Transcript Document

Some applications of PHOENICS in the underwater
environment at the Defence Science and Technology
Laboratory (Dstl)
Dr R P Hornby
Defence Science and
Technology Laboratory
Winfrith, UK
This work was carried
out as part of
the Electronics
Systems Research
Programme
NASA Space Shuttle Flight STS-7 18.5N 111.5E 23 June 1983
21 July 2015
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PHOENICS European User
Group Meeting, London, 30th
Nov 2006
Dstl is part of the
Ministry of Defence
Why PHOENICS?
• Predicting the underwater environment is a challenging problem
– Vital in assessing the performance of underwater sensors and the feasibility of
maritime operations
• Shelf Sea and Ocean models (UK Metrological Office)
– Provide environmental information at relatively large scale
– Not currently able to economically resolve the smaller scale processes
• Internal wave motions
– Affect water column density structure
– Produce relatively large current pulses
– Enhance turbulence and mixing
– These models also employ a hydrostatic approximation
• Restricted to processes with relatively small vertical velocities
– Precludes analysis of large amplitude internal wave propagation
• PHOENICS
– General purpose fluid flow package solving the full equations of motion
• Used to investigate these relatively small scale, but important, environmental
effects
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Observations of internal waves
• Regions of most energetic Shelf Edge internal tides
–
UK Shelf, Bay of Biscay
–
China Seas
–
Amazon Shelf
–
Northwest Australian Shelf, Timor Sea
–
Cape Cod Grand Banks, New York Bight, Mid Atlantic Bight
–
Bay of Bengal, Andaman Sea
–
Mid-Argentine Shelf
–
Pakistan/Goa Shelf, Arabian Sea
–
Gulf of Panama
–
Gulf of Alaska
–
North Bering Sea
University of Delaware (US) database
• Regions of most energetic internal tides at straits,
ridges and seamounts
–
Strait of Gibraltar
–
Strait of Messina
–
Strait of Malacca
–
Mascarene Ridge
–
Mid-Atlantic Ridge
–
Hawaian Ridge
–
Horseshoe seamounts (Portugal)
–
Hebrides Terrace, Anton Dohrn Seamounts (NW of UK)
UK Shelf
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Luzon Strait,
South China Sea
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Large amplitude internal waves
• Large amplitude internal waves
– Prevalent where stratified ocean is forced over bathymetry
• Shelf edge regions (eg UK Malin Shelf)
• Straits (eg Gibraltar)
• Ridges and seamounts
• Amplitudes as large as 100-150m, ‘wavelength’ ~ 1000m
• Phase speed ~1m/s
Wave of depression
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Wave of elevation
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Radar imaging of internal waves
Adapted from Liu et al 1998; waves are travelling
from right to left
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UK Shelf study area
Shelf Edge Study (SES) area
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UK Shelf study area
Light bands followed by
dark bands
B
300m
Left: Synthetic Aperture Radar image of
SES study area
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Right: SES mooring marked with diamonds and
labelled S700 to S140. Thermistor chain track
shown as dotted line, 0000-0200 19th August
1995. ‘A’ ,’B’ mark position of lead solitons at
1136 on 20th and 21st August 1995.
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UK Shelf study area: internal wave profiles
Malin shelf internal wave. Density (kg/m3) field (left) and horizontal velocity
(m/s) field (right) at t=0s. Water depth=140m.
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Internal wave dispersal effects
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South China Sea
• ASIAEX (Asian Seas International Acoustics Experiment)
– ONR sponsored, 2001
• Orr and Mignerey (NRL, 2003) reported in situ measurements
– ADCP (Acoustic Doppler Current Profiler:200, 350kHz)
• Water velocity as function of depth
• Acoustic backscatter from plankton, zooplankton etc or
turbulence to map internal wave shape
– CTD (Conductivity Temperature Depth probe)
• Density structure
– RADAR
• Detects internal wave at distance due to backscatter from
surface ‘roughness’ induced by passage of wave
• Real time display allows perpendicular traverse of wave
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Measurement site
Asian Seas International Acoustics Experiment, 2001
Transformation, Mixing
Luzon Strait
Generation: Kuroshio, tidal
Spreading
Refraction
Diffraction
Reflection
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Radar imaging of internal waves, South China Sea
Dark bands followed by light
bands as waves shoal
Light bands followed by dark bands
From Hsu and Liu 2000
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IW ship survey
Orr and Mignerey, 2003
Upslope direction (dashed line)
Ship track (solid line)
P Mignerey, private communication
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Acoustic backscatter
Orr and Mignerey, 2003
Horizontal axis is time
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~70m and 40m
amplitude
waves in deep
water, travelling
from left to right
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Simulation approach
• Computational Fluid Dynamics
Malin Shelf
– Unsteady 2-D equations of motion, no
Coriolis force (Ro>>1), Cartesian grid
– Source term for bed friction
– Two equation k,e turbulence model
with buoyancy effects
– Initial waveform derived from weakly
non-linear theory
dt=10s
dt=5s
dt=2.5s
115
Mean isotherm distance from bottom (m)
• 3rd order accurate spatial upwind
scheme
• 1st order implicit in time
• Porosity representation for arbitrary
bathymetry
• Grid: dx=15m, dy=2m, dt=1.25s
(Determined from previous simulations)
120
dt=1.25s
110
105
100
95
90
1000
1500
2000
2500
3000
x(m)
• Simulate internal wave propagation
– 260m to 100m over 20km range
– Slope gradient 1 in 125
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3500
Density structure
Nmax~ 17cph
Typical temperature and salinity measurements (left) and
resulting averaged density profile (right).
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Initial wave shape and range velocity fields
100m amplitude wave. (Left) Initial density field showing wave shape, KdV shape
(dotted) and empirical KdV (solid). (Right) Initial range velocity field.
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IW profiles
Elevation waves appearing in 175m to 190m depth (measurements record 150m to 180m depth)
(Left) CFD wave evolution for initial 70m wave and (right) 100m wave. The time interval between
each profile is 1250s. The thick dashed line represents the sea bed.
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IW phase speed
Green Cyan
Purple
Yellow
Variation of wave phase speed with on shelf
propagation. The solid curve represents the 100m
amplitude initial wave and the dashed curve the 70m
amplitude initial wave. ASIAEX measurements
(coloured) ; Mignerey, private communication
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Red
Blue
ADCP record (Mignerey, private
communication) marked with
features used to determine wave
phase speeds
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IW shape
CFD (left) wave profile predictions for the 100m initial wave at t=21250s compared with observations
(right, Orr and Mignerey, 2003) from ADCP backscatter intensity. Waves are travelling from left to right.
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IW velocity field
CFD(left) range velocity comparison for the 100m initial wave at t=21250s with ADCP (right, Orr and
Mignerey private communication) range velocity measurements
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IW kinetic energy – upslope component
Total ke from ADCP
Upslope
ke from
ADCP
Simulation
(upslope)
Estimates (with error bar) from
ADCP for just lead soliton and
elevation wave
GM
Kinetic energy per unit crest length in a control volume centred on the leading wave and extending 2.5km
in the upstream and downstream directions (from 22.4m below the surface to 24m above the bottom).
Square symbols – 7th May, triangles 8th May. ADCP upslope ke: Mignerey, private communication.
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Turbulent dissipation rate
(Left) Log10 of the rate of dissipation of turbulent kinetic energy per unit mass at t=11250s (scale range
is –9.05 to –3.79). Density contours relative to 1000 kg/m3 are superimposed to illustrate the wave
shape in relation to the dissipation predictions. (Right) Gradient Richardson number plot.
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Turbulent dissipation rate
(Left) Log10 of the rate of dissipation of turbulent kinetic energy per unit mass at t=21250s (scale
range is –9.05 to –3.84). Density contours relative to 1000 kg/m3 are superimposed to illustrate the
wave shape in relation to the dissipation predictions. (Right) Gradient Richardson number plot.
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Turbulent dissipation rate – elevation waves
Peak dissipation rate levels ~10-4 W/kg predicted in the elevation waves
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Turbulence levels
• Turbulent kinetic energy integrated
over a control volume 2.5km
upstream and downstream of leading
wave
• Energy dissipation rate by turbulence
in a control volume 2.5km upstream
and downstream of leading wave
• Energy dissipation rate and
turbulence levels peak as elevation
waves form
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Ambient turbulence
Dstl Mixed Layer Model
Shelf sea vertical profiler
(UW)
Oregon coast – J Moum
Dstl Mixed Layer Model
Open literature, various sources
Elevation wave prediction
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Bottom shear stress
Typical shear stress distribution
Flow distribution
Bed shear stress after
formation of elevation
wave (note change in
sign due to flow
reversal)
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Maximum bed stress with range
A bed stress ~ 2N/m**2
would lift sand type
particles with diameter
< ~0.1mm (Shields
criterion)
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Bottom sediment transport – passive scalar
(Left) Concentration distribution at t=20000s+1250s from an initial slope line source between
15km and 16.5km range . (Right) Concentration distribution at t=20000s+2500s. Wave position
at t=20000s shown with dashed line. Current wave position shown as solid line.
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Summary
• PHOENICS simulations have produced satisfactory results
• Reasonable agreement for ASIAEX programme
– Phase speeds
– Evolving wave shape and flow structure
– Kinetic energy in wave
• Results show strong horizontal and vertical flows and highest levels of
turbulence as the wave of depression transforms into waves of elevation
– Turbulence results need validating against measurements
• Improvements to quality and computing time can be achieved
– Second order accurate time discretisation (Ochoa et al PHOENICS J 2004)
– PARSOL for variable bathymetry (Palacio et al PHOENICS J 2004)
• Adaptive formulation?
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21 July 2015
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