Initial computational fluid dynamics modeling of the Giant … · 2017-02-02 · Initial computational fluid dynamics modeling of the Giant Magellan Telescope site and enclosure Ryan

Initial computational fluid dynamics modeling of the Giant Magellan

Telescope site and enclosure Ryan Danks*

a, William Smeaton

a, Bruce Bigelow

b, William Burgett

b

aRowan, Williams, Davies & Irwin Inc., 600 Southgate Dr., Guelph, ON, Canada, N1G 3W6

bGMTO 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 aero-

thermal environment in terms of image quality.

Keywords: Computational Fluid Dynamics, site microclimate, enclosure design, telescope siting

1. INTRODUCTION

The Giant Magellan Telescope1 (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 telescope2,3

.

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

Downloaded From: http://proceedings.spiedigitallibrary.org/ on 02/01/2017 Terms of Use: http://spiedigitallibrary.org/ss/termsofuse.aspx

1m Contour Lines 5m Contour Lines

1110m Contour Lines in 1 Arc-Second (30m) DigitalElevation Data

2. COMPUTATIONAL FLUID DYNAMICS APPROACH

A 3D computational model was developed using the commercial CFD package StarCCM+ v9.065. 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.

Figure 1. Topographic resolution of simulated terrain.



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.



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 Description Distance from

Enclosure

Height Above

Mesa

Maximum Cell

Size 1 Far field approach flow 500m 130m 6m

2 Near field approach flow 50m 130m 2m

3 Within Enclosure 1m 62m 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 5-

25cm 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.



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 K-

value 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.



300

280

260

240

220

200E

no 180(r)

75th Percentile Speed (Boundary)25th Percentile Speed (Boundary)75th Percentile Speed (Mesa)25th Percentile Speed (Mesa)

140

120= 100

80

60

40

20

05 6 7

Wind Speed [m /s]10 11 12

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 so-

called “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.



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.



f 1

ft fl ! 1 ! f f I !` T T .,7 T 7 T It

1

1 1 f o ,

s1 ' o s9, ', 'ss o s 0.,1 1 1

1 1 1 1 1/ 1 1 1 t t T¡s/u1 apí7w61ryv ,ti!,o¡áA 1 1 t f 1 f f 1 1 1 1 } 1 1 1 1 1 1 f i i í 1 t 1

1{ 1 1 1/ f; 1 f f 1 1 1 1 1 1 1

1 , ' 1 1 f 1 1 1 1 1 1 1 f t / f 1 / f l 1 1 1 1 1 1 1 1 1 f

1 f 1 1 1 11 1 1 1 f r` t 1 1 f/ 1 f f f 1 1 1 1 1 f 1 1 t 1 r f " f 1 P 1 1 1 1 1 1 t t f 1 i 1 1 1 1/ 1 1 1 1 1 1 1

1 1 f 1 1 1 1 t / 1 / / / 1 f í f 1 1 1 f f t t t t t

f f f 1 11 4 11 f f f/ f f 1 1 f` 1 f f f f,' t t t t

f r } i f 1 1 1 1 1 t 1 f f 1 1' f f 1 f f ; f t t t t

1 f 1 1 r 1 1 1 ! t f,{ 1 f f 1 t f 1 t/ 1; t t 1 t,I

1; f 1 f f r 1 1' ;' t t 1 / i f i' t 1 i 1 T f t ; t 1 f 1 1; 1 1 1 1 1.1 > f t 1 1 f i ` r-1t- f 1, f fi 1! } 1} 1 1 1 1 1 1 1;; I t 1 f f 1 f;; t f 1 1 1 1 1

1 f t 1 1''' r f;

f i

1 f 1'r. f r

1 f f 1 f 1 1 r 1 f 1 1 f;> t r f f 1 ; r t.

,' ,' 1 f 1 1 1/ 1 1 i 1 t 1 1 1 1 t f f f t ,` Y

1 1 1 1 1 1 1 f f 1 f f 1 / 1 , 1 1 1 1 1 f r r r 1 7 7

1 1 1 1 1 1 1 1 f-- 1 1 1 1 1 í i 1 1 f 1 r Ì .

f 1 / 1 1 / f 1 1 / 1 f 1 1 r t -1-- ; r f f r f r

1 1 1 1 f f 1 1 1 1 1 1 i r I t 1 1 ; f i; i 1

r t 1

1 1 1 1 1 1 1 1 1 1 1/ f 1 1; f; i; f 1 f 1 1 t i; . t 1 1 1 f 1 1 1 1 f 1 1; f i f 1 1 1 1 1 1 qe tug 1 1 1

1 1

1 1

I 1 1 1 1 f 1 1 1 1 1 1 1 1 1 1 1 1

1

f ' fof 1 1s1 /

/ /

ff 11 11 11 1

/

1

1

i

1

1

1

, I , , 4 4 4 , , ' 1 1

i o., i i 5.J ' i o.0_, i i s.§, o-S ' s'b o'Y) 1

1 1 1 1 /0/up apnnuyy ,lNoofán i t 1 t t 1 1 1 1 t i l 1 / / 1 / 1 I 1 i 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 / 1 1 Ì 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1--f1 1 1 1 J 1 / i 1 1 1 1 f 1 1 1 1 1 í 1 1

1 1 1 f 1 1 1 1 1 1 i T f 1 r' 1 1 1 1 1 1 1 1 1

f i 1 1 1 1 1; f i i i 1 1 1 1 1 1 1 1 1

li ' 1 i 1 1 1 f i t 1 1 1 1 1 1 1 1 1 1

f 1 1- i 1 1 1 1 1 f f 1 1 1 1 1 i 1 1 1 1 i i 1)---i 1 / 1 11 1 ; f 1 1 f 1 f " 1 1 1 1 1 1

i l l 1 1 ' 1 1 1; 1 i r 1 1 f 1 i t t 1 t

1 f 1; 1> 1 1 1 1 1 f 1 ì f f 1 t t i i

1 1 1 ; 1 > 1 r / ; f 1 1 1 i f 1 f z - f f t i t

1 1 1 1 1 1 1 1; ' f f 1 t 1 1 1 1 11 t -t-- J f 1 f it" 1 1 1`i f 1 f t; t 1 1 11 1 1 1 1;; 1 1 1 i. l i I t 1 1 1

1 1 1 1 1 i ' r' 1 1 f 1 t 1

1 1 1 1 1

1 1 1 1

1 1

1 1 1 1 1 1 1 f qe w

1 1/ 1 1 1

/ /

Figure 4. Predicted wind conditions on the mesa under a 75th percentile wind speed from the primary wind direction.

1m above mesa

5m above mesa



Surface 1

Surface 4

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.

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.



/ !r !

r

i, .,

.

., .., .

1

/.

.

.

.

.

.

.,.

lr.

.

.

.

//i

///

.,.//!

i.t

//

.

..

.

/ . i \ \/ \ .

- - -. .

,'`

!

- 1

\ ' i .\ I}

ry1111 .\,.f N'

\t.

_.

\t\-

.N

\\\-

..._

\ \\ \N \\ \\ \\

5. V5. .. .,\N \. .. \. .N N

\ .\ \N .\ \\ \\ \5 5\ .. \\\ \. .\. \N N

. \\ \\ \. \\ \\ \5 5. .N \\ \N. \N \N N. \N N

\ `.\ N.\ \\ \\ '.\ \5. \. \\ \\ \. \N N. .N N. . . _ _ .

. N. N. N. N. N. N NN. N..

N. `. \ 1 1

`. `. 1 1 \ O

*pV.5"-

ti-'.,,,,,,,{{{{,,i`!.6$k

\ \ \ \ \ -. N. N.\ \ \ \ `. -. N N.S. \ \ \ `. `. N N\ \ \ \ \ `. -. N.\ \ \ \ \ \ -. N.\ \ \ \ \ \ \ N.\ \ \ \ \ `. 1 `.\ \ \ \ \ \ \ \. \ \ \ \ \ \ \. . \ \ \ \ \ \ '\ N \ \ \ \ \ \. . \ \ \ \ \ \. . \ \ \ \ . \ '

i. . N \ \ \ \ \ \. \ \ \ \ 1 \. . \ \ \ '. 1 \ '\ \ 1 \ \ '\. \ . \ \ \ \. \ \ '\ \ '\ \\ \\ \\ \\ \ tA 1 \

V V

7.

I , ._ ..

J- N.r - - ...

. . .. .......... ...

Closed Soffit .-.-.-.-.-.. \ '. '. \ N.-. -. N. N. N. N. N. -. -. N. N. N. N. N. `+.

\ N N ....N N N N N NN N

. \ \-y N \

N

.. .. .. .. .. .. .. .. \ N1. .. \ N. .. \ .. N. .. N. N. ti

Open Soffit

8500

8000

]500

]000

6500

6000

5500

5000

4500

4000

3500

3000

2500

2000

1500

1000

500

0

Closed Soffit Open Soffit

Headwind (15th Percentile Headwind (25th Percentile Right Crosswind (15th Tailwind (15th PercentileWindspeed) Windspeed) Percentile Windspeed) Windspeed)

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.

Figure 7. Plot of overall mass flow into the enclosure for the Task 1 simulations.

Proc. of SPIE Vol. 9911 991113-10


Edge of Slope

10m Clearance

Nominal Position

45m

Alternate #1


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.

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 near-

grade 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


/f/ll/l///l///////////f//////I////l//

l/////l////////////////I//I/II///////

!//I//l//ll//////////I///I//////////I

/I/ //l/l////////////////////////'/

fll/lll/Il/I///I//II//////////////

fl/////////////////////II///////

/////////////////I////I

ti,,,,,,,//////////////////////

ttttt tf

////l//////////////I////////////////.-

lr/l/ll/fI///////I//I/I////////////

r////////r///////////////////////

r///////////////////////////////

r/

I/////////////////// /`'

r// ////////// //

//// I

///////////////////////I.

///////

////'

'

II///rI/IRA

1////

îYT

7.f//////////

/l///l//--

r//ll/ll//

//////ï

/7/

r

:II/////I/I/I//I//I /

////////////i'

////////////////I/I///////,

I////////////////

////////////////I//

//I/////I/I////////

////"//""/////..4

... ..

".;r

.Fi..

/eft

.....,

...

9000

8500

8000

7500

7000

6500

Ñ6000

Y5500

O

N 5000tAf0

2 4500

oH 4000

=.1), 3500vGJ 3000LO.

2500

2000 -

1500 -

1000 -

500 -0 -

Headwind Headwind HeadwindNominal Alt 1 Alt 2

Closed Soffit

Open Soffit

Tailwind Tailwind Tailwind Right Crosswind Right CrosswindNominal Alt 1 Alt 2 Nominal Alt 2

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.

Figure 10. Overall air entrainment rates for the Task 2 simulations.

Proc. of SPIE Vol. 9911 991113-12


1

N

20

L4

L3

L2

L1

r- UL

245°

LL

155°

LR

e5°

UR

- I CDeveloped Partial Elevation

"VRCW2" Configuration

- OPEN VENTS

- CLOSED VENTS


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.

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


8500

8000

7500

7000

6500

6000

Cu0

r.. 5500

0u-vs

4500

2R 4000

5000

TS014,U 30005tLCL 2500

3500

2000

1500

1000

500

Closed Soffit

Open Soffit

75% HeadwindNo Windscreen

75% Headwind33m Windscreen



75% Headwind33m WindscreenAll Vents Closed


Alt 2

75% Right 45 X -Wind Alt 2 75% Right 45 X -Wind Alt 233m Windscreen 33m Windscreen

VRCW2 Vent Config

/1/1/11//////////////////",///////////////////

//////////////1///////////////////!/////////J//1///1/lfii/////r////// /

iI{{Il \\\i/////iii////,{

////r..,_!

.O

mV

..

\ \ \ \ \ \ 1 \//ill/ /////////////

//./////////////////////,i

^///////////////////////////./1///;. / / //////////Ji:'..

/Iiir-.- .i-:

l

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.

Figure 13. Air diverted by the 22m wind screen impacts the rear of the enclosure and accelerates down.

Proc. of SPIE Vol. 9911 991113-14


S

_-----\-,\

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.

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


Distance From Terrain (m)

30.0 34.0 38.0 42.0 46.0 50.0 54.0 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 re-

enter 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


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.

REFERENCES

[1] McCarthy, P. J., Fanson, J. L., Bernstein, R. A., “Overview and status of the Giant Magellan Telescope Project” in

[Ground-based and Airborne Telescopes VI], Proc. SPIE, Preprint, (2016).

[2] Marchiori, G., De Lorenzi, S. Busatta, A., “The E-ELT project: the dome design status” in [Ground-based and

Airborne Telescopes IV] Proc. SPIE 8444, (2012).

[3] Vogiatzis, K., Thompson, H. A., “On the precision of aero-thermal simulations for TMT” in [Astronominal

Telescopes and Instrumentation], Proc. SPIE, Preprint, (2016).

[4] Ladd, J. A., et al., “Site and enclosure CFD modeling and analysis for the Giant Magellan Telescope” in

[Astronominal Telescopes and Instrumentation], Proc. SPIE, Preprint, (2016).

[5] CD-Adapco, STAR-CCM+ Vers. 9.06011, CD-Adapco, 2015.

[6] NASA, Shuttle Radar Topography Mission Database Vers. 2.1, National Aeronautics and Space Administration

(2015).

[7] NASA, “3 Dimensional Unsteady Navier-Stokes Equations”, NASA, 5 May, 2015,

<http://www.grc.nasa.gov/WWW/k-12/airplane/nseqs.htmll> (26 April 2016).

[8] Menter, F. R., "Two-equation eddy-viscosity turbulence modeling for engineering applications," AIAA Journal,

32(8), 1598-1605 (1994).

[9] Hardie, K, and Trancho, G., "GMT Environmental Conditions”, GMTO Corp., Pasadena (2015).

[10] NASA, U.S. Standard Atmosphere, Washington DC : U.S. Government Printing Office, Washington D.C. (1976).

[11] Idelchick, I. E., [Handbook of Hydraulic Resistance], Begell House Inc., New York, 516-517 (1996).

[12] Stull, R. B., [An Introduction to Boundary Layer Meteorology], Springer Netherlands, Rotterdam, (1988).

Proc. of SPIE Vol. 9911 991113-17


Documents

Initial computational fluid dynamics modeling of the Giant … · 2017-02-02 · Initial computational fluid dynamics modeling of the Giant Magellan Telescope site and enclosure Ryan