wind loading of large telescopeswind loading characteristics zis it possible to characterize wind...
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23 August, 2002 CHO SPIE Kona Conference (4837-40) #1
Wind Loading of Large Telescopes
Myung Cho, Larry Stepp, George AngeliAURA New Initiatives Office
David SmithMERLAB
23 August, 2002 CHO SPIE Kona Conference (4837-40) #2
Wind is a key factor in large telescope design
Driving factors:• Larger telescope size, larger wind-induced deflections• Telescope frequencies closer to peaks of wind spectra
Seeing and buffeting• wind helps to mitigate thermally-induced local seeing • wind buffeting affects pointing and tracking and causes localized deformations of mirrors
Wind Test (pressure and velocity)• Wind test on Gemini South Telescope, May 2000
23 August, 2002 CHO SPIE Kona Conference (4837-40) #3
Wind Test and Procedures
• Why: to identify source of pressure variations– Wind attack angles – Telescope pointing angle (Zenith angle) – Wind vent gate positions
• How: to setup various test configurations – 116 different combinations of wind test
• What: to measure wind pressure/velocities– 24/32 pressure; 6 anemometers– Pressure/velocities at 10 samples/second (no delay)
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Gemini South Wind Tests(May 2000) Sensor locations
3-axis anemometers
Pressure sensors, 32 places
Pressure sensors
Ultrasonic anemometer
Ultrasonic anemometer
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Overview of the L16, L9 Analysesstatistical approach - standard design of experiments (DOE)
• Largest effect is from vent gate position.• Elevation angle is NOT statistically significant.
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Simultaneous Animations(c00030oo)
Wind Pressure (N/m2) Mirror Deformation (microns)
Wind Speed at 5 Locations (m/sec)
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Structure functionCorrelation length, Lp
Are the pressures on the mirror correlated?If so, what is the limiting distance?
Solution: Structure functionD(ρ) = < | P(r+ ρ) - P(r) |2 >L p data set L p data set Lp data set
1.95 meter c00030oo 1.33 meter c04530oo 0.83 meter c09030oo1.80 meter t00030oo 1.71 meter t04530oo1.78 meter c00060oo 1.38 meter c04560oo 0.90 meter c09060oo1.88 meter d00060oo 0.90 meter d09060oo
wind direction90º
zenith angle 30o
zenith angle 60o
wind direction0º
wind direction45º
Correlation length is less than 2 meters for most wind cases
Strong dependence on wind direction
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Wind loading characteristics
Is it possible to characterize wind pressure as a time varying average pressure pattern?
If so, average temporal pressure can be representable to wind pressure.Average pressure can be predictable from Computational Fluid Dynamics (CFD) modelsThen, characteristics of wind pressure and velocity from CFD models can be an input wind loading as PSD in FE analyses
23 August, 2002 CHO SPIE Kona Conference (4837-40) #9
Best Fit to Average Pressure
Wind pressure at sensor locations
Temporal Average pressure at sensor locations
Pi(t) = C(t) Pai + Pri(t)Pi(t) raw data at time t at ith sensor C(t) coefficient of fit at time tPai temporal average pressure at ith sensorPri(t) fit residual at time t at ith sensor
<Pi(t)2> = <C(t) Pai2 > + <Pri(t)2 > + 2 <Pri(t) * C(t) Pai>
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Coefficient of Fit C(t)
C(t) time variation
<Pi(t)2> = <C(t) Pai2 > + <Pri(t)2 > + 2 <Pri(t) * C(t) Pai>
Fit ResidualAverage pressure is representable to wind pressure
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Does C(t) contain velocity effects?
C(t) with wind velocity at M1
f –1.384Kolmogorov:
( ) 35−
= fCfΦ vv
Prms vs C(t) PSD fit (open-open)
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.00.0 0.5 1.0 1.5 2.0 2.5 3.0
Prms
C(t)
PSD
slop
e
c13560oo
t00000oo
c13560oo
(f n)
( n )
f –1.70
C(t) shows Kolmogrov velocity PSD f-5/3 rule
23 August, 2002 CHO SPIE Kona Conference (4837-40) #12
Comparison of Predicted and Measured Pressure Patterns
c00030oo
t00030ooMeasured pressure patterns
Facing into wind, Zenith angle = 30°
Pressure pattern using a simple disk predicted by CFD
(DeYoung)
23 August, 2002 CHO SPIE Kona Conference (4837-40) #13
Alternate CFD Model
• For further investigation, Oleg Likhatchev (University of Arizona) set up simple CFD models with a disk of D x H (8m x 2m)
– CFD code FLUENT; Quasi-steady assumption; Zenith angle = 30
Stepped CFD model shows similar effect to measured data(Low pressure in “leading edge”; High in “trailing edge”)Accurate CFD modeling is essential; NIO/TSU collaboration
“CFD modeling of turbulent shedding effects on 30m GSMT”
Poster paper by Xu (4840-43)
23 August, 2002 CHO SPIE Kona Conference (4837-40) #14
Segmented Mirror wind modeling
Goals in segment wind modeling To specify segment support stiffnessTo identify segment control bandwidthSubscale model to GSMT integrate modelEnables to build analytical tools for GSMT
Segment configurationDimension = 1.33 m point to pointSupport stiffness = 10 N/microns36 segments for a 8 m primary mirror
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Gemini Imaginary Segmented Mirror
Gemini Segmented
Mirror Testbed (GSMT)
Gemini Mirror
23 August, 2002 CHO SPIE Kona Conference (4837-40) #16
Sample wind at t=0.1 sec. (t00000oo)
Wind pressure distribution Segment displacement pattern (microns)
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Spot size for Diffraction limit (single segment)
0.5 arcseconds
0.1 arcseconds
nominal seeing
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GISM Sample animation (t00000oo)
Spot diagram (arc-seconds)Segment displacement pattern (microns)
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Simultaneous Animation (t00000oo)
Spot diagram (arc-seconds)Segment displacement pattern (microns)“Active optics and control architecture for GSMT”presented by Angeli (4840-22)
23 August, 2002 CHO SPIE Kona Conference (4837-40) #20
GISM wind effects (t00000oo)
90% energy encircled at 0.173 arcsec radius (for 5 minute integration)
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Effect of vent gate positions
Pressure Deformation
Surface RMS = 0.31 microns
Surface RMS = 0.03 microns
Seg-Deformation
Surface P-V = 0.80 um
Surface P-V = 0.07 um
C00030oo
open
open
C00030cc
closed
closed
23 August, 2002 CHO SPIE Kona Conference (4837-40) #22
Summary and Conclusions
• Correlation length is less than 2 meters in most cases
• Average wind pressure: Low in “leading”; High in “trailing”
• CFD should reflect physical geometries, turbulent flow conditions• Average pressure pattern contains most energy (small fit
residuals)• Wind pressure is favorably expressed by time varying Pavg
• M1 wind deformations: astigmatisms; Segment in high
frequencies
• PSD of C(t) fits with frequency in fn, n ~ ( -1.4 to -2.4)
• On average C(t) fits show Kolmogrov velocity PSD f-5/3 rule
• Developed analytical/design tools for GSMT
23 August, 2002 CHO SPIE Kona Conference (4837-40) #23
Future Plan
Wind loading simulationWind loading characteristics (Lo, Lp, PSD,…)Gemini Segmented Mirror Testbed simulation
CFD analysesEstablish CFD models for GSMTIdentify and quantify key design parameters
More wind testsLonger time at higher rate in denser samplesShare expertise and collaborations