Initial computational fluid dynamics modeling of the Giant Magellan
Telescope site and enclosure
a
Ryan Danks*a, William Smeatona, Bruce Bigelowb, William Burgettb
Rowan, Williams, Davies & Irwin Inc., 600 Southgate Dr., Guelph, ON, Canada, N1G 3W6
b
GMTO Corporation, 465 N. Halstead Ave., Suite 250, Pasadena, CA, USA, 91101
ABSTRACT
In the era of extremely large telescopes (ELTs), with telescope apertures growing in size and tighter image quality
requirements, maintaining a controlled observation environment is critical. Image quality is directly influenced by
thermal gradients, the level of turbulence in the incoming air flow and the wind forces acting on the telescope. Thus any
ELT enclosure must be able to modulate the speed and direction of the incoming air and limit the inflow of disturbed
ground-layer air. However, gaining an a priori understanding of the wind environment’s impacts on a proposed telescope
is complicated by the fact that telescopes are usually located in remote, mountainous areas, which often do not have high
quality historic records of the wind conditions, and can be subjected to highly complex flow patterns that may not be
well represented by the traditional analytic approaches used in typical building design. As part of the design process for
the Giant Magellan Telescope at Cerro Las Campanas, Chile; the authors conducted a parametric design study using
computational fluid dynamics which assessed how the telescope’s position on the mesa, its ventilation configuration and
the design of the enclosure and windscreens could be optimized to minimize the infiltration of ground-layer air. These
simulations yielded an understanding of how the enclosure and the natural wind flows at the site could best work
together to provide a consistent, well controlled observation environment. Future work will seek to quantify the aerothermal environment in terms of image quality.
Keywords: Computational Fluid Dynamics, site microclimate, enclosure design, telescope siting
1.
INTRODUCTION
1
The Giant Magellan Telescope (GMT) is a ground based ELT, currently under construction at Las Campanas
Observatory, Chile, which when completed will consist of seven 8.4m primary mirror segments providing ten times the
resolution of the Hubble Space Telescope. While modern active and adaptive optics systems help mitigate the impact of
temperature gradients and wind induced movement, a well-designed enclosure can reduce the degree of those impacts.
For an ELT this is particularly important, as the large size of the primary mirror leads to increased wind forces acting on
them, which can potentially strain correction systems. Thus, ELT projects are increasingly employing modeling tools
like CFD, to qualify and quantify airflow through and around the enclosure and telescope 2,3.
This paper presents the results of preliminary work conducted by the authors to better understand the interaction between
the wind microclimate at Las Campanas and the design of the enclosure. The computational fluid dynamics (CFD)
simulations in this work were preliminary in nature, with the goal being to use simpler, more nimble models to assess a
number of possible scenarios to establish patterns and trends rather than absolute values. In addition to work
independently conducted by The Boeing Company4, the authors conducted over 30 simulations to characterize the
existing wind conditions, and also investigate the following questions:
1.
How does the geometry of the enclosure influence the air flow through and around it?
2.
How does the enclosure geometry interact with the natural wind speed variations at the site, and can those
interactions be taken advantage of to improve the performance of the enclosure?
3.
How does the windscreen and enclosure vent configuration impact the above results, and how can they be used
most effectively?
*[email protected]; phone 1 519 823-1311; fax 1 519 823-1316; rwdi.com
Modeling, Systems Engineering, and Project Management for Astronomy VII,
edited by George Z. Angeli, Philippe Dierickx, Proc. of SPIE Vol. 9911, 991113
© 2016 SPIE · CCC code: 0277-786X/16/$18 · doi: 10.1117/12.2233288
Proc. of SPIE Vol. 9911 991113-1
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2.
COMPUTATIONAL FLUID DYNAMICS APPROACH
A 3D computational model was developed using the commercial CFD package StarCCM+ v9.06 5. This software uses a
finite-volume approach to CFD, in other words the study domain is divided into many small sub-volumes (“cells”)
within which velocity, pressure and turbulence parameters are to be predicted.
2.1 Geometry
In order to properly define the study domain, a suitable representation of all blockages to airflow must be created using a
3D solid modeling package. Care must be taken to ensure that models are detailed enough to ensure that they have the
proper effect on airflow, but are not so detailed that simulation times become unreasonably long.
2.1.1 Terrain
Since there is no way to accurately predict a priori what the approach flow would be near the mesa, a large study domain
was required to ensure the terrain’s influence on the approach flow was captured appropriately. A 16km² area centered
on the mesa was modeled using a combination of topographic data provided by the GMTO and data sourced from
NASA’s Shuttle Radar Topography Mission (STRM) data set6. The data provided by the GMTO consisted of high
resolution topographic survey data (1-10m contours) of an area approximately 1km in radius around the site but did not
represent a large enough domain for approach flows from all directions, so the remainder of the area was filled in using
the STRM data set. The levels of detail are indicated in the plan view of the domain illustrated in Figure 1.
1m Contour Lines
5m Contour Lines
1110m Contour Lines in 1 Arc-Second (30m) Digital
Elevation Data
Figure 1. Topographic resolution of simulated terrain.
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2.1.2 Telescope & Optics
A simplified 3D model of the telescope and optical system components was provided by the GTMO for use in this study.
The model was then converted into an appropriate file format to be used in the CFD and imported into the software.
Figure 2 illustrates both the actual design of the GMT (upper images) as well as the simplified models using in the CFD
(lower images). For all simulations it was assumed that the telescope was pointed 30° off zenith.
Figure 2. Illustrations of the current GMT design (upper images) and simplified CFD model (lower images) for both the closed soffit
(left) and open soffit (right) configurations.
2.1.3 Enclosure
Similar to the telescope and optics model, two simplified versions of the enclosure were provided by GMTO for use in
these studies. The difference between these two versions is in the lower 9m of the enclosure, known as the soffit. By
reducing the diameter of the soffit from 61m (to be flush with the telescope platform) to 25m, the intent was to provide a
larger area for near-grade air to flow around, rather than into the enclosure. The left images in Figure 2 illustrates the
“closed” soffit configuration, and the “open” configuration is illustrated on the right.
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2.2 Mesh Generation
Once the study domain and geometry have been defined, one must then define the height above grade the domain should
include. In this case, a height 500m above the top of the leveled mesa was taken to be the top of the simulated domain.
Preliminary simulations showed that this height was far enough above the terrain to not influence the flow patterns (via
the “squeezing” of flow between the high points of the terrain and the boundary) while keeping the domain small enough
to simulate quickly. The resulting simulation domain was approximately 166,000,000 m³ in volume.
The next step in a CFD analysis is to subdivide the domain into the sub-volumes to be studied. Decreasing the size of the
cells improves accuracy of the simulation, however increasing the cell count leads to longer simulation times. A balance
must be struck to ensure appropriate results can be provided in a timely fashion. One approach to maintain this balance is
to increase the refinement near surfaces and volumes of interest. In these simulations, three regions were defined with
increasing levels of refinement. The three sub regions and their corresponding mesh conditions are summarized in Table
1 below.
Table 1. Mesh refinement region parameters.
Region
1
2
3
Description
Distance from
Enclosure
Far field approach flow
Near field approach flow
Within Enclosure
500m
50m
1m
Height Above
Mesa
130m
130m
62m
Maximum Cell
Size
6m
2m
0.5m
In addition to the volumetric controls on cell size, surface based controls were also employed to ensure good cell
resolution near the terrain, telescope, optics and enclosure. Cells on the telescope, and enclosure ranged in size from 525cm in order to properly resolve small features and corners. The terrain surface was meshed with a base resolution of
30 m to be consistent with the coarser data from the STRM topography and decreased in size approaching the telescope
based on the same size parameters used in the volumetric regions defined in Table 1. Because accurately representing the
boundary layer (i.e. the variation in airspeed near surfaces) is critical to this work, a so-called “prism layer” was
employed on the terrain boundary. This is a layer of cells which are made much thinner near a surface to better resolve
the boundary layer without requiring a large number of additional cells. In this case the height of the first cell near the
terrain ranged from 0.5m near the boundaries of the study domain, to 15 cm within 50m of the telescope, ensuring the
boundary layer is well resolved. Prism layers were also employed on the surfaces of the enclosure and optics. The
resulting meshes used in the simulations contained approximately 20 million cells.
2.3 Flow Simulation
2.3.1 Physics
Once the study domain has been discretized, the simulations of the flow field can then begin. The flow of fluids is
defined by the Navier-Stokes (NS) equations7. While analytical solutions of these equations do exist for simple
scenarios, in the general case approximations and simplifications are required. In the work presented here, we use a
Reynolds Averaged Navier-Stokes (RANS) solver. This approach simplifies the NS equations by time averaging them,
yielding a so-called “steady state” solution of the equations. This means that the predicted velocities represent an
“average” wind condition which does not include transient effects such as wind gusts or vortex shedding. A RANS
approach requires additional inputs in the form of parameters defining the turbulence of the flow field. We have used the
Menter shear stress transport (SST) k-Ω turbulence closure model8, which defines the turbulent kinetic energy and the
specific dissipation rate of turbulent eddies. Ambient air pressure was defined as the 50th percentile value (75.4863 kPa)
noted in the GMT Environmental Conditions report9. All other air properties were set as per the 1976 U.S. Standard
Atmosphere10 for an altitude of 2518 m above mean sea level, which is the approximate elevation of the leveled mesa.
The entire domain was also assumed to be isothermal with the air having a fixed density (0.95 kg/m³). Because these
studies compared the relative performance of each configuration and/or location of the enclosure to one another, as long
as the air properties remain consistent between the simulations, their precise values are not critical.
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Task 3 required additional physics to be included to account for the porous nature of the windscreen. Based on guidance
from the GMTO, the screen was modeled as a thin plate with evenly distributed circular holes laid out to achieve a 25%
open area. Based on this data, the pressure loss coefficient (or K-value) can be computed analytically using11. This Kvalue is then used in the simulations to model the pressure drop experienced by air flowing through the wind screen.
Equation 1 below defines the analytic relationship between pressure (P), density (ρ), velocity (V) and the K-value which
was employed in the simulations.
ΔP = 0.5KρV²
(1)
2.3.2 Boundary Conditions
Typical external CFD analyses use empirically derived formulae to define the incoming flow field based on either a
logarithmic or exponential change in velocity with height12. When working at heights less than 100m above local grade,
the logarithmic profile tends to be a better predictor of wind velocity and has the added advantages of being able to
model a wider variety of possible flow conditions (i.e. variable surface roughness and different atmospheric stability
conditions). It is for these reasons that the authors typically use a log wind profile rather than a power law in our work.
For complex locations like Cerro Las Campanas, no “typical” profile exists to define the wind conditions near the mesa.
Instead, the industry standard approach is to use a large domain so that the terrain itself shapes the flow field, rather than
an artificial profile. Thus the profiles set at the simulation boundaries were defined such that they represented an “open”
profile (i.e. a wind profile similar to what would be found at an area of relatively smooth terrain). In terms of the log-law
wind profile, this means a very low aerodynamic roughness length (z0) of 0.1m. For comparison, the corresponding
roughness parameter in a power law profile formulation (α) would be approximately 0.14. We also assumed a neutrally
stable atmosphere, in line with our isothermal assumption noted above.
As part of earlier work by the GMTO, a weather station was installed on the mesa which recorded wind and temperature
conditions at 1 minute increments for approximately 3 years. The 75th and 25th percentile mean wind speeds (10.7 m/s
and 4.5 m/s respectively) were extracted from this data; these were to be the target wind flow conditions for these
studies. A preliminary CFD model of the site was created without any planned GMT structures in place. The reference
velocity used to define the simulation boundary conditions was iteratively adjusted until the speeds at the approximate
location of the weather station on the mesa matched the measured 25th and 75th percentile speeds. These reference
values at the inlet were then used in all future simulations. An examination of the predicted wind profiles on the top of
the mesa (Figure 3) shows the characteristic “bulge” near local grade where flow is accelerated as it is driven over the
mountain top and also how the initial wind speed profile at the simulation boundary was shaped by the terrain.
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300
75th Percentile Speed (Boundary)
25th Percentile Speed (Boundary)
75th Percentile Speed (Mesa)
25th Percentile Speed (Mesa)
280
260
240
220
200
E
no 180
(r)
140
120
=
100
80
60
40
20
0
5
6
7
10
11
12
Wind Speed [m /s]
Figure 3. Predicted steady state wind speed profiles at the nominal location of GMT.
All simulations were conducted with the wind approaching from the primary wind direction (20° east of north) as per the
GMT Environmental Conditions report. This direction was chosen for this work because the wind blows from this
direction approximately 80% of the time, covering the vast majority of wind events which occur on site.
2.3.3 Solution Convergence
Solving the NS equations using CFD requires an iterative approach, meaning that one must also define when the
simulation has converged to a solution. Two methods are used to define convergence; the first is by monitoring the socalled “residuals” of the simulation. These can be thought of as a measure of imbalance in the solutions of each of the
variables of interest (velocity, pressure and turbulence parameters). The residual will never be exactly zero, but generally
speaking, the lower the residual, the more precise the solution. Residual values are normalized, and are expressed in
terms of their order of magnitude. A residual level of 1e-4 or smaller, is commonly considered to be converged, though
for complicated domains in non-academic settings, this is not always achievable. A secondary method to monitor
convergence is by tracking the predicted values of critical variables at selected locations within the study domain. In a
steady state analysis, the magnitude of a variable at a given point should converge to a fixed value. The size of the
variation of this value from one iteration to the next provides another indication of the convergence of the solution.
Monitoring the values of key parameters also provides a good cross check for the residuals, ensuring that the solver has
converged to a solution which makes physical sense. Since wind velocity is the key parameter for these studies, the
predicted velocities at several locations on the mesa were monitored and cross-checked against the residual history to
determine convergence.
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3.
CHARACTERIZATION OF THE EXISTING WIND MICROCLIMATE
As noted above, preliminary simulations were conducted without any GMT structures in place in order to determine the
boundary conditions required to achieve simulated wind conditions similar to those which were recorded on site. These
simulations also allowed for an understanding of the existing wind microclimate at Cerro Las Campanas.
Figure 4 illustrates steady-state wind speed contours and vectors for 1m and 5m above the mesa and indicates that the
natural differences in topography create variations in flow speed on the order of 1-2 m/s across the mesa. The gentler
slope on the eastern side of the mesa creates these slower areas, though it has little effect on steady-state wind
directionality. Similar variation of wind speeds are seen in the vertical wind profile as well. Areas with a steeper
approach path see increased flow acceleration over the mesa, compared to the areas with more gentle topography. This
acceleration of flow is a common feature in mountainous areas but the overall gentleness of the approach to Cerro Las
Campanas from the primary wind direction means the overall acceleration effect is reduced.
These findings spurred the definitions of the Task 2 simulations, to try to better understand the effect that these
variations have on the airflow in and around the enclosure.
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4.
ENCLOSURE PERFORMANCE
For each simulation, surfaces were defined for the main openings into the enclosure. Ten surfaces were defined along the
height of the primary opening, along with one for each row of vent openings. (Figure 5) The airflow through these
surfaces was integrated and the mass flow rate of air entering the enclosure computed. The overall and individual air
entrainment rates were then used to compare the relative performance of the enclosure with respect to the total amount of
air entraining into the enclosure and also the variation of that entrainment rate as a function of height and location on the
enclosure. Of particular interest is the amount of air entering the enclosure which approached from close to grade. This is
because near-grade air is more likely to have higher gradients in temperature and turbulence because of the proximity to
the ground.
Surface 1
Surface 4
Figure 5. Schematic diagram showing the sampling surfaces used to compute airflow into the enclosure.
4.1 Task 1 Observations
The aim of Task 1 was to assess the impact that the two soffit configurations have on airflow into and around the
enclosure. Eight simulations were conducted, so that the two soffit configurations could be compared under 3 different
enclosure orientations (headwind, right crosswind and tailwind), and 2 wind speeds. All simulations were conducted for
winds from the primary wind direction at the site (20° east of north) under a 75th percentile wind condition, with the
enclosure orientations defined relative to this direction, i.e. a “headwind” orientation implies that the enclosure’s main
opening is pointed into the wind, a “tailwind” orientation is a 180° rotation from the headwind position, etc. The
headwind orientation was also studied under the 25th percentile wind speed to gauge any influence that wind speed
would have on the airflow in and around the enclosure.
The simulation results showed that the closed soffit is creating what’s known as a recirculation zone. As the near-grade
flow crests the mesa, it impacts the soffit and is driven down and to either side in a swirling motion. (See Figure 6
below, which shows this phenomenon occurring for one of the cases.) This recirculation creates a region of higher air
pressure upstream of the telescope. This higher pressure slows the air which impacts it leading to lower air airflow rate
into the enclosure under the closed soffit condition across all pointing orientations, particularly at the lower openings,
generally on the order of 10%. While this effect is present in all pointing orientations the magnitude of the impact
appears to be proportional to the change in frontal area the soffit represents. Without the soffit this recirculation zone
does not form and thus the flow approaching the enclosure is moving faster and straighter than what is seen in the closed
soffit configurations. This faster moving flow leads to increased mass flow into and higher wind speeds within the
enclosure.
Proc. of SPIE Vol. 9911 991113-9
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Figure 6. Comparison of velocity fields under closed and open soffit configurations.
Figure 7 plots the overall mass flow rates into the enclosure for each pair of simulations. As one would expect the
headwind orientation experienced the highest mass flow rates into the enclosure, particularly closer to grade where the
flow is accelerated due to the topography. For a right crosswind enclosure orientation, the difference between the closed
and open soffit simulations is so small as to be effectively the same. In the cross wind orientations, the non-streamlined
nature of the opened main doors plays a large role in driving flow towards the enclosure. This effect dominates over the
effect of the recirculation zone. The tailwind enclosure orientations showed the least infiltration compared to the other
orientations for both cases, but the slight reduction in entrainment in the closed soffit configuration was also present.
Reducing the wind speed to the 25th percentile condition led to a roughly proportional decrease in overall mass flow of
approximately 40%. The lower wind speeds resulted in a slightly less intense recirculation zone which in turn resulted in
a smaller difference in mass flow rates between the closed and open soffit configurations at this speed.
Closed Soffit
8500
Open Soffit
8000
]500
]000
6500
6000
5500
5000
4500
4000
3500
3000
2500
2000
1500
1000
500
0
Headwind (15th Percentile Headwind (25th Percentile
Windspeed)
Windspeed)
Right Crosswind (15th
Percentile Windspeed)
Tailwind (15th Percentile
Windspeed)
Figure 7. Plot of overall mass flow into the enclosure for the Task 1 simulations.
Proc. of SPIE Vol. 9911 991113-10
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4.2 Task 2 Observations
A further 10 simulations were conducted for Task 2, to assess if natural variations in wind speeds on the mesa could be
taken advantage to reduce near-grade airflow into the enclosure, and also how the distance away from the mesa edge
influences the effects of the recirculation zone identified in Task 1.
Two alternate locations on the mesa were identified and are illustrated in Figure 8. Alternate 1 was positioned
approximately 90m east and 45m south of the nominal position (S 29.0478°, W 70.6836°). This placed the enclosure
between 15m and 35m from the edge of the mesa along the primary wind direction vector. Alternate 2 was positioned
160m east and 105m south of the nominal position, putting it between 19m and 33m from the edge of the mesa along the
primary wind vector. Alternate 1 puts the enclosure in zone of similar airspeeds as the nominal position, but closer to the
edge of the mesa. Alternate 2 is in an area identified during the preliminary simulations, where wind speeds were
naturally lower under the primary wind direction. All simulations were conducted under the 75th percentile wind speed
blowing from the primary wind direction.
Edge of Slope
10m Clearance
Nominal Position
45m
Alternate #1
Figure 8. Schematic drawing of nominal and alternate GMT positions.
The Task 2 simulations confirmed that the closed soffit recirculation zone identified in Task 1, and the reduction in neargrade mass flow it creates is independent of mesa location. However, moving the enclosure closer to the edge of the
mesa (Alternate Location 1), led to a slight decrease in size, and thus impact of the zone. Moving the enclosure further
from the mesa edge (Alternate Location 2) resulted in a recirculation zone of approximately the same size, indicating
that there is an upper limit to the size of the recirculation zone, and it is weakly dependent on the distance from the mesa
edge. Figure 9 illustrates the approaching flow and recirculation zones for a headwind orientation at each of the studied
locations. The approximate extent of the recirculation zone is outlined in red.
Proc. of SPIE Vol. 9911 991113-11
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eft
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Figure 9. Comparison of recirculation zones created under closed soffit configurations at the nominal and alternate locations.
Figure 10 plots the overall mass flow rates into the enclosure for the Task 2 simulations. Similar to the Task 1 results,
the closed soffit configuration results in approximately 10% less mass flow into the enclosure regardless of location or
enclosure orientation. The slightly slower wind speeds at Alternate Location 2 resulted in very slight (<5%) reductions in
overall mass flow under the headwind and right crosswind orientations compared to a more significant 10% reduction
under a tail wind orientation. This is due to geometric effects (the size of the openings for a headwind orientation, and
the open enclosure doors driving flow in the right crosswind orientation) dominating over the influence of the reduced
air speeds.
9000
Closed Soffit
8500
Open Soffit
8000
7500
7000
6500
Ñ
6000
Y
5500
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2000 1500 1000 -
500 -
0Headwind
Nominal
Headwind
Alt 1
Headwind
Alt 2
Tailwind
Nominal
Tailwind
Alt 1
Tailwind
Alt 2
Right Crosswind
Nominal
Figure 10. Overall air entrainment rates for the Task 2 simulations.
Proc. of SPIE Vol. 9911 991113-12
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Right Crosswind
Alt 2
4.3 Task 3 Observations
Having gained an understanding of how the soffit and location on the mesa influence air entrainment in isolation, the 13
simulations of Task 3 represent more realistic scenarios designed to assess how different configurations of the
windscreen and enclosure vents can further reduce the entrainment of near-grade air. Simulations were conducted for
two windscreen deployment heights, 22m and 33m (measured from the bottom of the observing floor) and three
enclosure venting configurations; all open, all closed, and a configuration which selectively closed the vents closest to
grade on the windward side of the enclosure, dubbed “VRCW2” (Figure 11). Simulations were conducted at the nominal
and Alternate 2 locations on the mesa, as well as at both the 75th and 25th percentile wind speeds. Due to the focus on
windscreen and vent performance only 2 enclosure orientations were simulated, a headwind orientation and a “right 45°
crosswind” orientation, i.e. the main enclosure opening is rotated 45° counterclockwise (when viewed from above) from
the primary wind direction. This orientation was chosen, so that both the vents and windscreen would be directly
challenged by oncoming winds.
1
20
N
r-
155°
245°
UL
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L3
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- CLOSED VENTS
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"VRCW2" Configuration
Figure 11. Right 45 cross wind enclosure and venting schematic.
As one would expect, for the headwind configuration, including the windscreen leads to a dramatic reduction in the total
airflow through the enclosure. The 33m and 22m wind screens resulted in 65% and 44% reductions in overall inflow
respectively. As with the previous tasks, a closed soffit results in slightly lower mass flow rates for most simulations,
however the differences are now so slight that we can only say that they would perform effectively the same. Geometric
effects similarly dominate over the effects of the recirculation zone and local wind speed reductions for the right 45°
crosswind orientation. Figure 12 plots the overall mass flows for the closed and open soffit configurations for each of the
simulations in this task.
Proc. of SPIE Vol. 9911 991113-13
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8500
Closed Soffit
8000
Open Soffit
7500
7000
6500
6000
Cu0
r.. 5500
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u-
5000
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TS
4500
4000
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5
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2000
1500
1000
500
75% Headwind
No Windscreen
75% Headwind
22m Windscreen
75% Headwind
33m Windscreen
25% Headwind
33m Windscreen
75% Headwind
33m Windscreen
All Vents Closed
75% Headwind
33m Windscreen
Alt 2
75% Right 45 X -Wind Alt 2 75% Right 45 X -Wind Alt 2
33m Windscreen
33m Windscreen
VRCW2 Vent Config
Figure 12. Overall air entrainment rates for the Task 3 simulations.
While the wind screen proved highly effective at reducing air entrainment, the low pressure zone it created within the
enclosure had the effect of “pulling” the diverted flow down, towards the open top of the enclosure. The 33m tall
windscreen proved to be tall enough that the diverted air was able to clear the enclosure regardless of wind speed.
However, when the windscreen was modeled as 22m tall, the air flow was not directed high enough and impacted the
rear of the enclosure. This jet of air is then accelerated down the rear of the enclosure to the floor, in a phenomenon
known as “downwashing”. This flow pattern is clearly visible in Figure 13 comparing the wind flow profiles for each of
the windscreen heights for a headwind orientation at a 75th percentile wind condition.
iI{{Il
Proc. of SPIE Vol. 9911 991113-14
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/1/1/11////////////////
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Figure 13. Air diverted by the 22m wind screen impacts the rear of the enclosure and accelerates down.
The value of a good degree of control over the enclosure vents was also apparent in this round of simulations. Under the
right 45° crosswind case, air was seen to accelerate though any open vents. In the case of the lower two rows of vents,
this accelerated air directly impinged on the primary mirrors at speeds on the order of 10 m/s. Closing the vent openings
which are directly exposed to the primary wind direction eliminated these “jets”, reducing the wind speeds by
approximately 50% in the vicinity of the primary mirrors, while still allowing significant airflow to help effective
flushing of the enclosure. Figure 14 plots the wind speed and direction values for a plane, normal to the assumed
telescope pointing zenith, offset 1m from the vertex of the primary mirrors for the above described cases.
S
_----\
-,
\
Figure 14. Wind speed contours within the enclosure with all vents open (left) and with selected vents closed (right).
As these simulations were the most realistic of the three tasks, additional post processing was undertaken to estimate
how close the air entering the enclosure would get to grade during its approach. This was approximated by probing the
streamlines emanating from the sampling surfaces along their length and computing the distance from a given point on
the streamline to the terrain (Figure 15). The minimum distance from the terrain along the final 1000m of the
streamlines’ approach to the enclosure was then computed, using each of the sample surfaces as the starting point.
Approximately 400 streamlines were used at each surface to ensure sufficient coverage. This analysis showed that the
closed soffit resulted in airflow that approached from closer to grade. The proximity to grade slows the flow and results
in the lower inflow rates seen throughout this project. Conversely, the open soffit cases had faster moving air entering
the enclosure which remained further from the terrain. This effect was consistent regardless of windscreen height wind
speed and location on the mesa. The difference in minimum height between these cases was generally less than 5m and
due to transient effects not captured in these simulations the streamlines’ approach may be different in reality. This
analysis also highlighted the significant increase in local turbulence under the right 45° crosswind orientation due to the
open main enclosure doors. This will act to increase the mixing of the air in the immediate upwind vicinity of the
enclosure as well as within the enclosure itself. Mixing within the enclosure is likely a positive feature as it will help
disperse any heat which may be building up, as long as the air can then escape. The mixing upwind of the enclosure will
likely have a similar effect which, depending on temperature gradients may be a positive or a negative feature.
Proc. of SPIE Vol. 9911 991113-15
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30.0
34.0
38.0
42.0
Distance From Terrain (m)
50.0
54.0
46.0
58.0
62.0
66.0
70.0
Figure 15: Sample streamlines colored by distance from terrain.
5.
CONCLUSIONS
As ground based telescopes increase in size and image quality requirements tighten, understanding the impacts of
ambient wind conditions on the telescope becomes more important. For extremely large telescopes, managing these
impacts can be critical. The interaction between a telescope, its enclosure and the environment are complex and can
often have non-intuitive consequences so an early understanding of those interactions avoids the need for costly
modifications late in design or post-construction. The preliminary computational modeling presented here identified key
aerodynamic characteristics of both the Cerro Las Campanas site and the GMT enclosure design:
 The closed soffit configuration leads to reduced mass flow into the enclosure through the creation of a
recirculation zone on the windward side of the enclosure compared to the open soffit case, regardless of
enclosure orientation and position on the mesa.
 While areas of reduced wind speeds on the mesa were identified, they do not have a major impact on the rate on
air entrainment, nor on how close to grade the flow approaches from.
 As expected the windscreen significantly reduces the amount of air entering the enclosure. This effect
dominates over the effect of the closed/open soffit and position on the mesa. However the deflected air may reenter the enclosure from above at certain deployment heights.
 The non-streamlined nature of the opened enclosure doors leads to increased turbulence generation for cross
wind orientations.
 Any venting schemes should be optimized to ensure that vents aligned with the wind direction can be
selectively closed during high speed wind events, while allowing the others to remain open.
6.
FUTURE WORK
The authors plan to continue our investigations into how to best manage the interactions between the enclosure and the
natural climate conditions at the site. This includes more detailed computational modeling of wind conditions, solar
modeling to understand daytime heat gains and wind tunnel testing to gather wind pressure data to understand the static
and dynamic loading on the enclosure and optical elements.
Proc. of SPIE Vol. 9911 991113-16
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ACKNOWLEDGEMENTS
The authors would like to thank all those who contributed to the development and publication of this work. In particular
the authors gratefully acknowledge the work of Mike Carl and Dianthé van Weerden at RWDI.
This work has been supported by the GMTO Corporation, a non-profit organization operated on behalf of an
international consortium of universities and institutions: Astronomy Australia Ltd, the Australian National University,
the Carnegie Institution for Science, Harvard University, the Korea Astronomy and Space Science Institute, the São
Paulo Research Foundation, the Smithsonian Institution, the University of Texas at Austin, Texas A&M University, the
University of Arizona, and the University of Chicago.
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