Analysis of the frozen flow assumption using GeMS telemetry data Angela Cortés 1, Alexander Rudy 2,...
If you can't read please download the document
Analysis of the frozen flow assumption using GeMS telemetry data Angela Cortés 1, Alexander Rudy 2, Benoit Neichel 3, Lisa Poyneer 4, Mark Ammons 4, Andres
Analysis of the frozen flow assumption using GeMS telemetry
data Angela Corts 1, Alexander Rudy 2, Benoit Neichel 3, Lisa
Poyneer 4, Mark Ammons 4, Andres Guesalaga 1 (1) Pontificia
Universidad Catlica de Chile, Santiago, Chile (2) University of
California Santa Cruz (3) Gemini Observatory Southern Operations
Center, La Serena, Chile (4) Lawrence Livermore National
Laboratory
Slide 2
Goal: Use telemetry data from the GeMS to study the validity of
the frozen flow hypothesis using two types of algorithms:
Spatio-temporal cross-correlations of 5 GeMS laser guidestars WFS
The Fourier Wind Identification (FWI) Results: Number of layers
present and their associated velocities. Estimation of their
altitude and strength (turbulence profiler) Rate of de-correlation
(how frozen is the flow?) Analysis of the frozen flow assumption
using GeMS telemetry data
Slide 3
60 arcseconds 0 1 2 54 84.9 arcseconds 42.4 arcsec 16x16 grid
Shack-Hartmann 204 active subapertures (total: 1020) sampling rate=
< 800 Hz 5 WFSs 3 DMs 917 actuators in total 684 valid actuators
(seen by the WFSs) 233 extrapolated actuators 0 km 4.5 km 9 km
Gemini-South MCAO System (GeMS)
Slide 4
Problems with SLODAR due to dome seeing Solution: spatio-
temporal correlations Turbulence profiling using GeMS telemetry
data (Corts et al, MNRAS 2012) Variance in subapertures, Y
direction Poster - N: 16162 :Performance of two turbulence
profilers for a MCAO system under strong dome seeing condition
Slide 5
For T = 0 s, the turbulence profile in altitude is extracted
from the baseline For T > 0, the layers present can be detected
and their velocity estimated w = 8.8 m/s w = 187.1 w = 17.7 m/s w =
227.7 The wind profiler or spatio-temporal correlations
Slide 6
Next, we analyze 4 cases of Frozen Flow: 1.Dome Turbulence
2.Ground Layer Turbulence 3.Mid Altitude Turbulence 4.High Altitude
Turbulence
Slide 7
1. Frozen flow for turbulence inside the dome Decay in
correlation for dome turbulence Wind speed = 0.0 m/s Dome Ground
layer By applying this method, an estimate of the dome seeing can
be obtained at any time!
Slide 8
2. Frozen flow for turbulence at the ground layer wind speed =
8.8 m/s wind direction = 187.1 Decay in correlation for ground
layer turbulence
Slide 9
3. Frozen flow for turbulence at mid-altitude (~ 4 Km) Wind
speed = 10.0 m/s Wind direction = 172.9 Decay in correlation for
mid-altitude turbulence
Slide 10
4. Frozen flow for turbulence at high-altitude (~ 12 Km) Wind
speed = 21.3 m/s Wind direction = 227.7 Wind speed=17.7 m/s Wind
direction = 227.7 Decay in correlation for high-altitude
turbulence
Slide 11
Dependence of frozen flow to wind speed w = 10.0 m/s |m|= 1.66
s -1 w = 17.7 m/s |m|= 3.26 s -1 w = 0.0 m/s |m|= 0.32 s -1 w = 8.8
m/s |m|= 1.33 s -1 More data is required to verify this!! 1 ~ 6 m
Linear or just a coincidence ? Decay in correlation Absolute rate
of fading, |m| vs. wind speed
Slide 12
Use Fourier Modes in Space and Time to find Frozen Flow Fourier
Wind Identification Fourier Modes Blow in Wind by
Slide 13
Transform Open-Loop Phase into Fourier Modes Fourier Wind
Identification Pseudo-Open Loop Phase Spatial and Temporal Fourier
Modes GeMS TelemetryFourier Modes (Same as Angelas data)
Slide 14
Then Fit Temporal Fourier Peaks to Frozen-Flow Layers Fourier
Wind Identification Find Peaks in Temporal Space Match Found Peaks
to Layer Templates
Slide 15
FWI Finds Frozen Flow Layers in GeMS Pseudo Open Loop Data
Fourier Wind Identification Single Layer Identified (plus a weak
second layer) 2 Layers Identified
Slide 16
Wind Vector Remains Constant when Examined over Longer Times
Fourier Wind Identification Time
Slide 17
Analysis of Longer Telemetry Intervals Improves Overall Signal
Fourier Wind Identification
Slide 18
Spatial-temporal correlation and Fourier Wind Identification
agree Wind speed=17.7 m/s Wind direction = 227.7
Slide 19
Conclusions Both Methods Detect Frozen Flow Turbulence The
short-timescale Spatial Temporal Correlation complements the long
timescale Fourier Wind Identification Both methods makes no
assumption of Kolmogorov Turbulence. Frozen flow exists and its
melting rate is proportional to the wind speed. The method provides
an estimate of the dome seeing. Tracking correlation peaks is a
major problem. Spatial-Temporal Correlation Fourier Wind
Identification Non-Frozen Flow Turbulence is automatically
rejected. No suppression of static modes, DC terms are fit like any
other turbulence.