Embed Size (px)
DESCRIPTION
Low cost on-line non-invasive sewer flow monitoring Andy Nichols, Kirill Horoshenkov , Simon Tait , Simon Shepherd and Yanmin Zhang [email protected]. Collaborators : Open University Cardiff University Stanford University Yorkshire Water Services Funding : - PowerPoint PPT Presentation
Citation preview
Low cost on-line non-invasive sewer flow monitoring
Andy Nichols, Kirill Horoshenkov, Simon Tait, Simon Shepherd and Yanmin Zhang
Collaborators:• Open University• Cardiff University• Stanford University• Yorkshire Water Services
Funding:• Yorkshire Water Services• EPSRC grant EP/G015341/1
Andy Nichols | [email protected]
‘Traditional’ flow monitor (submerged acoustic Doppler)
Operates from within the flow.Estimates flow depth and velocity
Main disadvantages:• Permanent obstruction to the flow• Accumulation of debris – maintenance requirements• Backscatter approach – power requirements
Andy Nichols | [email protected]
Airborne Doppler flow monitor
Operates from above the flowEstimates flow depth and velocity
Main advantages:• Minimal obstruction to the flow
– only when surcharged• Minimal accumulation of debris
– minimal maintenance
Main disadvantages:• Backscatter approach
– power requirements• Assumption of surface pattern
behaviour
• The free surface pattern does not simply travel along like a car on a motorway.
• Features may appear, fluctuate, oscillate, merge, separate, dissipate.
• The vertical motion can cause a Doppler shift comparable to that of the horizontal motion.
• Above all, the behaviour is not understood, so precisely what the device measures cannot be defined.
BUT: Perhaps the surface fluctuation behaviour can be used….
Wave probe1. An ultrasonic beam is fired toward the dynamic surface.2. The signal is reflected to a receiver.3. The phase of the received signal fluctuates according to the surface. 1
2
3
Andy Nichols | [email protected]
Measuring temporal surface fluctuations at a point – forward scatter
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
x 10-4
-1
-0.5
0
0.5
1
Time (s)
Nor
mal
ised
Am
plitu
de
Sent signalReceived signal
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
x 10-4
-1
-0.5
0
0.5
1
Time (s)
Nor
mal
ised
Am
plitu
de
Sent signalReceived signal
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
x 10-4
-1
-0.5
0
0.5
1
Time (s)
Nor
mal
ised
Am
plitu
de
Sent signalReceived signal
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
x 10-4
-1
-0.5
0
0.5
1
Time (s)
Nor
mal
ised
Am
plitu
de
Sent signalReceived signal
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
x 10-4
-1
-0.5
0
0.5
1
Time (s)
Nor
mal
ised
Am
plitu
de
Sent signalReceived signal
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
x 10-4
-1
-0.5
0
0.5
1
Time (s)
Nor
mal
ised
Am
plitu
de
Sent signalReceived signal
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
x 10-4
-1
-0.5
0
0.5
1
Time (s)
Nor
mal
ised
Am
plitu
de
Sent signalReceived signal3
114 115 116 117 118 119 120 121 122 123 124
73
74
75
Dep
th (m
m)
Time (s)
Time series from two wave probes seperated by 30mm
Andy Nichols | [email protected]
Tracking surface fluctuations
2 wave series separated by a small distance
• Multiple receivers allow a fluctuation time series to be recorded from multiple known locations on the free surface.
• Time series from nearby points can be quite different (hence the issues with the Doppler approach),
• BUT similar enough to estimate the temporal lag (by cross-correlation), and hence the surface velocity.
So we can measure advection velocity between pairs of reflection points, and can use a time-of-flight technique to
measure flow depth, and hence estimate flow rate.
BUT
We can also cross correlate between pairs of reflection points to obtain a
spatial correlation function
Andy Nichols | [email protected]
But can we do more?- Measuring the spatial evolution of the surface pattern
)2cos()( 10
2/ 22
LeW w
This describes the nature of the free surface pattern and relates to the
underlying turbulence, which is governed by the flow conditions, allowing a number
of empirical relationships to be drawn.
0 20 400
50
100
150
200
250
300
350
Roughness coefficient, ks (mm)
Sur
face
cha
ract
eris
tic p
erio
d, L 0 (m
m)
0 0.2 0.4 0.60
0.2
0.4
0.6
0.8
1
Mean flow velocity, U (m/s)
RM
S w
ave
heig
ht, h rm
s (mm
)
0 0.2 0.40
50
100
150
200
250
300
350
Depth x Bed Slope, DS0 (mm)
Sur
face
cha
ract
eris
tic p
erio
d, L 0 (m
m)
0 20 400
50
100
150
200
250
300
350
Roughness coefficient, ks (mm)
Sur
face
cha
ract
eris
tic p
erio
d, L 0 (m
m)
0 0.2 0.4 0.60
0.2
0.4
0.6
0.8
1
Mean flow velocity, U (m/s)
RM
S w
ave
heig
ht, h rm
s (mm
)
0 0.2 0.40
50
100
150
200
250
300
350
Depth x Bed Slope, DS0 (mm)
Sur
face
cha
ract
eris
tic p
erio
d, L 0 (m
m)
Andy Nichols | [email protected]
Conclusions• Forward scatter airborne acoustics allow unambiguous measurement of flow surface
behaviour.
• Tracking the free surface pattern in this manner allows velocity estimation.
• Time-of-flight measurements can provide depth data, in order to estimate flow rate.
• Further information regarding the flow conditions is encoded in the free surface pattern.
Thank You
Low cost on-line non-invasive sewer flow monitoring
Andy Nichols, Kirill Horoshenkov, Simon Tait, Simon Shepherd and Yanmin Zhang
