17th Aesi CFD Symposium, August 11-12, 2015, Bangalore, India
Estimation of Surface and Base Pressure Coefficient over Launch Vehicle
with Reverse Flow Nozzles using CFD
J Sreenivasulu, Sundeep Epuri and Dr. Patil M M
Vikram Sarabhai Space Center, Thiruvananthapuram, India.
(sreenivas_j@vssc.gov.in or jksnivas@gmail.com)
ABSTRACT
CFD analysis has been carried out over Launch vehicle with reverse flow nozzles configuration. The viscous
turbulent flow studies have been carried out to generate surface pressure distribution and base pressure at various
Mach numbers, at various angles of attack under jet-on and jet-off conditions using the PARAS-3D code. The present
CFD solver is validated using the pressure distribution of LAS data obtained through Wind Tunnel experiments. The
CFD data is matching good with experimental data under jet-on conditions. This paper gives the variation of surface
pressure distribution at the base of Launch vehicle with reverse flow nozzles configuration.
Key Words: Aerodynamic coefficients, PARAS-3D, Jet-off, Jet-on
NOMENCLATURE
CFD
=
CFL
=
LAS
=
LES
=
WT
=
α/AOA =

=
CN
=
CP
=
CPb
=
CT
=
M
=
Pj / Pinf =
TVD
Computational Fluid Dynamics
Courant–Friedrichs–Lewy condition
Launch Abort System
Launch Escape System
Wind Tunnel
Angle of attack, deg
Sideslip angle, deg
Normal force coefficient
Coefficient of pressure
Base pressure coefficient
Thrust coefficient=T/q.S
Mach number
Jet Pressure ratio
= Total variation diminishing
1. INTRODUCTION
Computational Fluid Dynamics constitutes a new "third approach" in the philosophical study and
development of the whole discipline of fluid dynamics. It nicely and synergistically complements the other two
approaches of pure theory and pure experiments, but it will never replace the either of these approaches (as
sometimes suggested). Computational fluid dynamic results are directly analogous to wind tunnel results
obtained in a laboratory. The advancement of fluid dynamics will rest upon proper balancing of three
approaches, with computational fluid dynamics helping to interpret and understand the results of theory and
experiment, and vice versa [1].
Launch vehicle configuration with reverse flow nozzle as illustrated in Figure 1, is a configuration for a
human spaceflight launch abort system [2, 3]. As illustrated in Figure 1, two geometry elements dominate the
aerodynamic configuration of the LAS reverse flow nozzle configuration. One is the set of nacelles composing
the reverse flow nozzles. The other is the large flare to carry the crew module at the downstream of the rocket
nozzles.
The main objective of this study is to estimate the surface and base pressure of launch vehicle with
reverse flow nozzles under jet-off and jet-on conditions at various Mach numbers and angles of attack. Section
two of this paper presents geometry details and computational approach carried out using PARAS-3D. Section
three gives the validation and details of the studies carried out at Mach number 1.6 at various angles of attack.
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J Sreenivasulu, Sundeep Epuri and Dr. Patil M M
2. COMPUTATIONAL ANALYSIS
2.1 Geometry details
The Launch Escape System (LES) configuration used in the CFD study consists of a nose followed by a
cylinder of diameter 1D with propulsive nozzles (4 No’s) at the middle of the cylinder named as jet1, followed
by a flare and cylinder configuration with four grid finsand 4 propulsive nozzles named as jet2. This
configuration has a base diameter of 4.625D; four grid fins are located at the base of vehicle. The total length of
configuration is 17.89D. Error! Reference source not found. shows the Launch vehicle configuration with
reverse flow motors, 3D model for CFD analysis shown in Figure 2.
2.2 Mesh generation
The PARAS code uses the Cartesian grid. The grid is generated by means of an adaptive Cartesian
mesh technique. The flow domain is 75D upstream, 90D on the downstream and 75D the remaining sides for
Mach <1.0 condition. For M>1, upstream domain reduced to 10D, remaining sides kept same. The initial coarse
grid contained nearly 9 million cells and the final finer grid turned out to be between11 to 20 million cells for
various Mach numbers. In order to capture the flow features, appropriate cluster was used to make finer grid
near the body. Error! Reference source not found. shows the section of 3 dimension grids for subsonic Mach
numbers. The boundary conditions are implemented with the dummy cells. Close up view of the computational
mesh of the LES configuration is shown in Error! Reference source not found.. Final grid after refinement is
shown in Figure 5.
2.3 Flow solver
The study has been carried out using Computational Fluid Dynamics (CFD) through in house
developed code PARAS-3D[4]. PARAS-3D is versatile tool for analyzing various types of flows with multiple
free streams. It solves the Reynolds Averaged Navier-Stokes (RANS) equations. The turbulent flows are
simulated using k-ε model in association with modified wall function. The time stepping is done for each cell
based on local CFL criteria while the fluxes at the interface of the cells are computed by means of an
approximate Riemann solver. The explicit scheme used is second order accurate in space and of TVD type,
which is achieved by means of min-mod limiter. As the solution proceeds, the flow can be refined by adding
more grids in regions of high flow gradients and at the same time removing extra cells around regions of low
flow gradients.
2.4 Boundary conditions
The CFD simulations are carried out at Mach numbers between 0.5 and 5 at angle of attacks 4 0. For
M<1, upwind boundary conditions are used for inward flow and pressure for all remaining surfaces. For M>1,
upwind boundary conditions are used for inward flow and shift (zero flow gradients) for all remaining surfaces.
For jet-on condition, exit properties are enforced at the exit of the nozzle for jet1 and jet2.
2.5 Convergence Criteria
The main convergence criteria [5] used to monitor and determine solution convergence is given below:
Convergence of force and moment coefficients was evaluated with a percent over a range (% OR) value
of less than 1 percent for each coefficient. The %OR value was calculated with Eq. (1), where CNmax is the value
of the coefficient at the last iteration. The coefficients were averaged over 10000 iterations. The standard
deviation and %OR value is calculated for each coefficient.
%OR 
CNmax  CN(max 9999)
Where : Cave
Cave
1

10000
x100%
Eq. (1)
N max

j  N max 9999
Cj
A typical convergence and grid independence plot in Figure 6 shows the convergence of normal force
coefficients for LES configuration at Mach 0.8, AOA of 4 0 under jet-off condition. For various grid sizes, the
solution converged very well within 1%.
This paper provides the information about coefficient of pressure over LES configuration. So, the
typical grid independence plots for Cp distribution at Mach 1.2, α=4 0 are shown in Figure 7. For various grid
sizes the Cp distribution converged within 0.01.
2.6 Computer Platform
All of the simulations are computed at the SAGA-220 (Supercomputer for Aerospace with GPU
Architecture), at Shatish Dhawan Super Computing Facility, VSSC. It operates with in house developed
Estimation of Surface and Base Pressure Coefficient over Launch Vehicle with Reverse Flow Nozzles using CFD
3
Compute Node Linux 2.0(64-bit) supporting GPU& Infiniband (Tiny OS for PXE booting of nodes). The
facility is powered by NVIDIA Tesla C2070 GPU (FERMI). The PARAS-3D flow solver uses approximately
0.1 μsec/cell/iteration on a single GPU for finer grid. Each solution used 6 GPU’S in parallel to solve the flow
equations.
3. RESULTS & DISCUSSION
3.1 Validation
The viscous simulation tool used in this study is validated using existing wind tunnel test [3] & CFD
data [2]. The wind tunnel experiment [3] provided opportunities to understand the flow physics and to verify the
CFD predictions, both qualitatively and quantitatively, for LAS with reverse-flow motors. The free-stream static
pressure and density corresponds to the wind tunnel conditions given in Table 1. The jet pressure ratio for
corresponding Mach number is given in Table 2. The angles of attack of the vehicle studied vary from 00 to 200.
The CFD results are verified using 1) surface pressure measurement 2) Aerodynamic coefficients 3) Flow
visualization
The surface pressure measurement (shown in Figure 8 are used to verify the CFD pressure distribution
along the launch vehicle body. Results (shown in Figure 8) indicate that surface pressure predictions are in very
good agreement with WT measurement data (Ref.3).
The aerodynamic coefficients are calculated based on the ref. length of 0.25m and ref. area of 0.049m2.
The aerodynamic coefficients and flow field characteristics are reported in Ref.[6].
3.2 Flow field characteristics
Flow field analysis was carried out at Mach number 2.5, at angle of attack of 4 0 under jet1 and jet2 on
condition is shown in Figure 9.
Mach palette in Figure 9 shows the bow shock at the nose cone region, oblique shock at first nozzle
region are captured well. The jets emanating from the nozzles 1 and 2 are shown clearly. Jet acts as a bluff body
and shock is formed at the jet1 region.
3.3 Base pressure coefficient
Variation of base pressure coefficient with Mach numbers 0.5 to 5.0 under jet-off condition at α=40 is
shown in Figure 10.
The CPb variation with Mach number for Apollo configuration, Empirical method and present LES is
shown in Figure 10. The empirical relation [7] relating the Cp base and Mach number is given in below:
--Eq. (2)
CPb of LES follows the similar trend of Apollo and empirical data with lesser magnitude in the
transonic regime this may be due to the presence of grid fins in the present configuration.
Figure 11 shows the base pressure coefficient variation under jet-off condition at an angle of attack of
40 at various Mach numbers. As the Mach number increases from 0.5 to 0.8 the base pressure coefficient
increases in magnitude (-0.35 to 0), for Mach above 0.8 the base pressure coefficient decreases in magnitude. In
the base region, pressure is less than the free stream pressure so, the pressure coefficient becomes negative. The
maximum CPb (-ve sense) variation along yaw axis observed near to the edge (i.e., Y/D≈1.875) for all the
Mach numbers
At Mach 5, the free stream pressure (27 Pa) is very low. In the presence of jet2 the base pressure
increases because jet2 has higher pressure and it creates a recirculation zone at the base as shown in Figure 12.
So, the CPb becomes positive at higher Mach numbers. Figure 13 shows the Comparison of minimum CPb with
Mach number at various jet-on conditions. Minimum CPb occurs at transonic Mach number 0.98 for jet-off,
Jet1on and jet1 &jet2 on condition. Some more studies are required to assess the applicability of Empirical
relation (Eq. 2) for LES kind of geometry with the presence of jets.
3.4 Cp distribution
Sectional view of the identification of circumferential CP distribution (hatched region) shown in Figure
14 .
Cp distribution over shroud of LES at various sections (0 to 180 0 @ 300 interval) is shown in Figure 15
to Figure 19. At Mach 0.5, Cp distribution over shroud is shown in Figure 15. It is observed that along the cone
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J Sreenivasulu, Sundeep Epuri and Dr. Patil M M
length Cp is decreasing as the flow is accelerating and it reaches maximum velocity at cone cylinder junction
hence the Cp decreases at this point. The decrease in the Cp recovers immediately in the cylindrical portion. In
transonic regime (0.8M1.2) slow recovery of Cp is observed at cylindrical portion as shown in Figure 16 to
Figure 17. For supersonic Mach number M2, Cp is almost zero along the cylindrical portion for all orientations
as shown in Figure 19.
At Mach 1.2, Cp distribution over shroud under both jets on condition is shown in Figure 20.
Compared to the jet-off case shown in Figure 17, Cp distribution throughout the shroud is decreased in presence
of jets. Here, jets acts as a body and obstruct the incoming flow contacting the shroud. Therefore, the change in
the pressure is very small. At X/D of 14 to 15, the large value of the Cp at 0 0, 50 and 900 generators are due to
the jet2 pressure.
4. CONCLUSION
CFD analysis has been carried out over LAS configuration at Mach 1.6, various angles of attack under
jet-off and jet-on conditions. The present CFD code captures the flow characteristics reasonably well. The
aerodynamic coefficients and pressure distribution are in good agreement with reported data.
Base Cp over LES base and Cp distribution over shroud have been generated under various conditions
(jet-off, jet1, jet2 and both jets-on) at various Mach numbers (0.5 to5) and α=40 using the PARAS-3D. Base
pressure coefficient is negative under jet-off, jet1-on and jet1 and jet2-on conditions. Min. CPb occurs at Mach
0.98. Under jet2-on condition, till Mach 4 base pressure coefficient is negative. For M>4, CPb becomes positive.
Jet creates a recirculation zone at higher Mach numbers under jet2-on condition. Cp distribution over shroud of
LES is also generated under jet-off condition at various Mach numbers at an angle of attack of 40.
Acknowledgment
Authors are thankful to Shri. A E Sivaramakrishnan, GH, ACEG, Dr. V Ashok, GD, ADTG and Shri.
S.Pandian, Deputy Director, Aeronautics Entity, VSSC for their guidance and encouragement for this work. We
also thankful to Sri. Rakesh C K, HSP for his support in geometry modeling. Authors are thankful to Sri.
Harichand & Sri. T C Babu, APCF for their support during simulation work. Authors thankful to Shri. K Kumar,
DPD, HSP/VSSC for kindly agreeing to review the paper and for the valuable suggestions
REFERENCES
1. John D. Anderson Jr., “Computational Fluid Dynamics”, McGraw-Hill , Inc.
2. H. Feiz, L. Glatt, M. Emerick, and S. Kovacic., “CFD Applications to Launch Vehicles with Reverse Flow
Plumes”, 46th AIAA Aerospace Sciences Meeting and Exhibit, 7 - 10 January 2008, Reno, Nevada
3. L. A. Cassel, T. P. Shivananda, H. Feiz, S. Kovacic, and R. Douglas, “Reverse Flow Plume Interference
Wind Tunnel Testing - For CEV Launch Abort System (LAS),” AIAA2008-0347, 46th AIAA Aerospace
Sciences Meeting and Exhibit, 2008.
4. V Ashok, Babu T C, et. al., “User manual for PARAS-3D”, VSSC, Trivandrum
5. Karen A. Deere, S. Paul Pao, and Khaled S. Abdol-Hamid., “Computational Analysis of Ares I Roll
Control System Jet Interaction Effects on Rolling Moment”, 49th AIAA Aerospace Sciences Meeting
including the New Horizons Forum and Aerospace Exposition 4 - 7 January 2011, Orlando, Florida.
6. J Sreenivasulu, et.al., “CFD analysis of a Launch Vehicle with Reverse Flow Nozzles- A Validation study”,
IJAI, Vol. 5, No.1, 2003
7. M. Prasath, et. Al., “Base Pressure Prediction of Blunt Bodies” , RAEFM, Vijayawada
Table 1: Free stream condition
M
Pressure (N/m2)
Density (Kg/m3)
Angle of attack (deg)
1.6
48349.8
0.673092
0 to 200
Table 2: Jet pressure ratio for jet-on condition
M
 Pj / Pinf 
Estimation of Surface and Base Pressure Coefficient over Launch Vehicle with Reverse Flow Nozzles using CFD
1.6
6.52
Y
X
Jet1
Jet2
Figure 1: Launch vehicle with reverse flow nozzles (Launch Escape System)
Figure 2: Launch Abort System with reverse flow nozzles used for validation (Ref.2)
Figure 3: Sectional view of the grid for subsonic Mach numbers
5
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J Sreenivasulu, Sundeep Epuri and Dr. Patil M M
Figure 4: Sectional view of the computational grid (close up)
Figure 5: Sectional view of the computational grid (close up) after refinement
Figure 6: Normal force convergence for various grids at M=0.8, angle of attack of 10 0 under jet-off
condition
Figure 7: Grid independence test-Coefficient of pressure for various grids at M=1.2, angle of attack of 4 0
under jet-off condition
Estimation of Surface and Base Pressure Coefficient over Launch Vehicle with Reverse Flow Nozzles using CFD
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Figure 8: Validation of PARAS-3D with experimental CP distribution of LAS configuration at angle of
attack of 200 in presence of jet (CT=0.34)
Figure 9: Mach palette over LES configuration at M=2.5, angle of attack of 4 0 in presence of both jet1
and jet2 on
Figure 10: Variation of base pressure coefficient with Mach number under jet-off condition at α=40
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J Sreenivasulu, Sundeep Epuri and Dr. Patil M M
Figure 11: Base pressure coefficient distribution at various Mach numbers under jet-off condition
Figure 12: Mach palette at Mach 5, angle of attack of 4 0 under jet2-on condition
Estimation of Surface and Base Pressure Coefficient over Launch Vehicle with Reverse Flow Nozzles using CFD
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Figure 13: Variation of base pressure coefficient with Mach number under various jet-on conditions
Figure 14: Identification of circumferential cp distribution (hatched region)
Geometry
Figure 15: Cp distribution over shroud under jet-off condition at M=0.5, α=40
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J Sreenivasulu, Sundeep Epuri and Dr. Patil M M
Geometry
Figure 16: Cp distribution over shroud under jet-off condition at M=0.8, α=40
Geometry
Figure 17: Cp distribution over shroud under jet-off condition at M=1.2, α=40
Geometry
Figure 18: Cp distribution over shroud under jet-off condition at M=2.0, α=40
Estimation of Surface and Base Pressure Coefficient over Launch Vehicle with Reverse Flow Nozzles using CFD
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Geometry
Figure 19: Cp distribution over shroud under jet-off condition at M=2.5, α=40
Geometry
Figure 20: CP distribution over a shroud at Mach 1.2, α=100 under both jets (jet1 and jet 2)-on condition