:

Vectrino

Vectrino Profiler


Central Transmitter 6.5 mm ceramic disc transmits at 10 MHz

Receiver Probe Head 6x3 mm ceramic Passive

• Introduction (ADV,examples,technique) • Noise and despiking • Turbulence • Filtering • Applications

The Vector is a field instrument designed for measurements of rapid small scale changes in 3D velocity, used for turbulence, boundary layer measurements, surf zone measurements, and measurements in very low flow areas. The Vectrino is used in hydraulic laboratories to measure turbulence and 3D velocities in flumes and physical models. The Vectrino Profiler is a profiling version of the Vectrino with a 3 cm profiling zone.

ADV – Velocimeters Examples: Vectrino Vector

MODELS:

Vector

Vectrino

Vectrino Profiler Rita F. Carvalho, DEC-UC

they include a sound emitter, three or four sound receivers, and a signal conditioning electronic module

Cortesia: Pedro Agostinho, Qualitas


• Introduction (ADV,examples,technique) • Noise and despiking • Turbulence • Filtering • Application

ADV technique - … to Vectrino Profiler/Vectrino Christian Doppler, proposed Doppler effect in 1842 (the change in frequency of a wave for an observer moving relative to its source - arrival of successive wave crests at the observer is reduced/increased, causing an increase/decrease in the frequency)

MODELS:

Vectrino


T. emits ultrasound (short acoustic pulse) at a known frequency and R. gets back the echo of ultrasound (sound-scattering particles) reflected in the water particles with a different frequency difference between the frequency

emitted and reflected is proportional to water velocity

Minimized size of the electronics makes it fit inside

Reduced probe size minimizes the flow interference

fourth receiver improves turbulence measurements and provides redundancy



https://en.wikipedia.org/wiki/Christian_Doppler

ADV technique The instrument emits a pair of

pulses of duration Dt a short time apart (Dτ >>Dt) and measures the phase shift between the reflected signals.

If the along-beam water velocity is vb, the backscattering particles in the water travel along-beam distance vbDτ during the interval Dτ.

the travel time difference between two reflected pulses is 2vbDτ/c where c is the speed of the sound in water, which means that the phase shift Dϕ between the two backscattered pulses is 2πf0(2vbDτ/c) where f0 is the ADV acoustic frequency

The instrument measures this phase shift ϕ to determine the along-beam water velocity as vb = cDϕ/(4πfoDτ) = lDϕ/(4πDτ)

The along-beam velocities are converted to the Cartesian velocities by an internal processing module using a calibration matrix velocity results of the acoustic echo reflected by the sound-scattering particles in the water during an overall time T/3 - averaged value, over an interval time T

Increased internal sampling rate reduces measurement noise Probe configuration file stored on probe board simplifies a change of probes. Integrated temperature sensor in the probe. Parallelized receiver increases the number of samples by four Rita F. Carvalho, DEC-UC

phase shift is calculated from the auto- and cross correlation computed for each single pulse-pair using pulse-to-pulse coherent Doppler techniques, radial velocities vi i=1,2,3 are computed using the Doppler relation (fADV=10 MHZ)

along the time with frequency fS=1/T (100 Hz)



ADV – sampling volume – spatial averaging MODELS:

Vectrino

sidelooking Rita F. Carvalho, DEC-UC

The sampling volume resembles, approximately, a cylinder with the axis along the axis of the transmitter. The transmitter diameter gives the sampling volume diameter (φ = 6 mm), while the convolution of the acoustic pulse length with the receive window over which the return signal is sampled defines the sampling volume height (hv = 1.2 to 20 mm depends on the instrument model and manufacturing company). Nortek allows the users to define the height of the sampling volume between 3mm and 15mm for the Vectrino ADV and between 5mm and 20mm for the Vector ADV. In the case of SonTek/YSI, the sampling volume height is 4.5mm for the 16MHz MicroADV and 9mm for the 10MHz ADV (alter the sampling volume height with software modifications.)

Although, within the sampling volume, many backscattering particles move, only one velocity vector is reported. This can be thought as some kind of spatial averaging.



ADV – sampling - temporal averaging Since the instrument employs different frequencies for sampling (fs) and recording (fR) signals, a temporal average of N values is performed in order to reduce the Doppler noise. Thus, fR = fs /N. This second averaging process is a digital non-recursive temporal filter - the process of acquisition itself (analogous to the spatial averaging) can be seen as an analog filter with a cut-off frequency, 1/T or fS

The higher sampling frequency of Vectrino is due to the fact that the MicroADV performs a sequential sampling of each receiver while Vectrino performs a simultaneous sampling for all the receivers.

MODELS:

Vectrino

downlooking Rita F. Carvalho, DEC-UC



ADV measurements The uncertainty involved in the experimental determination of a turbulence parameter presents several components: • errors due to the experimental setup (e.g. misalignment of the

instrument); • errors due to physical constraints of the measurement technique

(e.g. the size of the sampling volume and sampling frequency of the instrument);

• statistical errors due to sampling a random signal, and • errors due to the methodology used to compute the parameters

(e.g. scaling relations only provide order of magnitude estimates of a parameter).

ADV produces 3D mean values water velocity (accuracy better than 4%)

Characterize turbulent flow is being done exploring ADV capabilities - velocity variances (in the streamwise, spanwise and vertical direction), turbulent kinetic energyand Reynolds shear stresses.

Vectrino

Field probe Rita F. Carvalho, DEC-UC



ADV data All data is output at the user specified sample rate - each receiver corresponds a velocity component, a signal strength value, a signal-to-noise (SNR) and a correlation value.

MODELS:


• Velocity vectors - The Vectrino uses Pulse-Coherent Processing: a Doppler shift is estimated from a change in phase between two signals output in either beam or XYZ coordinate systems in m/s • Amplitude – measure of the return signal strength in digital counts • Signal-to-noise ratio (SNR) – exactly that, expresses Amplitude relative to the instrument noise level in DB. Higher is better. • Correlation – A measure of signal quality in %. Higher is better. ADV should be operated at the maximum recording frequency fR and minimum sampling volume height



Examine data before and after effects of any processing on data, particularly processing which is attempting to correct or remove data and to understand the underlying theory being used to detect and remove outliers.


ADV data the signal strength (acoustic backscatter intensity) may related

to the instantaneous suspended sediment concentration with proper calibration

signal strength (SNR) and correlation values are used primarily to determine the quality and accuracy of the velocity data - simple SNR and correlation filters rarely work to remove spikes (or outliers), so some type of spike/outlier filter is important.

Although noise and spikes can be reduced or eliminated in

many cases by adjustment of probe operational parameters, there are some situations in which spikes cannot be entirely avoided



2 different spatial points Rita F. Carvalho, DEC-UC

characterize turbulence is important A turbulent flow is characterised by an unpredictable behaviour, a broad spectrum of length and time scales, and its strong mixing properties (Chanson 2008) turbulence is a "random" process, the small departures from a

Gaussian probability distribution are some of its key features. The skewness and kurtosis give some information on the temporal distribution of the turbulent velocity fluctuation around its mean value. A non-zero skewness indicates some degree of temporal asymmetry

of the turbulent fluctuation, an excess kurtosis larger than zero is associated with a peaky signal

produced by intermittent turbulent events. The Reynolds stress is a transport effect resulting from turbulent

motion induced by velocity fluctuations with its subsequent increase of momentum exchange and of mixing

Turbulence measurements must be conducted at high frequency to resolve the small eddies and the viscous dissipation process.

They must also be performed over a period significantly larger than the characteristic time of the largest vortical structures

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2 different spatial points Rita F. Carvalho, DEC-UC

To characterize turbulence we need two point, time statistics Velocity at two different time turbulence kinetic energy (k) is the mean kinetic energy per unit mass associated with eddies in turbulent flow. It is produced by fluid shear, friction or buoyancy, or through external forcing at low-frequency eddies scales (integral scale). Physically, the turbulence kinetic energy is characterised by velocity

fluctuations RMS (measured root-mean-square- square root of the mean of the squares of a sample ), also known as the quadratic mean in statistics

quantified by the mean of the turbulence normal stresses:

is the integral of E() over all wavenumbers

energy dissipation rate comes from the analytically derived conservation equation for turbulent kinetic energy

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• Noise and despiking • Turbulence • Filtering • Applications • Introduction (ADV,examples,technique)

Turbulence In a fully turbulent flow a range of scales exist for fluctuating velocities that are often characterized as collections of different eddy structures - Eddies of size l have a characteristic velocity u(l) and timescale (l) l/u(l).

macroscopic length scale (physical boundaries of the flow - channel width) account for most of the transport of momentum and energy to smallest turbulent eddies - largest size eddies are characterized by the lengthscale l0 which is comparable to the flow length scale L and timescale 0 l0/u0 .Their characteristic velocity u0u(l0) is on the order of the r.m.s. turbulence intensity u’ (2k/3)1/2 which is comparable to U. energy of eddy with velocity scale u0 is dissipated in time 0 , (m2/s3) is the energy dissipation rate, Re0 u0l0/ is large (comparable to Re) and the direct effects of viscosity on these eddies are negligibly small

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• Noise and despiking • Turbulence • Filtering • Applications • Introduction (ADV,examples,technique)


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energy is transferred to successively smaller and smaller eddies that continues until the Reynolds number Re(l) u(l)l/ is sufficiently small that the eddy motion is stable, and molecular viscosity is effective in dissipating the kinetic energy. At these small scales, the kinetic energy of turbulence is converted into heat, defined as microscopic length scale, viscous scale, Kolmogorov length scale. -are the smallest scales in turbulent flow (viscosity dominates and the turbulent kinetic energy is dissipated into heat). These eddies have energy of order u0

2 and timescale 0 =l0/u0 so the rate of transfer of energy, is proportional to u0

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ADV velocity data • raw ADVs velocity data are not true turbulence and should

never be used without adequate post processing of the recorded signals

• signal suffers parasitical noise contributions • presence of doppler noise, spikes and filtering effects due

to the ADVs' sampling strategy could strongly affect the turbulence characterization based on the recorded signals.

doppler noise which has white noise characteristics constitutes an important error source in acoustic Doppler measurements - it bias the variances high and does not affect the covariances spikes in the water velocity signals recorded using ADVs may be caused by aliasing of the Doppler signal, phase shift ambiguities (when velocities exceed the upper limits of ADV probe velocity range), aeration effects, high turbulence intensities, from measurements near bed of stream,… Filtering affects water velocity signals and then turbulence

DATA




ADV – good description of turbulence when certain conditions related to the instrument configuration - sampling frequency and noise energy level; and flow conditions - convective velocity and turbulence scales in the flow As the velocity signal is sampled at a frequency fS, and the highest frequency that can be resolved by the instrument is fS/2, aliasing of the signal occurs energy in the frequency range of fS/2 < f < fS is folded back into the range 0 < f < fS/2, which may or may not be of importance depending on the flow characteristics. Flows with a large convective velocity, Uc, will have a considerable portion of the energy in the range

of wavelengths: fS /2Uc < f /Uc < fS /Uc, while flows with a low convective velocity will have no energy in this range and, therefore, aliasing will not be of relevance energy in the signal with a frequency higher is filtered out - acquisition process acts as a low-pass filter and reduces all of the even moments in the water velocity signal and affects: • autocorrelation functions increased for small lag times • time scales computed from them producing results that are high biased, • power spectrum producing a low resolution of the inertial range

MODELS:




ADV operations to turbulence flow field characterization – exploring ADV capabilities to measure flow turbulence 1. Acess doppler noise and presence spikes – despiking and

the noise-reduction methods improved the overestimated RMS velocities – In general, noise-reduction filters detect spikes • excluding low-correlation (< 60-70%) and low signal-to-

noise ratio samples (< 5-20 dB) • For each velocity component, the signal is filtered with a

lowpass/high-pass filter threshold F = 0.1 Hz • Despiking involves two independente steps: detecting the

spike and replacing the spike (singlepoint spike, relatively simple despiking algorithms; multipoint spikes not straight forward)

2. Acess filtering effects due to the sampling strategy implemented by ADVs (spatial and temporal averaging processes) • Sampling volume heights • Recording frequencies • Distances from the sampling volume to wall (bottom)




ADV signal outputs and Noise characteristics • ADV signal outputs include the combined effects of turbulent velocity

fluctuations, Doppler noise, signal aliasing, turbulent shear across the sampling volume boundary proximity and other disturbances

Noise • The noise of the different receivers is statistically independent • it is possible to have noisefree estimation of the turbulent shear

stress. However, the estimation of the turbulent normal stress is affected by noise

• the total noise in the velocity variance increases at higher sampling rates.

Spike detection algorithms and replacing procedures

• Progressive cut-off of the lower and upper Limits • Acceleration Thresholding Method • Phase-Space Thresholding Method (Goring and Nihora 2002 +

some modification as Whal 2003 (Win ADV) and Nobuhito Mori 2005 (despike_phasespace3d-matlab)).

• Doppler noise removing methodology from Voulgaris & Trowbridge (1998) –spectral method

• corrected autocorrelation function

M




ADV despicking Progressive cut-off of the lower and upper Limits As the velocity time distribution does not fit as a normal distribution, it is better to estimate a threshold trends to the upper limit really registered in the signal - progressive cut-off of the lower and upper limits, as a function of the5 % and 95 % statistical – replacement for the mean or the maximum /minimum cuttoff

Acceleration Thresholding Method - detection and replacement procedure with two phases: one for negative accelerations and the second for positive accelerations to consider a point like spike, the acceleration must exceed a threshold, lag and the absolute deviation from the mean velocity of the point must exceed ks , where la (1-1.5)is a relative acceleration threshold, s the standard deviation and k a factor to be determined (1.5) Phase-Space Thresholding Method the concept of a threedimensional Poincaré map or phase-space plot in which the variable and its derivatives are plotted against each other. The points are enclosed by an ellipsoid defined by the Universal criterion and the points outside the ellipsoid are designated as spikes.




ADV Despiking method - Goring and Nikora 2002 - detecting

spikes – have superior performance to various other methods and it has the added advantage that it requires no parameters method combines three concepts: 1- differentiation enhances the high frequency portion of a signal, 2- the expected maximum of a random series is given by the Universal threshold, (Phase-Space Thresholding) 3- good data cluster in a dense cloud in phase space or Poincare´ maps construct an ellipsoid in three-dimensional phase space The method uses the principle that good data lie within a cluster and that any data point lying well outside that cluster must be suspected of being a spike then points lying outside the ellipsoid are designated as spikes




ADV Despiking method - Goring and Nikora 2002 –

Phase-Space Thresholding 1. Calculate surrogates for the first and second derivatives from

central differences algorithm

2. Calculate the standard deviations of all three variables su , sDu and sD2u , and hence the expected maxima using the Universal criterion;

3. Calculate the rotation angle of the principal axis of Dui2 versus ui

using the cross correlation

4. For each pair of variables, calculate the maxima and minima ellipse. For Dui versus ui the major axis is lU su and the minor axis is lU sDu ; for Dui

2 versus ui the major axis is lU sDu and the minor axis is lU sD2u and for Dui

2 versus ui maximum and minimum a and b are the solution of

5. Wahl proposed the sample median as an estimator of location and, the median of the absolute deviations from the sample median, as estimator of scale WinADV




ADV measurements Spike replacement for ADV data with sampling frequencies from 25 to 100 Hz, the best solution is to use 12 points on either side of the spike to fit a third-order polynomial that is interpolated across the spike

Nortek velocimeters have quite a bit less spiking, especially the Vector - better signal processing algorithms




ADV Doppler noise removing methodology - Voulgaris &

Trowbridge (1998) and verified by Blanckaert & Lemmin (2006) • this automated method “spectral method" computes the noise

energy levels for the longitudinal and transversal water velocity components averaging the energy level in the tail end (noise floor) of each power spectrum (PS)

• PS for the vertical water velocity component recorded using ADV does not often show a Doppler noise floor. Noise energy level for the vertical velocity component is related to the longitudinal and transversal noise energy levels due to geometrical probe configuration (Lemmin and Lhermitte 1999) - Thus, the noise energy level for the vertical velocity component is estimated averaging the noise level of the longitudinal and transversal water velocity components and dividing this average by a constant calculated from the ADV’s calibration matrix 30 (Nikora and Goring 1998).

• Corrected velocity power spectra can be then obtained by subtracting the noise levels from the initial spectra and corrected variance can be estimated for each flow velocity component as the integral of each corrected power spectra.

M




ADV - corrected autocorrelation function • Compute the mean, variance, autocorrelation function, and

power spectra in the time domain for each flow velocity component.

• Correct velocity power spectra and variance – remove low-signal-to-noise and low-correlation data

• The corrected autocorrelation function can be computed by the inverse fast Fourier transform of the corrected computed power spectra

• integrating the corrected autocorrelation function, up to the first zero crossing estimate corrected integral time scale characterizing the larger flow turbulent structures

DATA

Power spectrum in frequency domain




ADV – filtering effects– spatial & temporal averaging Montero et al (2014) provided DNS simulation and proposing a simple assumption on the basis that the smallest flow structure sampled by the instrument is given by the characteristic length scale, calculated time series of each three flow velocity components in sampling volume simulating the ADV sampling strategy. Different sampling volume heights (hv = 0.8, 2.3, 3.9, 5.4, 7.0, 8.6 mm);

recording frequencies (fR = 247.8, 49.5, 24.7, 9.9 and 4.9 Hz); and distances from the sampling volume to the channel bed (z = 0.009, 0.032 and 0.055 m)

Defined dimensionless parameter Fst = z/ max(Uc /fR, φ, hv) – 1st scale sampling frequency limitation other determined by spatial constraints of the sampling volume.

concluded that filtering effects decrease as Fst increases and spatial and temporal averaging are most significant for values of Fst less than 5;

vertical velocity variance is the most affected and Reynolds shear stress and streamwise velocity variance are less affected by filtering.

Vectrino

downlooking Rita F. Carvalho, DEC-UC



ADV measurements – APC curves

Acoustic Doppler Velocimeters performance curves (APC) to quantify filtering effects on both variance and integral time scale as a function of the dimensionless frequency F = fRL/UC (where fR is the recording frequency (50 Hz-100 Hz), L is the energy-containing eddy length scale (assume L to be equal to the water depth), and UC is the convective velocity (local value of the mean velocity in the longitudinal direction), the variance, integral time scale for each velocity component are corrected by filtering effects using the APCs (e.g., for F = fRL/UC =20, filtering effects on variance and integral time scale are about 10% and 10%, respectively)




ADV measurements the Moving Block Bootstrap (MBB) technique (García et al. 2006) provides a good approximation of statistical error

generated during turbulence characterization provides an estimate of the uncertainty for each

parameter to evaluate trends and differences in the parameters computed for different experimental conditions

MBB is a simple resampling algorithm which can replace parametric time series models, avoiding model selection and only requiring an estimate of the moving block length.

A conservative estimate of the uncertainty (95% confidence intervals) for each turbulence parameter can be computed using special techniques.

SediTrans, IST 29th July 2015 Rita F. Carvalho, DEC-UC



ADV Software - experimental investigation on turbulent flow

characteristics in several hydraulic structures complete signal analysis in order to identify and remove the influence of errors in ADV measurements on the computation of the mean velocity, turbulent kinetic energy and Reynolds stresses. ADV yield a good description of the flow turbulence when

certain conditions are satisfied elimination of spikes, using the phase-space threshold despiking method proposed by Goring and Nikora (2002) and Wahl (2003),

ADVs produce a reduction in the moments of the water velocity signal because of the sampling strategy and fiitering used by this velocimetry technique. The minimum distance from the bottom boundary from where velocity

measurements were measured was 0:02m to avoid boundary effects (Liu et al. 2002; Precht et al. 2006; Chanson et al. 2007).

average velocity - correlation coefficient can be as low as 30% turbulence parameters - correlation coefficients lower than 70%

can provide good data when SNR is high and the flow is turbulent (Romagnoli et al. 2011,…)

MODELS:

Electronics housing

Fixed Probe

Cabled Probe

Vectrino

Vectrino Profiler SediTrans, IST 29th July 2015 Rita F. Carvalho, DEC-UC



ADV Measurements Examples 50 cm wide, 50 cm deep and 10 m long channel, 1% slope 30 cm wide, 30 cm deep and 60 cm long acrylic box, with a

circular orifice at the center of the bottom of the box, 8 cm in diameter

inlet Reynolds number ranged between 2.8e+04 and 7.4e+04

EXAMPLES:

3D side looking ADV 180 s 3cm mesh 5 cm to 29 cm no points in the centre of the box SediTrans, IST 29th July 2015 Rita F. Carvalho, DEC-UC

Surcharged flow: additional tube connected to the orifice and a wall in the channel 4, 5 and 6 l/s < 4 to low > 6 not possible

Sampling time was 300s (7500 samples at 25Hz sampling frequency, approximately 100 times the largest turbulence time scale estimated as the integral of the autocorrelation function up to the first zero crossing



ADV Measurements Examples samples with correlation coefficients as low as 50% were not eliminated, which is a similar approach to that of Romagnoli et al. (2012), who considered correlations as low as 45%.

EXAMPLES:


Q (l/s) Hu (m) U (m/s) Re (-) Fr (-) f (Hz) t (s)

S

urc

har

ged

Q4 4 0.76 0.80 6.4x104 0.90

1

180

Q5 5 0.825 0.99 8.0x104 1.12

Q6 6 0.905 1.19 9.5x104 1.34

D

rain

age

Q15 15 0.030 0.99 8x104 1.80

25 Q22 22 0.037 1.18 9x104 1.94

Q32 32 0.047 1.36 11x104 2.00

Q42 42 0.059 1.43 11x104 1.89



Example: ADV Measurements SIGNAL POST-PROCESSING TECHNIQUE • quality of water velocity time series - based on both

the percentage of spikes detected by PSTM and the COR parameter.

• Turbulence parameters from water velocity time series with both high amount of spikes and low COR (<< 70%) were discarded

• neither spike replacement nor Doppler noise affects mean velocities computations.

• Garcia et al. (2005) presented Acoustic Doppler velocimeters Performance Curves (APCs) which quantify filtering effects on variance as a function of the dimensionless frequency F = fsT (where fs is the sampling frequency and T is turbulence time scale).

EXAMPLES:




Results – reversed flow

Q4 Q5 Q6

Min

imu

m

Max

imu

m

•The air is dragged in from the upstream corner.

•Q4 does not present air bubbles

•Air concentration increases with flow rate.



ADV Measurements Examples

EXAMPLES:


Re( (Ud/v or UH/v) – 6 to11 x 1011

Fr (U/(gd)0.5 0.90 and 1.34 or U/(gH)0.5 1.80 and 2.00



Results – reversed flow velocity fields Q4 Q5 Q6

•Anticlockwise vortex on the left side.

•Near the jet the velocity is almost vertical.

•The velocities increase with the flow rate.



ADV Measurements Examples EXAMPLES:


Re( (Ud/v or UH/v) – 6 to11 x 1011






Re( (Ud/v or UH/v) – 6 to11 x 1011






Re( (Ud/v or UH/v) – 6 to11 x 1011






Re( (Ud/v or UH/v) – 6 to11 x 1011






Re( (Ud/v or UH/v) – 6 to11 x 1011






Re( (Ud/v or UH/v) – 6 to11 x 1011






Re( (Ud/v or UH/v) – 6 to11 x 1011




ADV Measurements Examples – Gully reverse flow the flow showed a similar pattern between the different analized experimental conditions in terms of mean velocity and turbulent kinetic energy. • A macro anticlockwise spanwise-axis vortex was identied based

on the mean velocity field in the region encompassed between the upstream wall and the inlet centerline.

• Maximum mean velocity magnitudes were about 25% of the inlet velocity and characterized the outer flow of the vortex.

• Higher turbulent kinetic energy was observed at the inlet centerline region with values about 5% of the inlet velocity

square. • The Reynolds stresses exhibited some anisotropy, which was

revealed by both the shear stresses values and by the differences in the normal stresses.

• Normal stresses were higher than shear stresses with maximum values at the inlet centerline region u2= 3%, v2= 5% and w2=2% of the inlet velocity square, respectively.

EXAMPLES:




•

Results – drainage flow

Q15 Q32 Q42

Min

imu

m

Max

imu

m

•Air entered the gully on the downstream corner.

•Air concentration decreased with the flow rate due to the increase in the water height.



Q15 Q32 Q42

•One vortex located near the center of the gully.

•The highest velocities are on the right side.

•The velocities increase with the flow rate.

Results – drainage flow turbulence



Streamwise Vertical

23

14

5

Height (cm)

• Increase in the normal stresses with horizontal distance. • Maximum values only occur on the downstream wall for vertical at 14 cm. • No clear relation with the flow rate. • For streamwise, the maximum is always Q15, for vertical it is always Q22.

Results – drainage flow turbulence



Results – drainage flow turbulence Height (cm)

23

14

5

Shear Turbulent kinetic energy

•Generaly, there is an increase with the horizontal distance. •Shear stresses, for 23 cm, are of the same order on both walls, except Q22. •Q22 presents the maximum values, except for shear stresses at 5cm.



conclusion experimental investigation on turbulent flow characteristics in several hydraulic structures elimination of spikes, using the phase-space threshold despiking method proposed by Goring and Nikora (2002) and Wahl (2003), elimination of points – (Lohrman et al., 1994),

• signal to noise ratio (SNR) < 5dB for the surcharged flow • signal to noise ratio (SNR) <15dB for the drainage flow • correlation coefficients

average velocity - correlation coefficient can be as low as 30% turbulence parameters - correlation coefficients lower than

70% can provide good data when SNR is high and the flow is turbulent,

samples with correlation coefficients as low as 50% were not eliminated, which is a similar approach to that of Romagnoli et al. (2012), who considered correlations as low as 45%.

MODELS:

Electronics housing

Fixed Probe

Cabled Probe

Vectrino

Vectrino Profiler SediTrans, IST 29th July 2015 Rita F. Carvalho, DEC-UC



Documents

ADV data processing - FenixEduruif/SUMMER_SCHOOL/presentations/Seditrans_… · ADV data processing Noise correction, despiking ... • errors due to the methodology used to compute