A viewgraph presentation on Computational Fluid Dynamic (CFD) Simulation of Space Shuttle Launch Vehicle (SSLV) ascent and debris transport analysis is shown. The topics include: 1) CFD simulations of the Space Shuttle Launch Vehicle ascent; 2) Debris transport analysis; 3) Debris aerodynamic modeling; and 4) Other applications

Aftosmis, Michael

Chan, William

Gomez, Ray

Murman, Scott

Rogers, Stuart

Stuart, Phil

Vicker, Darby

NASA Technical Reports Server

1Simulations of SSLV Ascent and Debris 
Transport
Space Shuttle Return-To-Flight
Seminar in Fluid Mechanics
Stanford University
October 11, 2005
Stuart Rogers
Michael Aftosmis
Scott Murman
William Chan
NASA Advanced 
Supercomputing Division
NASA Ames Research 
Center 
Reynaldo Gomez
Darby Vicker
Phil Stuart
Jim Greathouse
NASA Johnson 
Space Center 
https://ntrs.nasa.gov/search.jsp?R=20060017031 2019-08-29T21:50:27+00:00Z
2Outline
CFD simulations of 
the Space Shuttle 
Launch Vehicle 
ascent
Debris transport 
analysis
Debris aerodynamic 
modeling
3CFD Analysis of SSLV Ascent
Motivation
 Predict air-loads on the redesigned External Tank
 Roll maneuver air-loads
 Debris analysis flow-fields
 3% Shuttle wind-tunnel test loads prediction
Approach 
 Overflow RANS flow solver
¾ Central-differencing + scalar dissipation, 2nd order
¾ Diagonalized approximate factorization implicit scheme
¾ Spalart-Allmaras turbulence model
¾ Multi-level parallelism, scalable to hundreds of CPUs
¾ Use full-multi-grid sequencing to get started
 Overset (Chimera) gridding approach
¾ Developed an automated grid-generation capability
¾ Gimble angles for SSME and SRB nozzles
¾ Control surface deflections
¾ Plume boundary-condition generation for SSMEs and SRBs
 Validation with 3% WT model: Cp, PSP, PIV 
4CFD Analysis of SSLV Ascent
Results
Over 400 Overflow solutions run for Return-to-
Flight
New grids generated for each ascent condition
¾ 2 hours on 32 Itanium-2 CPUs
¾ 30 to 50 million grid points each
Average of ~1000 Itanium-2 CPU hrs / solution
¾~20 hours of wallclock time running on 64 Itanium-2 CPUs
¾Never converges to a steady-state: aft end of ET, 
attachment hardware, plumes, etc
¾Typically run for ~10,000 iterations
5Geometry Details
Old Grid System
New Grids with 
Bipod Ramp New Grids without Bipod Ramp
Control Surface and 
nozzle deflections
6 The NAS houses the 
world’s fastest 
operational 
supercomputer 
providing 61 teraflops
of compute capability 
to the NASA user 
community
 Columbia is a 20-node 
supercomputer built on 
512-processor nodes
 Columbia is the largest 
SGI system in the 
world with over 10,000 
Intel Itanium2 
processors
“Columbia”: World Class Supercomputing
7IA-700 Wind Tunnel Tests
ARC 9x7 Unitary, AEDC 16T
8IA-700 Transonic Surface Cp –
Orbiter Wing
9IA-700 Transonic Surface Cp –
ET
Stagnation pressure is artificially high in the 
PSP data because of poor camera angles
10
Integrated Wing Loads
J. Greathouse/JSC
11
IA-700 Transonic PSP vs. CFD
 Mach = 1.55, α = 0°, β = 0°
¾ Lighting & camera angles 
reduced measurement quality 
in ET nose and Orbiter lower 
surface regions
12
ARC 9 × 7 Mach 2.5 PIV Comparison
OVERFLOW CFD
Cart3D CFD
Cart3D solution – M. Aftosmis/ARC
OVERFLOW solution – S. Rogers/ARC
Optical 
distortion 
from ET 
bow shock 
caused PIV 
inaccuracies
OVERFLOW - PIV
Cart3D - PIV
Window glare 
results in artificial 
low velocity 
regions
13
Post STS-114 Solutions
Addition of Ice/Frost Ramps
Mach = 1.55
Alpha = -3.5 deg
Beta = -0.4 deg
MET = 61 sec
Alt = 39,600 ft
Cp
14
Debris Impact Assessment Process
Damage Assessment
Debris Source
Debris Transport Analysis
15
Debris Transport Process Overview
Debris Source/Outputs
¾Material properties
¾Installed geometry
¾Likely debris shapes
¾Failure mechanism, initial 
conditions
DTA Inputs
¾Freestream conditions
¾CFD-based flowfield
¾Debris aerodynamic 
models
¾Vehicle Geometry
DTA Environment
¾Impact location, mass, 
velocity, incidence angle
¾Rotation rate
Element Impact Capability
¾Material properties
¾Installed geometry
¾Impact tolerance
¾Damage tolerance
16
Debris Transport
 Ballistic debris integration:
¾ Steady-state CFD flowfield
¾ Integrate motion of point-mass subject to drag 
force due to relative local wind vector at current 
location in the flowfield
¾ Neglects effect of cross-range dispersions due to 
lift
 Debris Transport software development:
¾ Developed debris-drag models using Cart3D 6-
DOF unsteady simulations
¾ Significant improvements to debris-trajectory 
computations
¾ Wrote software for debris collision and proximity 
detection
¾ Wrote general purpose sorting and filtering of 
collision output
 Millions of debris trajectories have been computed and 
analyzed
17
Debris Code Analysis Options
Probabilistic
Zero Lift Trajectory + Crossrange Cone
Deterministic
Zero Lift Trajectory + Range of Initial Velocities
LH2 Flange Foam
Mass = 0.023 lbmρfoam = 2.34 lb/ft3
Vpop-off = 113 ft/sec
Cross range = f(shape, mass, 
rotation rate/orientation)
18
19
Time History
Th
ru
st
 P
an
el
 F
oa
m
 D
eb
ris
20
Debris Aerodynamics Modeling
 Debris Transport currently requires two aerodynamic models for 
each type of debris to be analyzed:
¾ Drag model : determines impact velocity
¾ Cross-range model : determines impact locations
 Impractical to determine model parameters using experimental 
techniques (too costly, time consuming, restricted to simple shapes).
 Use validated CFD methods (cheap, rapid turnaround, not restricted 
by geometry shapes).
 Compute hundreds of 6-DOF trajectories using a  Monte-Carlo 
approach (vary shape, orientation, rotation rate) and model the 
resulting behavior.
 Have developed drag and cross range models for:
¾ Tumbling cube
¾ Foam divots (based on a conical frustum model)
¾ Ablator material
¾ Hemisphere, to model ice balls
21
Cart3D
Automated mesh 
generation from CAD
Partitioned on the fly for 
any number of CPUs
Solves Euler equations:
¾Unstructured Cartesian cells
¾Finite-volume formulation
¾Multi-grid acceleration
¾Shared-memory 
parallelization w/ OpenMP
¾4.5 million cells, 15 levels of 
refinement
Machín and Murman
Date Page 22
Drag Modeling
• Drag modeling uses 6-DOF data
• Kinetic energy (damage potential) used as “fitness function”
• Drag model validated against Ames GDF range data
• Drag models created Feb. ‘04
• These models were used in the design of all the validation experiments
Drag Kinetic Energy
23
Foam Cross-Range Model
 Debris can generate aerodynamic “lift” in arbitrary 
direction during trajectory (referred to as crossrange).
 This effect is modeled in a post-processing step.
 Crossrange cone applied to zero-lift debris trajectories 
from ballistic code to determine possible impact points.
Drag Model Leads to 
“Zero-Lift Line”
Crossrange Model Provides 
all possible impact locations 
24
Foam Cross-Range Data
 Data from Monte-
Carlo CFD 6-DOF 
trajectories used to 
develop crossrange
cone.
 Several shapes 
used to develop 
crossrange
behavior.
 Results can be 
scaled to arbitrary-
sized debris.
 A probability can be 
assigned to any 
location within 
crossrange cone. 
25
Validation With Gun Development 
Facility (GDF) Data
 There are two aspects to the validation effort:
¾ Validate the ability of the Cart3D code to 
simulate a 6-DOF foam trajectory by direct 
comparison against range data. (validation 
of CFD method)
¾ Validate the foam drag and cross-range 
models using the range data. (validation of 
models)
26
Ames Gun Development Facility
1¢
Sabot and Projectile
27
6-DOF Method Validation
Ames GDF ballistic data Distance vs Time
Mach 2.51, 6000 g’s deceleration
Axial Distance (Drag)
28
6-DOF Method Validation
Ames GDF ballistic data Pitch/Yaw vs Time
up
down
righti tleftl ft
pitch axis
itc
 
is
Shot 5, Tripped
Shot 3, Untripped
29
Drag Model Validation
 Low oscillation trajectory - shot 2, Mach = 3.00
30
Drag Model Validation
 Medium oscillation trajectory - shot 7, Mach = 2.81
31
Drag Model Validation
 High oscillation trajectory - shot 6, Mach = 2.46
Machín and Murman
Date Page 32
Crossrange Validation
• Cart3D 6-dof predictions bound ballistic data
• CFD (all) represents several hundred CFD trajectories generated from offset 
C.G. and asymmetric models
• CFD data is used in dprox code to determine potential impact cone
• Even mild asymmetry generates strong crossrange
33
CUBRC Setup
View looking back upstream
Divot
 Traje
ctory
34
Run 9
5.1 inch divot  AR 5.4
Mach 3.5  Q 706 psf
f93
f90
f95f96 f94
f91
f92
What the two pieces looked like several feet down stream
f100
f103
35
Run 12
7.4 inch divot
AR 7.8
Mach 3.5
Q 729 psf
Run12-h1
80% fs, 
2x(2xbright,1xcontrast)
f73
f74
f72
36
DFRC F-15B
Flight Test Fixture
BX-265 
foam sheets
37
Results from F-15B Testing
 Conducted 9 flights using BX-265 foam sheets
¾ Total of 38 divots liberated 
 All 31 of the supersonic divots ‘trimmed’
¾ Of these, 30 of 31 rotated leading edge away from the sheet 
trimming with the small diameter facing forward 
o Divot C at Mach 1.6 and 850 psf passed through this first trim point and 
trimmed with the large diameter forward (only divot to behave in this 
fashion)
¾ 2 of the 5 subsonic divots tumbled after one oscillation
 36 divots survived the aerodynamic deceleration associated with 
being ejected into the flow field
¾ Two of the three divots generated using the lowest successful 
ejection pressure rotated back into the sheet
o As a result of re-contact with the sheet, the divots fractured into several 
pieces
¾ Ejection pressure did not appear to affect divot geometry
o All divots tended to be slightly smaller than predicted (using 30° angle 
assumption)
38
1-dof Comparison to F-15B 
Data
0
0.5
1
1.5
2
2.5
3
0 0.001 0.002 0.003 0.004 0.005 0.006
proc277c_out.txt
X nominal 1-dof (ft)
X static 1-dof (ft)
X tumble 1-dof (ft)
X F-15B (ft)
d
o
w
n
r
a
n
g
e
 
d
i
s
t
a
n
c
e
 
(
f
t
)
time(s)
39
STS-114 Ice/Frost Ramp Debris Event
Computed and Enhanced Video Trajectories
Mass = 0.03 lbm, 30 ft/sec pop-off velocity
40
Trajectory, 0.03 lbm
30 ft/sec pop-off velocity
41
Mass=0.03 lbm Trajectories
0 – 10 ft/sec pop-off velocity
42
LH2 PAL Ramp Foam Debris
 LH2 PAL ramp release conditions at SRB Sep +5 sec
¾ Mach=4.19, Qbar=19.5, α = 1.23 deg, β = -0.87 deg
Mass estimated ~= 0.98 lbm
 BX-265 Foam density = 2.34 pcf
43
Qbar adjusted to 20 psf
44
Mass=1.0 lbm Trajectories
Only outer edge of cone intersects wing:
low probability of hitting wing
45
Concluding Remarks
CFD simulations of SSLV ascent have become a 
value data tool for the program
¾Significant computational and experimental validation 
efforts
Deterministic debris transport simulation has 
been used to quantify the debris environment 
during ascent
¾Being extended to reentry cases
Probabilistic debris simulation capability under 
development, significantly aided by CFD 
simulations


Simulations of SSLV Ascent and Debris Transport

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