August 2014 

NASA/TM–2014-218507 

Multi-Mission System Analysis for Planetary 

Entry (M-SAPE) Version 1 

Jamshid Samareh and Louis Glaab

Langley Research Center, Hampton, Virginia 

Richard G. Winski 

Binera, Inc., Yorktown, Virginia 

Robert W. Maddock 

Langley Research Center, Hampton, Virginia 

Anjie L. Emmett 

Analytical Mechanics Associates, Inc., Hampton, Virginia 

Michelle M. Munk 

Langley Research Center, Hampton, Virginia 

Parul Agrawal, Steve Sepka, Jose Aliaga, 

Kerry Zarchi, and Nancy Mangini 

Ames Research Center, Moffett Field, California 

Scott Perino and Javid Bayandor 

Virginia Polytechnic Institute and State University, Blacksburg, Virginia 

Charles Liles 

Old Dominion University, Norfolk, Virginia 
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Space Administration 

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Hampton, Virginia 23681-2199 

 

August 2014 

NASA/TM–2014-218507 

Multi-Mission System Analysis for Planetary 

Entry (M-SAPE) Version 1 

Jamshid Samareh and Louis Glaab

Langley Research Center, Hampton, Virginia 

Richard G. Winski 

Binera, Inc., Yorktown, Virginia 

Robert W. Maddock 

Langley Research Center, Hampton, Virginia 

Anjie L. Emmett 

Analytical Mechanics Associates, Inc., Hampton, Virginia 

Michelle M. Munk 

Langley Research Center, Hampton, Virginia 

Parul Agrawal, Steve Sepka, Jose Aliaga, 

Kerry Zarchi, and Nancy Mangini 

Ames Research Center, Moffett Field, California 

Scott Perino and Javid Bayandor 

Virginia Polytechnic Institute and State University, Blacksburg, Virginia 

Charles Liles 

Old Dominion University, Norfolk, Virginia 
Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802 

Acknowledgments 

M-SAPE project is a multidisciplinary analysis and design activity, and the project would 

have been impossible to do without the help of many experts. The authors would like to 

thank the following people: Howard Abston (LaRC), Sasan Armand (LaRC), James 

Arnold (ARC), Nikki Bauer (GT), Robert Braun (GT), Juan Cruz-Ayoroa (GT), Artem 

Dyakonov (LaRC), Bob Gershman (JPL), Robin Hardy (LaRC), Kimberly Halom 

(LARSS), Chris Johnston (LaRC), Daniel Lyons (JPL), Richard Mattingly (JPL), 

Michelle Munk (LaRC), Erik Nilsen (JPL), Dinesh Prabhu (LaRC), Jeremy Shidner 

(LaRC), Aaron Siddens (VT), Brandon Smith (GT), Erick Sturm (JPL), Ken Sutton 

(LaRC), Chris Tanner (GT), John Theisinger (LaRC), and Ethiraj Venkatapathy (ARC). 

The use of trademarks or names of manufacturers in this report is for accurate reporting and does not constitute an 

official endorsement, either expressed or implied, of such products or manufacturers by the National Aeronautics 

and Space Administration.
iii

TABLE OF CONTENTS 

List of Figures

List of Tables

Nomenclature

Summary

1.0 Introduction

		



	


2.0 M-SAPE System






 

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3.0 M-SAPE Modules#


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4.0 Sample Results 0

5.0 Conclusion#

6.0 Future Development and opportunities for collaboration#

7.0 References#'
v

LIST OF FIGURES 

Figure 1. Sample Return Earth Entry Vehicles (left to right: Genesis, Stardust, Hayabusa, and Phobos-Grunt)...........................................2 

Figure 2. MSR Vehicle Concept...................................................................................................................................4 

Figure 3. Time into Design Process ..............................................................................................................................5 

Figure 4. Design Structure Matrix for M-SAPE ................................................................................................................7 

Figure 5. M-SAPE Flow Chart ....................................................................................................................................9 

Figure 6. M-SAPE System Integration .......................................................................................................................10 

Figure 7. M-SAPE System Integration Example (UML Sequence Diagram).................................................................................11 

Figure 8. Screenshot of M-SAPE Database Query and Results...............................................................................................14 

Figure 9. Screenshot of M-SAPE Contour Plot...................................................................................................................15 

Figure 10. Screenshot of M-SAPE Sample Single Run Results................................................................................................16 

Figure 11. Sample Vehicle Models .................................................................................................................................17 

Figure 12. Flow Chart for Parametric Vehicle Model...........................................................................................................20 

Figure 13. Geometry Models for MSR and Non-MSR Vehicles...... ........................................................................................22 

Figure 14. Non-MSR Vehicle Assumptions ........................................................................................................................23 

Figure 15. MSR Vehicle Assumptions...............................................................................................................................23 

Figure 16. MSR and/or Non-MSR Vehicle Parameters............................................................................................................24 

Figure 17. MSR and/or Non-MSR Vehicle Parameters (Close Up) ..........................................................................................24 

Figure 18. MSR Vehicle Parameters ..............................................................................................................................25 

Figure 19. Impact G’s Assuming Ground Penetration...........................................................................................................27 

Figure 20. Required foam Thickness and Compression Strength ..............................................................................................28 

Figure 21. A Biplane Cross Section View of the Finite-Element Model of Impact Sphere................................................................29 

Figure 22. Finite-Element Model of EEV for Launch and Entry ..............................................................................................30 

Figure 23. Model Integration into POST2, MSL Example..... ..................................................................................................31 

Figure 24 MMEEV Representative Trajectory .... ...............................................................................................................33 

Figure 25. MMEEV Supersonic, Hypersonic, and Rarefied Flow Pitching Moment at 0.2D cm .......... ...............................................34 

Figure 26. CFD Computer Flow field at 100 Degrees Angle of Attack.........................................................................................35

Figure 27. Time History Data for Several Trajectories with Different CGs (Cms). Baseline CG=0.2D...................................................37 

Figure 28. MMEEV Normal Force Coefficient for All Mach Numbers........................................................................................38 

Figure 29. MMEEV Pitching Moment and Axial Force Coefficients for All Mach Numbers..............................................................40 

Figure 30. MMEEV Pitch Damping Data from M=0.5 to 2.1.......................................................................................... .........41 

Figure 31. Geometries are Superimposed...........................................................................................................................43 

Figure 32. Mach Contour for the v1 Geometry....................................................................................................................45 

Figure 33. Correlated Heat Flux with Varying Velocity as Compared to DPLR and Sutton-Graves......................................................45 

Figure 34. Correlated Heat Flux with Varying Entry Path Flight angles as Compared to DPLR and Sutton-Graves....................................46 

Figure 35. Correlated Heat Flux with Varying Ballistic Coefficient as Compared to DPLR and Sutton-Graves........................................46 
vi

Figure 36. Stagnation Point Heat Flux along a Trajectory for v2 Geometry................................................................................47

Figure 37. Stagnation Point Heat Flux along a Trajectory .....................................................................................................48 

Figure 38. Mach Contour after Grid Adaption for the v2 Geometry...........................................................................................48

Figure 39. Mach Contour after Grid Adaption for the v3 Geometry...........................................................................................49

Figure 40. Stagnation Pressure for v1 Geometry. ...............................................................................................................50 

Figure 41. Stagnation Pressure for v3 Geometry. .............................................................................................................51 

Figure 42. Non-dimentionalized Shoulder Heat Flux as a Function of Mach Number for the v3 Geometry,..........................................51 

Figure 43. Heat Flux Comparison between Laminar and Fully Turbulent Solutions at an Early Point in the Trajectory ............................52 

Figure 44. Heat Flux Comparison Between Laminar and Fully Turbulent Solutions at Peak Heating along the Trajectory........................53 

Figure 45. Time History of the Maximum Value of Momentum Thickness Reynolds Number for the v3 Geometry .................................53 

Figure 46. Different Phases of Earth Entry Vehicle and Time Span for Thermal Soak Inside the Vehicle......... ...................................59 

Figure 47. MMEEV Thermal Soak Model Geometry...........................................................................................................61 

Figure 48. Finite Element Mesh with Component Sets..........................................................................................................62 

Figure 49. Materials Map for Baseline Thermal Analysis.......................................................................................................63 

Figure 50. Trajectory Space with Selected Trajectories ........................................................................................................65 

Figure 51. Figure 51. Sample M-SAPE Trade Space for MSR Model ......................................................................................71 

Figure 52. Galahad EEV Concept (Maddock et al. 2010).....................................................................................................71 

Figure A1. MMEEV Aero Database for Rarefied Flow Conditions. Moment Reference Point is the Vehicle theoretical Nose Apex............A-1 

Figure A2. MMEEV Aero Database for M=24. Moment Reference Point is the Vehicle theoretical Nose Apex........ ...........................A-2 

Figure A3. MMEEV Aero Database for M=9.8. Moment Reference Point is the Vehicle theoretical Nose Apex...................................A-3 

Figure A4. MMEEV Aero Database for M=3.98. Moment Reference Point is the Vehicle Theoretical Nose Apex.................................A-4 

Figure A5. MMEEV Aero Database for M=1.3. Moment Reference Point is the Vehicle theoretical Nose Apex...................................A-5 

Figure A6. MMEEV Aero Database for M=1.15. Moment Reference Point is the Vehicle Theoretical Nose Apex ................................A-6 

Figure A7. MMEEV Aero Database for M=0.85. Moment Reference Point is the Vehicle Theoretical Nose Apex ...............................A-7 

Figure A8. MMEEV Aero Database for M=0.75. Moment Reference Point is the Vehicle Theoretical Nose Apex ...............................A-8 

Figure A9. MMEEV Aero Database for M=0.6. Moment Reference Point is the Vehicle Theoretical Nose Apex ..................................A-9 

Figure A10. MMEEV Aero Database for M=0.5. Moment Reference Point is the Vehicle Theoretical Nose Apex ................................A-10 

Figure B1. PICA-only MER GoF vs. FIAT-predicted PICA Thickness.....................................................................................B-2 

Figure B2. Carbon Phenolic over ACC6 MER, GoF vs. FIAT-predicted PICA Thickness................................................................B-4 

Figure B3. SIRCA MER GoF.....................................................................................................................................B-5 

Figure B4. Acusil II MER GoF...................................................................................................................................B-6 

Figure B5. SLA-561V MER GoF................................................................................................................................B-7 

Figure B6. LI900 MER GoF........................................................................................................................................B-10 

Figure C1. The 256 Surface Body Points for Full body Aerothermal Analysis..............................................................................C-1 

Figure C2. Heat Flux Distribution along Vehicle at Peak Heating.... ........................................................................................C-1 

Figure C3. DPLR Solutions with the Curve Fits Superimposed for the Low Heat Load Trajectory .....................................................C-2 

Figure C4. Temperature Contours for the High Heat Load Trajectory ......................................................................................C-2 

Figure C5. Temperature contours for the Low Heat Load Trajectory .......................................................................................C-3 
vii

Figure C6. Temperature Contours in the Probe after Touchdown..............................................................................................C-3 

Figure C7. Peak Foam Temperature for the Two Trajectories .................................................................................................C-4 

Figure C8. Payload temperature History and Peak Payload Temperature for Various Trajectories .....................................................C-4 

Figure C9. Parametric Studies for Heat Flux Magnitude Variation for the High Heat Load Trajectory..................................................C-5 

Figure C10. Parametric Studies for Heat Flux Magnitude Variation for the Low Heat Load Trajectory ...............................................C-5 

Figure C11. Peak Payload Temperature Variations with Head Load for a Given Vehicle Diameter ....................................................C-5 

Figure C12. The GoF for Established Correlations for Peak Payload Temperature with Vehicle Diameter, 

 Peak Stagnation Heat Flux and Head Load .......................................................................................................C-6 

 
viii

LIST OF TABLES

Table 1. Mass Property Output Values ....................................................................................................................25 

Table 2. Geopotential Coefficients..........................................................................................................................32 

Table 3. Geometry Configurations .........................................................................................................................42 

Table 4. CFD Trade Space for v1 Geometry ..............................................................................................................44 

Table 5. Flight Trajectory Space Considered for the MERs.............................................................................................55 

Table 6. Surface Heating and Pressure Ranges Found by Running FIAT over the 840 Trajectories............ ..................................56 

Table 7. Summary of MERs Model Development Data..................................................................................................57 

Table 8. Summary of Arcjet, Galileo, Pathfinder, and MSL Materials Test Data ....................................................................59 

Table 9. Thickness of the Subcomponents along the Vertical Centerline for MMEEV Vehicle ...................................................61 

Table 10. Specific Heat Capacity of Rohacell-51..........................................................................................................63 

Table 11. Thermal Conductivity of Rohacell-51 ... .......................................................................................................64 

Table 12. For PICA TPS.......................................................................................................................................68 

Table 13. For CP TPS..........................................................................................................................................68 

Table 14. Input Parameters for Case 1.......................................................................................................................70 

Table 15. M-SAPE Results Compared with Dillman and Corliss 2008.................................................................................70 

Table 16. M-SAPE Results Compared with Maddock et al. 2010.......................................................................................72 

Table B1. B1. Material Stack-up for Carbon Phenolic Atop Advanced Carbon-Carbon 6..........................................................B-1 

Table B3. Vehicle Characteristics............................................................................................................................B-1 

Table B4. MER Summary................ ....................................................................................................................B-1 

Table B5. Flight Trajectory Space and Correlation Details for the PICA-only MER........................ .......................................B-2 

Table B6. Material Stack-up...................................................................................................................................B-3 

Table B7. Flight Trajectory Space and Correlation Details for the Carbon Phenolic Atop ACC MER............................................B-3 

Table B8. SIRCA MER Parameters ........................................................................................................................B-5 

Table B9. Acusil II Parameters .............................................................................................................................B-6 

Table B10. SLA-561V Parameters .........................................................................................................................B-7 

Table B11. LI-900 MER Parameters .....................................................................................................................B-9 

 
ix

NOMENCLATURE 

a,b,c Power Law Fit Parameters 

ACC Advanced Carbon-Carbon 

ACE Aerothermal Chemical Equilibrium Computer Program 

CAD Computer Aided Design 

CFD Computational Fluid Dynamics 

CEV Crew Exploration Vehicle 

CG Center of Gravity 

CMA Charring Material Thermal Response and Ablation Program 

DPLR Data Parallel Line Relaxation 

DSMC Direct Simulated Monte Carlo 

DAC DSMC Analysis Code 

EDL Entry Descent and Landing 

EEV Earth Entry Vehicle 

EFPA Entry Flight Path Angle, absolute degrees 

FE Finite Element 

FEA Finite Element Analysis 

FIAT Fully Implicit Ablation and Thermal Response 

GoF Goodness of Fit 

GRAM Global Reference Atmospheric Model 

HL Heat Load, J/cm2

i Index of Data Point 

IGES Initial Graphics Exchange Specification 

LaRC Langley Research Center 

LAURA Langley Aeroheating Upwind Relaxation Algorithm 

MER Mass Estimating Relationship 

MMEEV Multi Mission Earth Entry Vehicle 

M-SAPE Multi Mission System Analysis for Planetary Entry 

MSR Mars Sample Return 
x

n Total Number of Data Points 

OS Operating System 

PHP PHP Hypertext Preprocessor 

PICA Phenolic Impregnated Carbon Ablator 

POST Program to Optimize Simulated Trajectories 

PRA Probabilistic Risk Assessment 

Python Platform-independent open-source programming language 

qmax Maximum (peak) Heat Flux of a Trajectory, W/ cm

2

R Residual 

SD Standard Deviation 

STAB Standard Ablation Program 

STEP Standard for the Exchange of Product Model Data 

TH TPS Thickness 

TPS Thermal Protection System 

TRL Technology Readiness Level 

UTTR Utah Test and Training Range 

V Velocity, km/sec 
1

SUMMARY 

This report describes an integrated system for Multi-mission System Analysis for Planetary 

Entry (M-SAPE). The system in its current form is capable of performing system analysis and 

design for an Earth entry vehicle suitable for sample return missions. The system includes 

geometry, mass sizing, impact analysis, structural analysis, flight mechanics, TPS, and a web 

portal for user access. The report includes details of M-SAPE modules and provides sample 

results. 

Current M-SAPE vehicle design concept is based on Mars sample return (MSR) Earth entry 

vehicle design, which is driven by minimizing risk associated with sample containment (no 

parachute and passive aerodynamic stability). By M-SAPE exploiting a common design concept, 

any sample return mission, particularly MSR, will benefit from significant risk and development 

cost reductions. The design provides a platform by which technologies and design elements can 

be evaluated rapidly prior to any costly investment commitment. 

An important goal for M-SAPE is to provide an integrated environment such that a low fidelity 

system analysis and trade can be performed in hours (not weeks or months) with sufficient hooks 

to perform high-fidelity analysis in days. The system is designed to help analysts to gain a better 

understanding of various entry system concepts and their limitations. The role of discipline 

experts in the systems analysis process is indispensable and cannot be replaced by any tool. 

However, M-SAPE helps to improve the performance of the systems analysis team by 

automating and streamlining the process, and this improvement can reduce the errors resulting 

from manual data transfer among discipline experts. The process improves and accelerates 

design activities such as trade studies, sensitivity analyses, Monte Carlo analyses, and vehicle 

optimization. 

Another goal of M-SAPE is to use existing software components, especially open-source 

software, to avoid unnecessary software development and licensing issues. M-SAPE is a looselycoupled system that uses Python language (platform-independent open-source software) for 

integration. Development has relied heavily on the object-oriented programming capabilities 

available in Python. Modules are provided to interface with commercial and government off-theshelf software components. 

 
2

1.0 INTRODUCTION 

In recent years, the sample return mission concept has gradually grown increased favor. The 

successes of the Stardust and the partial successes of the Genesis missions, and most recently, 

the Japanese Hayabusa mission, have clearly highlighted the advantages of bringing samples 

back to Earth where they can be studied in much greater detail by more powerful instruments, 

examined by a much wider scientific community, and preserved over an extended period of time. 

With growing interest in the Moon, asteroids, comets, and particularly Mars, more and more 

sample return missions are included into the space exploration roadmap. For example, the 

Planetary Science Decadal Survey, published in March 2011, includes several missions to return 

samples to Earth from around the Solar System. 

There were many sample return missions in the past. Figure 1 shows several sample return Earth 

entry vehicles. The Soviets had several successful robotic lunar sample-return missions in the 

1970s. The NASA Genesis project was a sample return mission that was launched in August of 

2001 to collect a sample of solar wind and return it to Earth. In September of 2004, the Genesis 

Earth entry vehicle crashed in the Utah desert when the parachutes failed to deploy, and the 

planned mid-air retrieval could not be performed. Stardust was a NASA sample return mission 

launched in 1999 to collect cosmic dust. The Stardust entry vehicle successfully landed at the 

Utah Test and Training Range in 2006. Hayabusa was a Japanese mission that collected dust 

from an asteroid, and it landed in June of 2010 in the South Australian Outback. There is a plan 

for a follow-up mission for Hayabusa 2 scheduled for either 2014 or 2015. Phobos-Grunt was a 

Russian sample return mission to Phobos. The mission was launched in November of 2011, but a 

failure left the spacecraft stranded in low Earth orbit. China has a mission plan to return a lunar 

sample in 2017. There is also the Mars Sample Return (MSR) plan, which is the most 

challenging of all existing sample return plans. Mattingly and May 2011 provide the most up-todate overview of this mission plan. 

Fig. 1 Sample Return Earth Entry Vehicles (left to right: Genesis, Stardust, Hayabusa, and 

Phobos-Grunt) 

The potential for terrestrial contamination from returned sample material could be a major driver 

for Earth Entry Vehicle (EEV) design. A planetary Entry, Descent, and Landing (EDL) system 
3

typically consists of a heat shield for entry, a parachute for descent, and either retro rockets or 

airbags for landing. Mitcheltree et al. 1998 provide a discussion on two possible options for a 

reliable EEV design: either the design includes sufficient redundancy for each subsystem or 

eliminates the need for the subsystem. They propose a simple passive entry system solution that 

replaces the parachute and landing system with a hardened container surrounded by sufficient 

energy absorbing material to assure containment during ground impact. Dillman and Corliss 

2008 continued refining Mitcheltree’s model that is the basis for the EEV model used in the 

current study. 

The Multi-Mission Earth Entry Vehicle (MMEEV) provides a highly reliable, yet flexible design 

concept, from which any sample return mission can benefit. Based on the MSR EEV design that 

was developed by minimizing risk associated with the loss of sample containment at Earth, the 

MMEEV concept provides a logical foundation upon which any individual mission can build an 

optimized design that meets their specific needs. Beginning from a single design concept ensures 

maximum commonality and feed-forward for all MMEEV users. The MMEEV concept provides 

a platform by which new technologies, design elements, and processes can be developed, tested, 

and even flown, prior to implementation on MSR. By leveraging common design elements and 

technology development, this approach could significantly reduce the risk and associated cost in 

development of not only the MSR EEV, but all sample return missions which utilize the 

MMEEV concept. 

Maddock et al. 2008 provides the details of the MMEEV analysis, design, system components 

and the vehicle trade space for the Galahad asteroid sample return mission proposal submitted in 

response to the NASA New Frontiers solicitation. The second version of MMEEV is described 

in Maddock et al. 2011, where the initial tightly coupled MMEEV integration approach is 

introduced. The loosely coupled system integration was introduced in Samareh et al. 2012.

The NASA In-Space Propulsion Technologies (ISPT) Project, funded by the Science Mission 

Directorate, is continuing to conduct activities that will mature a class of vehicles in support of 

Earth entry, descent and landing mission phases. However, the ISPT Project funding level does 

not currently support a dedicated flight test prior to use of the MMEEV concept. The current 

strategy, therefore, is to mature vehicle critical characteristics for a range of MMEEVs to TRL 5-

6 before the next Discovery or New Frontiers Announcement of Opportunity. 

1.1 MMEEV Description 

The Multi-Mission Earth Entry Vehicle (MMEEV) concept is based on the Mars Sample Return 

(MSR) EEV design originally developed at NASA LaRC in the 1990s for the 2003/2005 MSR 

Project (Figure 2). Development continued from 2000-2005 through focused technology 

development activities, including impact attenuation, sample containment, and aerodynamic 

performance. Development continued during 2005-2008 with NASA Langley internal funding.

From 2008-2012, In-Space Propulsion Technology Program (ISPT) funded further development 

of the MMEEV system concept. During 2007-2008, a system analysis for planetary EDL (SAPE) 

(Samareh 2009) was developed with NASA Langley internal funds. The ISPT program 

continued funding development of MMEEV system analysis capability, which has since 

morphed into M-SAPE activity. 
4

Figure 2. MSR Vehicle Concept 

The MSR EEV design was driven by the mission’s Probabilistic Risk Assessment (PRA) where 

(Earth) planetary protection and sample containment were the major drivers. This required the 

need to eliminate or minimize the use of active systems, as well as the strong drive to use 

heritage, high Technology Readiness Level (TRL) elements. In essence, the MSR EEV design 

was to be the most reliable vehicle humans could build. 

This approach provided a logical starting point for any sample return mission. However, because 

the same level of reliability may not be needed by all missions due to variable planetary 

protection requirements, by using the MSR EEV design concept as a starting point, each mission 

could maximize reliability while also optimizing for specific mission needs. Although the MSR 

EEV baseline design has not changed dramatically since 2001, modifications were anticipated as 

new technologies and processes were made available. And that is where MMEEV came in. 

The high reliability of the MSR EEV design can be attributed to two design principles, both of 

which were preserved in the MMEEV concept. The first of these design principles was 

eliminating all active systems, including the parachute. The combined reliability of parachutes 

and their required automated deployment systems is much lower than that needed to ensure 

sample containment for MSR. Also, parachute systems can be massive, thus increasing 

aeroheating and impact loads in the event of a parachute failure, and possibly reducing 

aerodynamic stability during entry by shifting the center of mass aft. They can also be difficult to 

package, which could complicate the sample transfer chain. 

The second key design principle which the MMEEV preserved is the use of a well-known, 

highly tested, and flight proven aerodynamic forebody shape - the 60 degree, half-angle sphere 

cone. This forebody shape provides robust performance against a wide range of entry condition 

dispersions and atmospheric uncertainties. In addition, selecting an aftbody shape that provides 

for hypersonic stability upon atmospheric reentry is highly desirable. This allows for a 

reorientation capability, even when spin stabilized and entering the atmosphere backwards or 

tumbling due to attitude errors from spacecraft separation or meteoroid impact. Although, this 

particular capability is only needed for missions with very stringent planetary protection 

requirements, such as MSR, the concept was implemented on MMEEV and studied as a way to 

provide feed-forward to MSR. 
5

To support this strategy, the software tool known as M-SAPE (Multi Mission System Analysis 

for Planetary Entry) has been created to assist in the development of a flexible Earth Entry 

Vehicle (EEV) design that can be utilized by multiple sample return missions. 

1.2 M-SAPE Description 

As the vehicle design process goes forward, designs are refined based upon information 

developed during the process. However, as shown in Figure 3, the later in the process this 

information becomes available, the more difficult and expensive it becomes to act upon the 

knowledge gained. 

M-SAPE is a system analysis tool developed for use by planetary probe vehicle designers to 

make enough information available early in the design process to maintain design freedom while 

still improving the probability of mission success. 

Figure 3. Time into Design Process (baseline is in solid lines and improved is in dotted lines)

2.0 M-SAPE SYSTEM 

The purpose of systems analysis of an EEV is to gain a better understanding of various entry 

system concepts and their limitations. Systems analysis teams typically include systems 

engineers and discipline-specific experts in flight mechanics, aerodynamics, 
6

aerothermodynamics, structural analysis, impact analysis, thermal soak, and thermal protection 

systems. The systems analysis process may take from several weeks to several years. 

The NASA In-Space Propulsion Technology (ISPT) Program has funded a system analysis 

project for the development of Multi-Mission Earth Entry Vehicle (MMEEV) for the past several 

years. Maddock et al. 2008, 2010, and 2011; and Samareh et al. 2012 have documented the 

overall MMEEV project and its progress. 

The implementation of the multidisciplinary analysis approach presented here is a modified 

version of the System Analysis for Planetary EDL (SAPE) code (Samareh 2012). This 

implementation is targeted for Multi-Mission Earth Entry Vehicles using SAPE (M-SAPE). The 

purpose of M-SAPE is to provide a variable-fidelity capability for conceptual and preliminary 

analysis within the same framework. M-SAPE uses Python language (platform-independent 

open-source software) for integration. The development has relied heavily on the object-oriented 

programming capabilities available in Python. M-SAPE has links to commercial and government 

off-the-shelf software modules (e.g., flight mechanics code, POST2). An important goal for MSAPE is to provide an integrated environment such that a low fidelity system analysis and trade 

can be performed in minutes and hours (not days or weeks) with sufficient hooks to perform 

high-fidelity analysis in days. Another goal of M-SAPE is to use existing software modules, 

especially open-source software to avoid unnecessary software development and licensing 

issues. 

As stated in Section 1.0, the M-SAPE goal is to develop a flexible Earth Entry Vehicle (EEV) 

design that can be utilized by multiple sample return missions. By preserving key common 

elements, the MMEEV concept provides a platform by which technologies, design elements, and 

processes can be developed and flight-tested prior to implementation on MSR. This approach 

could not only significantly reduce the risk and associated cost in development of the MSR EEV, 

but all sample missions that would benefit by leveraging common design elements. 

2.1 Multidisciplinary Systems Analysis 

Integrated multidisciplinary analysis tools improve the performance of the systems analysis team 

by automating and streamlining the process, and this improvement can reduce the errors resulting 

from manual data transfer among discipline experts. The process improves and speeds up the 

design activities such as trade studies, sensitivity analyses, Monte Carlo analyses, and vehicle 

optimization. 

A multidisciplinary problem can be decomposed into a set of key disciplines. These discipline 

tools, in this paper referred to as modules, can be represented in matrix form using the Design 

Structure Matrix (DSM) approach. The matrix is a graphical approach for representing the 

interdependencies among the various modules. The DSM is a square matrix with the analysis 

modules positioned along the main diagonal. Figure 4 shows a DSM representation for the EEV 

integrated analysis tool that includes seven analysis modules: geometry, mass sizing, impact 

analysis, structural analysis, flight mechanics, TPS sizing, and thermal soak. For each analysis 

module shown along the DSM diagonal, relevant outputs are listed in the corresponding row; the 

inputs are listed in the corresponding column. For example, the required inputs for impact 
7

analysis are the OML, mass, terminal velocity, and temperature field. Impact analysis outputs 

include an estimate for the mass of impact sphere as well as the required impact stroke. The data 

exchanges among modules below the DSM diagonal indicate a feedback loop. The DSM can be 

reordered to reduce the number of feedback loops or to exchange strong feedback loops with 

weaker ones. 

Figure 4. Design Structure Matrix for M-SAPE 

2.2 M-SAPE Tool Architecture 

There are two approaches to implement a multidisciplinary analysis system: tightly-coupled or 

loosely-coupled. In a tightly-coupled implementation, the modules are integrated at the module 

levels. This type of implementation results in a system with faster execution time, but it is 

difficult to implement and maintain. In a loosely-coupled approach, the modules are integrated at 

the application levels. This type of coupling is relatively easy to implement, modify, and 

maintain. However, there is an additional computational overhead, albeit a very small one for 

this implementation. M-SAPE uses a loosely-coupled implementation approach. 

The current M-SAPE implementation, geometry, mass sizing, impact analysis, and structural 

sizing are combined into a single integrated tool referred to as the parametric vehicle model. The 

aerodynamics model is incorporated into the flight mechanics tool. Figures 5 - 7 show M-SAPE 

implementation process from different perspectives.

As discussed before, M-SAPE is a loosely coupled, integrated problem with a strong feedback 

loop. The problem is solved using standard Gauss–Seidel approach, which has been very 

effective. Figure 5 shows a flowchart of M-SAPE overall solution process, which is broken into 

10 major steps (numbered in the order they are processed): 

1. Read and validate user inputs. 

2. Create relevant M-SAPE Python objects. 
8

3. Initialize objects and database. 

4. Start Gauss–Seidel iterative process by calculating mass and volume based on available 

information. 

5. Calculate trajectory. In early stages of convergence, a low-fidelity model is used. As 

convergence is reached, a high-fidelity model is used. 

6. Calculate heating environment. 

7. Calculate thermal soak response. 

8. Calculate TPS thickness. 

9. Assess M-SAPE model and convergence status. 

10. Create a report and update database. 

11. If convergence is not reached, go to step 5. 

12. End of process. 

Python language is used to wrap individual modules and integrate them together. Figure 6 shows 

an overview of M-SAPE implementation. Completed modules and connections are shown with 

solid lines, and those that are incomplete are shown with broken lines. The modules are designed 

to be independent, and interactions among them are managed by M-SAPE Driver that is also 

written in Python. 

Figure 7 shows a typical module integration using POST as an example. The figure is a sequence 

diagram that is used in unified modeling language (UML). This sequence diagram involves four 

modules: M-SAPE Driver, Post Wrapper, M-SAPE Utilities, and POST executable. The diagram 

is executed from top to bottom and from left to right. 

2.3 Web Portal 

M-SAPE users access the software through a Web Portal. To run a vehicle design case, users 

must set vehicle characteristics and then select the “Run M-SAPE” button on the Web page. 

The goal of M-SAPE Web Portal is to provide an intuitive, secure user interface and datatracking system. The tool allows approved users to utilize the M-SAPE software in a webenabled and secure environment. A PHP (Hypertext Preprocessor) user interface connected to a 

MySQL database on a NASA server was developed for this project. The PHP pages are 

accessible by a user’s browser as long as they are inside the NASA Langley firewall. 

Users must provide credentials to access the M-SAPE Web Portal. They can then run the MSAPE software for either a single entry vehicle analysis or a trade-space contour plot. They are 

also able to view and query previous M-SAPE data runs stored in the MySQL backend of the site 

(aka Database Module). There are over 80,000 unique entry vehicle designs stored in the MSAPE Web Portal. This easy-to-use system grants vehicle designers the functionality to obtain 

preliminary information about possible probe designs while maintaining data security via user 

authentication and limiting access to data based on a user’s privileges. 

The real strength of M-SAPE is its flexible handling of large amounts of data. System 

Administrators can upload massive datasets into the Database Module (via a PHP interface) 

where regular users can access, but not alter it. 
9

Figure 5. M-SAPE Flow Chart
10

Figure 6. M-SAPE System Integration (modules in tan color are in Python and sky-blue color are in 

the native application language) 
11

Figure 7. M-SAPE System Integration Example (UML Sequence Diagram) 
12

The most obvious advantage of the Web Portal system is that it provides authorized users a 

secure access to M-SAPE, enabling more streamlined development of entry vehicle prototypes 

for a wider audience in the future. 

2.4 Web Portal Development and Operational Approach 

The M-SAPE Web Portal system was designed to be flexible and intuitive to use. It was also 

constructed to function on a wide array of operating systems (OS) such as Windows, Macintosh 

and Unix/Linux and software environments such as Internet Explorer, Google Chrome, Safari, 

Opera, and Mozilla Firefox. PHP was chosen as the code for developing this system because it is 

a server-side scripting language that provides a user-interface via common Internet browsers. 

PHP can also connect to a MySQL database stored on the server, allowing users to access 

previously stored data and update data without granting them full access to the database. Finally, 

PHP is capable of calling other code on its server. The Web Portal was designed to make calls to 

M-SAPE code written in Python and return the results. 

The PHP pages built for the M-SAPE Web Portal are connected to a MySQL interface that is 

stored on a server. The PHP interface to the MySQL database can import, export, and display 

dynamic data. Datasets in different formats can be uploaded into the system. PHP identifies the 

format of the data based on the CSV file from which it is uploaded into the system. It then 

dynamically creates tables to store the information. For each new dataset created in the M-SAPE 

Web Portal, another storage table is created to identify what type of data is being stored in its 

associated table. Also, the M-SAPE PHP code identifies independent variables and creates 

indexes for each of these variables within the data storage tables. These indexes are vital for fast 

retrieval of queried data. 

Once data is uploaded to M-SAPE web portal, users can query this information from a browser 

interface. The interface provides a mechanism for querying both numeric and nominal (i.e., text) 

data. M-SAPE also can export this data to the user’s file system in the same format from which it 

was originally uploaded. 

It proved to be a challenge to integrate the PHP interface with the preexisting Python code from 

the M-SAPE project. This had to be implemented for both the contour plotting functionality and 

the single-run M-SAPE Python function. The PHP pages which run the Python scripts on the 

server side have to record user inputs, export data from MySQL into a CSV file that Python can 

read, call the Python script while passing user inputs and the input CSV file location as 

command line arguments, and then display the results of the call. The primary difficulty when 

doing this is due to timing as some Python M-SAPE routines can take several minutes to run in 

some cases. So, to display the results, PHP opens a separate pop up window that continuously 

refreshes and checks to see if the Python results have been generated. Once the M-SAPE Python 

code outputs its results, the pop up window automatically shows the user the resulting page. The 

M-SAPE Web Portal also logs users’ actions that an administrator can use for maintaining and 

debugging problems. 
13

2.5 Sample Web Portal Results 

As mentioned above, the web portal interface was built in PHP which can be opened in any 

common internet browser. Users can query the underlying MySQL database by entering query 

parameters into their browser and selecting a “Query” button on the displayed webpage. Figure 8 

shows an example screenshot where a user has queried the M-SAPE database to find all stored 

examples of entry vehicles with a carbon-phenolic forebody TPS and an Accusil aftbody TPS. 

This interface is designed to be intuitive and easy to use. Users can enter minimum and/or 

maximum values for numeric, independent variables as shown in Fig. 8. They can also select 

nominal, independent variables in the combo boxes displayed in the middle of the screenshot. 

The query returned by the system will be based on what the user has entered for these nominal 

and numeric variables. The number of results obtained by the query is displayed (in this case, it 

is 10,079 records), and the user has the option to export the results to a CSV file by clicking on 

the “Export Results to CSV” hyperlink. 

The M-SAPE web portal also has the ability to display contour plots of dependent, numeric data 

as shown in Figure 9 using either a K-dimension tree (KD-tree) or neural network plot prediction 

methods. The neural network plotting method also has the ability to provide nominal output plot 

shading. Users must first select independent, nominal variables for the plot. They must then 

select which numeric, independent variables will be used in the X and Y axes of the plot. Next, 

specific values for the remaining numeric, independent variables must be entered, and the 

numeric, dependent variables which the user would like to see displayed in the plot must be 

selected. When this has been done, users can click a “Plot” button in their browser, and a pop-up

window will appear which will continuously refresh until the contour plot is created. When the 

plot has been created by the underlying M-SAPE Python code on the server, it will automatically 

appear in the new pop-up window. Figure 9 shows an example of a contour plot where the 

Forebody Max Total Heat Rate, Max Entry Load, and Total Entry Mass have been plotted 

together. 

Figure 10 shows a single run of the Python M-SAPE code from the Web Portal. For a single MSAPE run, users must enter all desired values for nominal and numeric independent variables for 

the entry vehicle they want to design. The system will then test the validity of the vehicle design 

using the Python M-SAPE code on the server. A pop-up window will appear showing the results 

of the run. The results will include a cross-section of the entry vehicle design, a table showing 

details about the model and its performance, warnings about which variables had to be adjusted 

to close the user-desired design, and plots showing the vehicle’s altitude over time, total heat rate 

over time, deceleration over time, and altitude over relative velocity. A user’s previous M-SAPE 

results are stored in the Web Portal and can be accessed later by the user. The Web Portal also 

authenticates users on sign-in and tracks their use of the system. 

The Web Portal provides a number of advantages. First, users have an easy access to M-SAPE. 

Second, the security of the M-SAPE software can be maintained at just one location. Third, the 

code for M-SAPE can be maintained and updated at just one site. M-SAPE also stores data 

securely behind the NASA firewall. It requires user authentication and grants hierarchical rights 
14

based on whether or not users are also administrators. The Web Portal also tracks user actions 

and prevents unauthorized users from viewing the data and results stored in the MySQL database 

by specific users. 

Figure 8. Screenshot of M-SAPE Database Query and Results 
15

Figure 9. Screenshot of M-SAPE Sample Contour Plot 

The Web Portal provides a number of advantages. First, users have an easy access to M-SAPE. 

Second, the security of the M-SAPE software can be maintained at just one location. Third, the 

code for M-SAPE can be maintained and updated at just one site. M-SAPE also stores data 

securely behind the NASA firewall. It requires user authentication and grants hierarchical rights 

based on whether or not users are also administrators. The Web Portal also tracks user actions 

and prevents unauthorized users from viewing the data and results stored in the MySQL database 

by specific users. 

The M-SAPE Web Portal provides a structure within which users can securely access entry 

vehicle data. It grants access to M-SAPE Python code for users’ analysis without giving them 

direct access to the underlying software. Appendix D contains a User Guide and step-by-step 

instructions on operating the Web Portal interface.
16

 

Figure 10. Screenshot of M-SAPE Sample Single Run Results 

 
17

3.0 M-SAPE MODULES 

The current M-SAPE software tool consists of five loosely coupled Python modules, POST, a 

MySQL Database, and a Web Portal. As of this writing, the completed modules are: 

1) Parametric Vehicle Module 

2) Flight Mechanics Module 

3) Aerodynamics Module 

4) Aerothermal Earth Entry Module 

5) TPS Sizing Module 

6) Thermal Soak Module 

3.1 Parametric Vehicle Module 

The parametric vehicle module is a MATLAB script that is used to create the vehicle geometry 

in 2-D, and then rotate the geometry 360° to generate a 3-D vehicle model from which mass 

properties are estimated. An MSR and a non-MSR concept have been implemented (see Figure 

11). For the MSR concept, instead of simply setting the payload on top of impact foam sized to 

handle the stroke given an impact load requirement, an approach which encompasses the payload 

in both impact foam and an impact shell was also included. This design better approximates the 

MSR approach where increased reliability in off-nominal impact (e.g., backwards impact) is 

required. 

a. Non-MSR Model 

b. MSR Model 

Figure 11. M-SAPE Vehicle Models 
18

3.1.1 Inputs 

The input parameters were selected to accommodate possible future trade space expansion and/or 

to provide some insight into vehicle sensitivities. The full list of inputs for the parameter vehicle 

model includes: 

• MSR-mode designation (on or off) 

• payload mass 

• payload flag (payload diameter or density) 

• payload size 

• vehicle diameter 

• ratio of vehicle nose radius to vehicle radius 

• ratio of vehicle shoulder radius to vehicle radius 

• vehicle mass margin (applied to all components except the payload) 

• material selection (CP with ACC-6 or PICA with aluminum honeycomb) 

• forebody TPS thickness 

• vehicle terminal velocity 

• maximum entry load 

3.1.2 Rules and Constraints 

In addition to input parameters, several rules and constraints were added to the vehicle model to 

assist with convergence process. Given the large number of inputs and associated geometric 

relationships, it was necessary to provide some realistic boundaries to the vehicle model to keep 

the iterative process from straying too far from the solution. In some cases, simple constraints 

(i.e., minimum and maximum allowed values) on such parameters as structure or TPS thickness 

were sufficient. However, in many other cases, the complex geometry required the enforcement 

of several other rules in order to ensure the vehicle model would converge to a feasible solution. 

These rules included such things as: 

• Minimum vehicle diameter: Although the lower bound of the vehicle diameter in the 

mission trade space remained at 0.5 m, it is easy to see that given some payload mass and 

size inputs, a vehicle as small as 0.5 m would simply not close geometrically. In these 

cases, the vehicle diameter was gradually increased such that the minimum size vehicle 

was found that could accommodate the payload. In the cases where the input payload 

diameter was more than sufficient to accommodate the payload, no change was made. 

• Shoulder radius: For all cases, the shoulder radius is kept fixed for the vehicle if at all 

possible. However, some cases may arise that would require a change in this parameter. 

In those cases where the slope of the aft-side of the vehicle wings is too steep (steeper 

than the forebody angle of 30° from horizontal), the shoulder radius is allowed to 

decrease. This is done in order to avoid those vehicles where the wing is thicker at the 

tips than at the wing base. This geometry is expected to be very difficult to accommodate 

in manufacturing as well as likely in aerodynamic flexing. In addition, for those cases 

where there is insufficient room in the shoulder to accommodate the required geometry 

(i.e., very thick forebody TPS), the shoulder radius is increased to the smallest value 

which would allow the shoulder geometry to close. 
19

• Lid and attached structure placement: In closing the geometry during the iteration process 

in the parametric vehicle model, the most challenging aspect is the placement of the 

attached structure in relationship to the lid. In order to best ensure rearward aerodynamic 

instability (allow for self-righting prior to the heat pulse), it is best to keep the center of 

curvature of the lid at the vehicle CG. This makes the vehicle much less susceptible to 

variations or dispersions in the atmosphere and/or aerodynamics during rearward flight 

(since all normal forces on the lid would act through the vehicle CG and, thus, not result 

in a moment on the vehicle). Once the lid location is determined, the attached structure 

can be placed such that the lid is at most a hemisphere (can be less), and also provides the 

correct geometry for the wing (not shallower than 5°, but no steeper than 30°). This 

approach balances the need for a flexible geometry with the desire for rapid vehicle 

convergence. For the MSR-mode “On” cases, the lid was constrained to a full hemisphere 

in order to allow for reasonable access to the payload through the impact shell/sphere. 

For these cases, a constraint was applied that the payload center and the lid center of 

curvature must be coincident, and the attached structure must also be at the same height. 

The vehicle module iterates until there are no geometry changes (e.g., vehicle diameter or 

shoulder radius change) and the vehicle CG and attached structure locations converge to with 0.1 

mm. If a geometry change is flagged, then the vehicle is re-initialized with that change and the 

iterative processes starts over. A flowchart of parametric vehicle model is shown in Figure 12.

3.1.3 Validation 

Once the parametric vehicle model was complete, it was critical to validate the results for both 

the final geometry and estimated mass properties. Because a parametric Pro/Engineer Wildfire 

3.0 (ProE) was created with several of the more straightforward constraints and relationships 

described above, a setup already existed to generate a vehicle for a given set of inputs. A file 

interface was set up between MATLAB and ProE to provide the minimum number of inputs 

necessary to fully define the vehicle. This allowed for use of each piece of software to be used 

where it is strongest, i.e., MATLAB for the complex vehicle geometry iteration process and ProE 

in the design and mass properties calculations. This also allowed for continued use of the ProE 

model, integrated with M-SAPE, to provide CAD models in STEP or IGES format or other 

geometric data for other modules for such things as meshing. Several test cases were run for both 

MSR and MSR-modes. Input data was provided to ProE that generated an output file including 

geometric references (i.e., vehicle total height, attach structure location, etc.) as well as mass 

properties (CG and inertias) for both the total vehicle as well as the individual components. 

These were then compared against the MATLAB vehicle model output. In all cases, the 

geometry matched. For the mass properties, several different test cases were run to “tune” the 

MATLAB vehicle model (e.g., in the number of points which define a curve or the delta angle 

the vehicle is rotated at to generate the 3-D model) such that good comparisons could be made in 

the mass properties while also allowing the vehicle model to converge more quickly. In the end, 

all mass properties in the MATLAB model were within 5% of the ProE values, with total vehicle 

mass and CG location always better than 1%, and the mass inertias typically within 1−3%. 
20

Figure 12. Flow Chart for Parametric Vehicle Model 

 
21

The Parametric Vehicle Software Module consists of the following primary sub-components: 

• Geometry and Mass Sizing 

• Impact Analysis 

• Structural Analysis 

3.1.4 Geometry and Mass Sizing 

Geometry requirements are outlined in Section 3.1.1-3.1.2. This section provides additional CAD 

related discussions. A parametric vehicle model representing a simplified MMEEV model was 

developed based on the MSR EEV design in ProE. The CAD model was constructed from a 

series of curves in a “skeleton” model that references a set of geometric relations defined within 

the trade space. This approach allows for automatic vehicle regeneration as input parameters are 

modified. This skeleton model was built around certain dimensional assumptions that serve as 

constants in the geometric relations. 

Solid models of the MMEEV components were created from the curves defined within the 

skeleton file. Because the vehicle components reference these curves, they are recreated 

appropriately in response to changes in input parameters. Each component was assigned a userdefined density, allowing ProE to generate a new set of mass properties at the component and 

assembly level for each design. Analysis features were created to allow non-geometry properties 

such as ballistic coefficient to be defined as features in the model and thus be used as output for 

further analysis. 

The result is a parametric vehicle model for the MMEEV trade space, covering a range of overall 

vehicle diameters and payload masses as inputs and the overall vehicle mass and axial center of 

gravity as outputs. For this trade space, the center of gravity is assumed to be axial with no 

lateral component. 

From the parametric vehicle model, two generalized configurations can be created. The first is 

the “MSR” configuration, in which the payload is encapsulated within a foam-filled impact 

sphere as shown in Figures 13 and 15. The second is a “Non-MSR” configuration, in which the 

payload is nested in impact foam, with no surrounding impact sphere as shown in Figures 13 and 

14. 

Several geometric assumptions were made for these two parametric models. For both, the vehicle 

coordinate system (CSYS) is assumed to be at the virtual apex of the vehicle and the horizontal 

portion of the aftbody is 60 mm (Fig. 14). In the Non-MSR configuration, the distance between 

the payload and the lid structure is 50 mm (Fig. 15). In the MSR configuration, the impact shell 

is tangent to the FWD structure and the impact sphere is concentric with the payload. 

The geometric input variables for the parametric model are illustrated in Figures 16-18 with the 

variable description shown in black text with the model variable name shown in red. The model 

will automatically be regenerated based on these user-defined values. 

As part of MMEEV trade space studies, the user is able to vary several mass properties within 

the model. The CAD user-defined mass property input parameters and their respective ProE 
22

variable names are shown in the Table 1. Based on these values, ProE can generate mass 

property output values such as the vehicle mass, vehicle CG, and moments of inertia. 

Figure 13. Geometry Models for MSR and Non-MSR Vehicles 

 
23

Figure 14. Non-MSR Vehicle Assumptions 

Figure 15. MSR Vehicle Assumptions 
24

Figure 16. MSR and/or Non-MSR Vehicle Parameters 

Figure 17. MSR and/or Non-MSR Vehicle Parameters (Close Up) 
25

Figure 18. MSR Vehicle Parameters 

Table 1. Mass Property Output Values 

VARIABLE DESCRIPTION MODEL VARIABLE NAME 

AFT TPS Density DENSITY_AFT_TPS_M 

AFT Structure Density DENSITY_AFT_STRUCT_M 

Shell Foam Density DENSITY_LID_INSUL_M 

Payload Density DENSITY_PAYLOAD_M 

Skirt Density DENSITY_PRIMARY_STRUCT_M 

Impact Foam Density DENSITY_IMPACT_FOAM_M 

Aluminum Honeycomb Density DENSITY_WIND_INSUL_M 

Fwd Structure Density DENSITY_FWD_STRUCT_M 

FWD TPS Density DENSITY_FWD_TPS_M 

Impact Shell Density DENSITY_IMPACT_SHELL_M 

Body Foam Density DENSITY_BODY_FOAM_M 

Payload Mass MASS_PAYLOAD 

Mass Margin MASS_MARGIN 
26

3.1.5 Impact Analysis 

Two approaches were used to develop the impact model, and both approaches assume a 1-D and 

perfectly vertical impact. The first approach assumes all impact energy is transferred from a 

perfectly rigid body and payload through ground penetration. During MSR EEV development, 

penetrometers were used to perform ground characterization tests at the UTTR (Fasanella 2001).

These test data were then used to develop a simple empirical relationship that was used to 

determine peak deceleration of the EEV when penetrating the soft clay surface of UTTR: 

M

D V

G

tn

2

= α (1) 

where G is the peak deceleration (Earth g’s), Dn is the nose diameter (m) of the penetrometer or 

vehicle, Vt

 is the impact velocity (m/s), M is the total mass (kg) of the penetrometer or vehicle, 

and  is an empirical constant determined from test data ( is found to be between 27 and 29 for 

UTTR soft clay). The penetrometer results show that peak deceleration is a function of nose 

diameter. It may be counter intuitive that peak deceleration is inversely proportional to mass, but 

one can think of the heavier masses penetrating deeper, therefore having a longer stroke that 

spreads out the impact acceleration pulse. For vehicles reaching terminal velocity before the 

impact, the vehicle terminal velocity can be expressed as: 

C AV Mg D t =

2

2

1

ρ

 

 (2) 

where parameters , CD, A, Vt

, M, g are atmospheric density at impact, vehicle drag coefficient, 

drag area, terminal velocity, vehicle mass, and Earth gravity, respectively. Assuming the vehicle 

terminal velocity has been reached before or at impact, Eqs. 1 and 2 are combined into: 

D D

D

C

g

G

n

D

8 1





























=

πρ

α (3) 

where D is the vehicle overall diameter. Equation 3 shows that maximum acceleration is 

independent of mass and is inversely proportional to the overall diameter. This is only true for 

vehicles that have reached terminal velocity before impact. Figure 19 compares impact G’s 

results from Eq. 1 with over 17,000 M-SAPE runs calculated for a wide range of entry 

conditions, TPS types, vehicle concepts, and payload masses. 
27

Figure 19. Impact G’s Assuming Ground Penetration 

The second approach uses a simplified energy balance approach to understand the impact of the 

MMEEV with a perfectly rigid surface. In this case, since penetration is not possible, the vehicle 

and/or payload must be allowed to decelerate over some distance, or stroke, while transferring 

the kinetic energy by crushing a material designed for this purpose. Because the payload is the 

only critical element of the MMEEV that needs to survive, the mass and size of the payload are 

used to determine the resulting payload stroke distance:

G g

Vt

× ×

=

2

Stroke

2

 (4)

where Stroke is payload stroke (m), G is the design impact load (Earth g’s), and g is Earth 

acceleration due to gravity (m/s2

). For MSR concepts, a triangular acceleration pulse is assumed, 

and stroke is multiplied by a factor of two. Combining Eqs. 2 and 4 results in: 

C A

M

G D

= β =

ρ

β

Stroke , (5) 

This equation shows that stroke is linearly proportional to the vehicle ballistic coefficient () at 

impact. Figure 20 shows stroke calculated using Eq. 5 and M-SAPE run results for over 17,000 

M-SAPE runs calculated for a wide range of entry conditions, TPS types, vehicle concepts, and 

payload masses. Equations 3 and 5 are accurate for estimating the impact loads and required 

stroke. 

Once the stroke length is known, the compression strength, FS (Pa), of the foam is calculated as 

a function of the Stroke, payload mass (MPL), and payload reference area (Ap). 
28

 (6) 

With the stroke length and the stroke efficiency factor, the required foam thickness and 

compression strength can be calculated. 

Combining the stroke and compression strength calculations causes the velocity term to drop out, 

making the compressive strength of the foam solely a function of payload mass. 

Figure 20. Required Stroke Comparison with Eq. 5 

3.1.6 Structural Analysis 

The structural analysis in this section is limited to EEV MSR concept as shown in Fig. 13. The 

EEV structural analysis was divided into two analysis modules: an impact module and a 

structural dynamics module. The EEV requires an impact absorbing sphere called an impact 

sphere (IS) to protect the payload during the severe collision with Earth. The IS shown in Fig. 

21 is a lightweight high energy absorption structural system comprised of radially oriented 

interlocking hybrid fiber composite panels and crushable foam filled chambers. The features of 

the impact sphere allow the structure to be subjected to progressive crumpling failure rather than 

less efficient buckling failure. Numerical representation of the interactions between stiff and soft 

components under high-rate compressive loading is difficult and often fails to reproduce the 

2 Stroke

FS

2

×

×

=

p

PL t

A

M V
29

proper mechanics. To incorporate such intricacies, in this analysis methodology, complex 

structural interactions and a wide range of failure physics are uniquely addressed. Numerical 

models using the developed methodology were created and benchmarked against experimental 

tests conducted at the Impact Dynamics Research Facility at NASA Langley Research Center. 

The post impact structural damage assessment showed close correlation between simulation 

predictions and experimental results. Acceleration, velocity, displacement, damage modes, and 

failure mechanisms were all effectively captured. 

Two fully parametric finite-element models were developed for the structural analysis of the 

EEV. The structure primarily responsible for the impact response of the EEV is the IS, which 

was the focus of the first analysis module. A biplane cross sectional view of the FE model for 

the first module is shown in Fig. 21, which different elements have been selectively hidden so 

that most components are visible. The key challenge in the developing the EEV is ensuring the 

containment and integrity of the samples contained within the IS. Thus when designing the EEV 

much effort was spent focusing on the IS and its ability to meet these requirements. More details 

can be found in Perino et al. 2013 and 2014.

Figure 21. A Biplane Cross Section View of the Finite-Element Model of Impact Sphere 

The second structural dynamics analysis module was developed for evaluating the quasi-static 

and dynamic structural response of the vehicle during launch and entry (see Fig. 22). During 

launch and reentry the EEV may experience large structural loads from multiple sources. In an 

effort to decrease development time and cost, a fully parametric and automated finite element 
30

analysis methodology was developed. In contrast to the typical analysis method where models 

are built manually using a pre-processor GUI, the developed methodology uses a parametric 

technique to build EEV models. The parametric technique allows rapid modification of nearly all 

aspects of the model including: geometric dimensions, material properties, loads and boundary 

conditions, mesh properties, and analysis controls. Furthermore once model parameters are 

defined, all required analyses can be performed automatically. Given the proper computational 

resources, the developed methodology can be used to rapidly generate data for thousands of 

potential EEV configurations. A range of analyses including quasi-static inertial, structure born 

vibration frequency response, random acoustic, and aeroelastic analyses are automatically 

executed. Preliminary parametric analyses indicate that the quasi-static load cases induce the 

highest stresses near the structural attachment points. Cone angle and vehicle diameter have 

been identified as two parameters that have strong influence on vehicle mass and structural 

response. These investigations demonstrated that the two structural analysis modules have great 

potential in facilitating future planetary exploration missions. More details can be found in 

Perino et al. 2013 and 2014. 

Figure 22. Finite-Element Model of EEV for Launch and Entry 

3.2 Flight Mechanics Module 

The POST2 software is a generalized point mass, discrete parameter targeting and optimization 

program. POST2 provides the capability to target and optimize point mass trajectories for 

multiple powered or unpowered vehicles near an arbitrary rotating, oblate planet. POST II has 

been used successfully to solve a wide variety of atmospheric ascent and reentry problems, as 

well as exoatmospheric orbital transfer problems. The generality of the program is evidenced by 

its multiple phase simulation capability which features generalized planet and vehicle models. 

This flexible simulation capability is augmented by an efficient discrete parameter optimization 

capability that includes equality and inequality constraints (Brauer et al, 1977 and Striepe et al. 

2004). Some other projects that have used POST2 include the Mars Exploration Rover for EDL 
31

analyses, Genesis, Stardust, X-43 (Hyper X), Huygens Probe, Mars Phoenix Lander, and many 

others. It has also been used on flight projects such as Mars Science Laboratory (MSL), Orion, 

Ares, Space Launch System (SLS), and Launch Abort System. Figure 23 gives a graphical 

representation of how MSL models are integrated into POST2. 

Figure 23. Model Integration into POST2, MSL Example 

Many fewer models are required for the MMEEV application as the vehicle is unguided and 

ballistic. However, initial entry conditions, aerodynamic, aerothermodynamic, planet, 

atmosphere, and vehicle mass properties models are still required. The POST2 simulation uses a 

4

th order Runge-Kutta numerical integration method for the MMEEV application, although other 

methods can be chosen in POST2. Simulations may be run in either a 3-degree of freedom (DoF) 

or 6-DoF mode, where 3-DoF is the translational equations of motion and the rotational 

equations of motion are also integrated in the 6-DoF mode. 

The planet model is an oblate planet based on the one adopted by the U.S. Air Force Satellite 

Control Facility from the 1960’s forward, taken from Mercer, 1965. Table 2 contains a list of 

Earth gravitational moment coefficients, which are the dimensionless parameters describing the 

Earth’s gravitational field. The listed values are the first three geopotential coefficients, J2-J4, for 

a non-spherical planet. 
32

Table 2. Geopotential Coefficients 

J2 1.0823x10-3

J3 0.0 

J4 0.0 

The corresponding rotation rate of the planet is 7.292115x10– 05 rad/s, the gravitational constant 

() is 3.986x1014 m

3

/s2, the equatorial radius is 6378.1 km, and the polar radius is 6356.8 km. 

Atmospheric interface is defined at a geodetic altitude of 125 km. The initial state is selected to 

be representative of the UTTR landing constraint. While not landing at UTTR, it was decided to 

direct the vehicle towards UTTR. The initial geodetic latitude used was 33.0° north and the 

initial longitude was 246.3833° (east longitude relative to the Prime Meridian). More in-depth 

studies may start at spacecraft separation to account for perturbations due to the separation event. 

The initial velocity and flight path angle were varied in the trade space. For 6-DoF mode, the 

attitude rates are initialized through body rates. The ideal would be zero pitch and yaw body 

rates and only a roll body rate. The roll body rate is used to provide gyroscopic stability to 

counter aerodynamic perturbations. This method has been used on many previous missions such 

as Stardust and Genesis at Earth (Mitcheltree et al. 1999). Perturbations from the spacecraft 

separation event must also be accounted here, usually in Monte Carlo simulations. 

The atmospheric model used in the simulation is the 1976 U. S. Standard Atmosphere. Future 

iterations may use Global Reference Atmosphere Model (GRAM) (Justus et al. 1999, Justus et 

al. 2007, and Leslie et al. 2011) and/or a specific range reference atmosphere for the landing site. 

No winds are included for this current trade space study. 

The vehicle aerodynamics is incorporated into the POST2 simulation as a FORTRAN routine. 

The moment reference center is defined at the virtual apex of the cone of the vehicle. Section 3.3 

and 3.4 describe the aerodynamics and aerothermodynamic models, respectively. 

The aerothermodynamics in the POST2 simulation are based on a Sutton-Graves (Sutton and 

Graves 1972) estimation of the convective heating and a Tauber-Sutton (Tauber and Sutton 

1991) estimation of the radiative heating (Tauber 1991). The Sutton-Graves constant used was 

1.74153x10-4. Section 3.3 describes the comparison to computational fluid dynamics (CFD) in 

further detail. The nose radius, reference area, and reference lengths varied based on the overall 

vehicle diameter in the trade space. 

The POST2 simulation event sequence is simple since this is a ballistic and unguided vehicle. 

For the trade space studies, only monitor events were used. These events monitor quantities like 

altitude, peak heating, peak entry deceleration, and touchdown. The events are used to pull 

information out from the results for compilation. 
33

3.3 Aerodynamic Module 

The MMEEV capsule will perform a stable, yet uncontrolled, atmospheric entry and deceleration 

through the hypersonic and supersonic regimes, and will reach subsonic velocity at a high 

altitude of approximately 25 km. A representative trajectory profile is shown in Figure 24. To 

better resolve the time scale of deceleration, only the first half of the entry duration is shown. 

The latter half (not shown) contains the remainder of subsonic flight over roughly 25 km of 

altitude (82,000 ft). Figure 24 illustrates the amount of time during entry that is spent at a low 

subsonic velocity, thus stressing the importance of characterization of subsonic aerodynamics. 

The aerodynamics database of MMEEV uses a range of sources, including Direct Simulated 

Monte Carlo (DSMC), CFD, wind tunnels and ballistic range data. Aerodynamic performance in 

the rarefied atmosphere is described by collision-less calculation with DSMC Analysis Code 

(DAC) for angles of attack from zero to 180 degrees. Hypersonic aerodynamics is described by 

non-equilibrium calculation of Langley Aeroheating Upwind Relaxation Algorithm (LAURA) 

for high flight enthalpy, and perfect gas air wind tunnel data at low enthalpy. Rarefied and 

hypersonic continuum data are blended through the transitional region by interpolation on 

Knudsen number. The validity of this approach was confirmed by comparison with the full 

DSMC calculations. At low velocities, wind tunnel data is used. 

Figure 24. MMEEV Representative Trajectory 

3.3.1 Summary of Data Sources 

• Free-molecular static aerodynamics based on the collisionless DAC calculations. 
34

• Hypersonic static aerodynamics are computed using LAURA code with the perfect gas 

air model (Cheatwood 1996) 

• Low supersonic and high subsonic static aerodynamics were measured in the Ames 2x2 ft 

transonic wind tunnel by Marko 1968 and Walker et al. (1967). 

• Low subsonic statics and dynamics obtained at NASA Langley in the Vertical Spin 

tunnel (VST) by Mitcheltree, Fremaux, and Yates (1999). 

• Transonic and supersonic dynamics from Viking Project (Steinberg 1970) 

• Collision-less DAC solutions on the updated shape. Consistency of trends between 

collision-less, DSMC and hypersonic continuum LAURA results were verified. 

• Non-equilibrium LAURA results for high hypersonic regime to replace earlier perfect gas 

calculations. 

• Walker and Weaver 1967 statics data for Mach 2.2 to 9.5 to blend high hypersonics to 

transonic data from Marko (1968) that is used in the database. 

The moment reference point for static aerodynamics was shifted to the virtual nose of the 

vehicle. This point is the cone virtual apex point, and is generally a few centimeters ahead of the 

physical nose. It is chosen because the physical nose presents a challenge, when datasets come 

from different experiments with different values of nose bluntness. 

 Figure 25. MMEEV Supersonic, Hypersonic, and Rarefied Flow Pitching Moment at 0.2D 

Cm 

3.3.2 Rarefied and Hypersonic Aero Data 

Substantial updates were made on the V_021 MMEEV aero database to address the area of pitch 

stability in the 2010 timeframe by Artem Dyakonov. Figure 25 is the summary of the high-speed 

aerodynamics pitching moment data that is available for use for the MMEEV. 
35

In the free-molecular regime the differences between the previous calculation on the older 

iteration of the capsule’s shape (shape 1) and the recent calculation on the shape from the family 

of the MMEEV contours (shape 2) is represented by the green and black curves in Figure 25. 

Some differences occur at near 40°, near 90° as well as near 140°. Near 90° the differences can 

be traced to the larger overhang of the center aftbody for the shape 1. In general, however, it is 

difficult to quantify these differences because the work on shape 1 was at least ten years old and 

the procedures used in these calculations could not be verified. It should be pointed out that in 

the free-molecular regime, under the assumption of the accommodation coefficient of unity, the 

body shape matters only so far as it affects the body outline in the direction of the incident 

velocity vector. The details within the outline do not matter. 

Hypersonic pitch stability was described by perfect gas calculations in LAURA, which were 

performed on shape 1 (shown in orange through the entire range of incidence). Shown for 

comparison are the plots of real gas calculations on an updated shape, as well as in Walker 1967 

low angle of attack data from the perfect gas wind tunnel. Walker’s data is entered into the 

database for low Mach numbers, though not presently used. Prediction of hypersonic 

aerodynamics of a sphere-cone in air by CFD is considered reliable for nominal (nose forward) 

flight, and it is unlikely that experimental data will have to be used to fill the aero database in 

this regime. Transition between the hypersonic and supersonic datasets in the aero database is 

handled through interpolation on the free-stream Mach number. 

Figure 26. CFD Computed Flow Field at 100° Degrees Angle of Attack. 

The potential, however small it may be, for the aftbody-forward entry does require some amount 

of analysis of the trim characteristics in this regime. In the free-molecular and transitional 

regime, the capsule will tend to right itself, as shown in the Figure 25. However, a previous 

investigation by Buck and Cheatwood (unpublished) has shown capsules of this shape stable in 

reverse orientation in the hypersonic continuum, as witnessed by the slope of the trim curve in 
36

Figure 25 near 180° degrees angle of attack. Predictive capability of CFD is somewhat degraded 

for high angles of attack because of the flow complexity. Figure 26 shows the computed flow at 

100° degrees angle of attack. As the figure indicates, there is potential for shock interaction and 

unsteadiness, and because of that, the high angle of attack aerodynamics are uncertain. 

It is possible to address this problem in the future through one of the following venues: 

• Various passive aerodynamic aides can be added to avoid hypersonic backward stability 

altogether. Impact of these aerodynamic aides on capsule mounting, TPS etc. should be 

assessed. 

• Design of sufficiently conservative margin and demonstration of robustness to it. 

• Comprehensive definition of conditions that would lead to a backward stable entry. 

• Reassessment of the likelihood of backward conditions. 

To demonstrate robustness for MMEEV, an analysis was carried out where a high fidelity, six 

degree-of-freedom trajectory tool, together with the existing aerodynamics aero database, was 

tested across a range of flight path angles, entry velocities and ballistic coefficients. The results,

shown in Figure 27, indicate passive reorientation before the onset of continuum flow. Center of 

gravity (CG) placement was shown to be a significant driver. While these results indicate that reorientation is possible, they do not represent what could be a worst case scenario, i.e., a vehicle 

with a non-zero rotation rate. Preliminary simulations indicate that even for small pitch rates, on 

the order of one to two degrees per second, a vehicle could end up with a rearward entry if it 

entered the atmosphere at the wrong time. While the MMEEV vehicle is not expected to be 

rearward stable in the rarefied flow regime, the very low dynamic pressures that are acting on the 

vehicle during this period of flight would be unable to exert much force. It should be noted that 

further research and testing is needed in this area to achieve very high reliabilities. 

The aerodynamic data are provided in Appendix A in Figures A1, A2, and A3, for the rarefied 

flow, Mach=24 and Mach =9.8, respectively. For these Figures, the moment reference center is 

the virtual nose apex of the vehicle. 

3.3.3 Supersonic Aero Data 

The supersonic aero data for the MMEEV is defined for M=3.98 and provided in Appendix A in 

Figure A4. 

3.3.4 Transonic Aero Data 

Transonic data in the database is derived from the experimental data obtained by Marco 1968 on 

models with a nose radius 15% of the base diameter. It was previously shown that for small 

angles of attack the nose radius is not a major driver, but this area will need to be revisited. 
37

The transonic Mach data were defined for M=1.3, 1.15, 0.85 and 0.75 and aerodynamic data are 

provided in Appendix A Figures A5, A6, A7, and A8, respectively.

Figure 27. Time History Data for Several Trajectories with Different CGs (Cms). 

Baseline CG = 0.2D 

3.3.5 Subsonic Aero Data 

Low subsonic static and dynamic characteristics were obtained by Mitcheltree as a result of 

experiments in the Langley Vertical Spin Tunnel (VST) (Mitcheltree, Fremaux, & Yates 1999). 

This data is in the form of curve fits, and some assumptions are made about applicability across 

the range of the Reynolds number. No work has been done to assess those assumptions in the 

context of MMEEV. Data is used as is, with the intent to revisit it in later analyses. Aerodynamic 

data for Mach numbers of 0.6 and 0.5 and provided in Figures A9 and A10. 

3.3.6 Summary of all Static Data across the Mach Number Range 

This section provides summary plots for all of the Mach numbers in the V-021 aero database. 

Providing the data in this format provides the ability to evaluate Mach effects as they are 

currently modeled. Figures 28 and 29 provide a summary of the static aerodynamic data for all 

Mach numbers. From these Figures it can be seen that there are high levels of CN, CM, and CA for 

rarefied flow conditions. These levels decrease for M=24 and then continue to decrease through 
38

the Mach number range down to M=0.6. However, between Mach numbers of 0.5 to 0.6, there is 

a large increase in CN, CM, and CA which is not expected. 

Figure 28. MMEEV Normal Force Coefficient for All Mach Numbers 

3.3.7 MMEEV Dynamic Data 

The MMEEV dynamic aero database is composed primarily of Viking data and some data from 

the LaRC 20-FT wind tunnel (for Mach numbers below 0.5). Figure 28 provides the MMEEV 

dynamic damping data. Viking damping data covers Mach numbers from 2.1 down to 0.7. For a 

Mach number of 0.5, pitch damping is provided from the equation provided below. Above 10°

degrees angle of attack, the Viking vehicle is indicated to have nearly constant pitch damping as 

shown by the relatively constant negative values of Cmq over this range of angle of attack. 

Below 10° degrees, there is a strong decrease in pitch damping and some unstable damping for 

Mach numbers above M=0.9. Bottom of Figure 30 also provides an expanded view of the Viking 

data below 10° degrees angle of attack. As can be seen from Figure 30, the low-speed subsonic 

damping data for M=0.5 is significantly different from the data at the next highest Mach number 

(0.7). More work is needed to define the damping data for the MMEEV across the speed range, 

especially in the low-speed area where it is likely to be the most critical. 

The subsonic pitch damping is provided by the following equation: 



 	 
 
  
 
    
 



 (7) 

where: 

	   !"
39


  #!#$

 %&'()*+%,,%-./&0%1/%&2



 #!#345





  #!6$

  78  9)
cos(:  78  9

: 
 2/&7:

8  #!"3; 
 -*27:

9  #!4$3  #!$ 
 2/&7:

8  #!"3; 
 -*27:

9   2/&7: 
 -*27:

The V_021 database includes several blending functions involving the dynamic data. Below 

M=0.5, the subsonic damping data as defined above is used. Between Mach numbers of 0.5 and 

1.5 a blending of the subsonic and Viking data are used. The Viking damping data are used up 

through hypersonic Mach numbers. At Knudsen numbers greater than 0.001, a blending of the 

Viking data with zero damping is performed up to Knudsen number of 10.0. For Knudsen 

numbers greater than 10, zero pitch damping is assumed. 
40

Figure 29. MMEEV Pitching Moment and Axial Force Coefficients for All Mach Numbers 
41

Figure 30. MMEEV Pitch Damping Data from M=0.5 to 2.1 
42

3.4 Aerothermal Module 

This section describes the computational fluid dynamic (CFD) aerothermal analyses for the MSAPE application. The goal for this analysis is to provide heating estimates for the probe surface 

that could be further used for thermal soak analysis (Agrawal and Sepka, et al. 2012) and to 

establish correlation coefficients for aerodynamic heating based on the Sutton-Graves (Sutton 

and Graves 1972) equation for the vehicle that could be used for rapid design trades. An 

additional outcome was to study the effect of varying the shoulder radius while keeping the nose 

radius and vehicle diameter constant. 

The concept vehicle’s TPS model must be designed for a large matrix of entry velocities (10 to 

16 km/s), ballistic coefficients (42 to 129 kg/m2) and entry flight path angles (−5 to −25 

degrees). An aerothermal database is necessary to understand the environments the vehicle could 

experience. The parametric space consists of too many cases for a high-fidelity CFD code to 

perform affordable characterization of the heating. An engineering approach which is CFD 

anchored was established to quickly characterize quantities pertinent to TPS design, such as heat 

flux, heat load, and surface pressure. The engineering approach uses outputs from POST II and 

Data Parallel Line Relaxation (DPLR) CFD code based correlations to find surface quantities. 

For addition details, readers are referred to Aliaga-Caro, Zarchi, and Sepka 2013. 

3.4.1 Vehicle Geometries 

Three capsule geometries–version 1 (v1), version 2 (v2), and version 3 (v3)–have been 

considered and are shown in Figure 31. Table 3 lists the configurations’ defining geometric 

dimensions. 

A series of CFD simulations were conducted to determine how CFD compared with the SuttonGraves correlation for all geometries. 

A parametric study was conducted for the v3 geometry to assess the affect of shoulder radius on 

maximum shoulder heating. Shoulder radius was varied at one Mach number to determine 

change in shoulder heating versus the shoulder radius non- dimensionalized with vehicle radius, 

Rs

/Rb. Additionally, one trajectory was run for two different shoulder radii (0.0234 and 0.0384 

meters) to determine the change, if any, of the fraction of maximum shoulder heating over 

stagnation point heating as a function of Mach number. For the Sutton-Graves comparison, all 

simulations of the v3 geometry were performed on the smallest shoulder radius as the highest 

heating is expected to occur at the shoulder for this radius. For this set of analyses, only the 

forebody of the v3 geometry was considered. 

Table 3. Geometry Configurations 

v1 v2 v3 

Nose Radius [m] 0.35 0.132 0.352 

Vehicle Diameter [m] 1.08 1.057 0.900 

Shoulder Radius [m] 0.025 0.048 0.02340-0.0495 
43

Figure 31. Geometries are Superimposed (v1 = red, v2 = black, v3 = blue). Only the Fore-body 

(from the nose of the vehicle to the apex at the shoulder) of v3 is used for CFD Simulations 

3.4.2 CFD Methodology 

The DPLR v. 4.02 CFD code was used for all the analyses (Wright et al. 2009). It is a parallel, 

structured multi-block, finite volume code that solves the reacting Navier-Stokes equations for 

continuum flow, including finite rate chemistry and thermal non-equilibrium. In the present 

study, the Euler fluxes are computed using modified Steger-Warming flux vector splitting 

(MacCormack and Candler 1989) with third-order spatial accuracy via Monotone Upstreamcentered Schemes for Conservation Laws (MUSCL) extrapolation coupled with a min-mod 

limiter (Yee 1989). The viscous fluxes are computed to second-order spatial accuracy with a 

central difference approach. 

The surface of the vehicle was modeled using the radiative equilibrium wall boundary condition 

with a constant emissivity of 0.85. The flow field was assumed laminar throughout the entire 

trajectories and was modeled using an 11-species finite-rate air chemistry model. Transport 

properties are computed using Yos mixing rule and multi-species diffusion coefficients are 

computed using consistent effective binary diffusion. At least ten points were chosen along each 
44

trajectory to capture the heat pulse. Three grid adaptations were performed for each location 

along the trajectory. 

3.4.3 CFD Results 

Convective Heating: 1.33 Factor 

A common engineering relation for convective heating is the Sutton-Graves (Sutton and Graves 

1972). The Sutton-Graves correlation is defined as:

qw = C

ρ ∞

Rn

V∞

3

 (8) 

where qw is heat flux (W/cm2

) at the stagnation point, C is a constant with value of 

1.7415310-4 for Earth, Rn

 is the nose radius (m) and ρ ∞ and V∞ are the freestream density 

(kg/m3

) and freestream velocity (m/s), respectively. 

The first parametric trade space for the v1 geometry is made up of 840 trajectories ranging in 

entry velocities (V∞

) from 10 to 16 km/s, ballistic coefficients (β) of 42 to 129 kg/m2 and entry 

flight path angles (γ) of -5 to -25 degrees. A number of DPLR cases were performed on the v1 

geometry that spanned the parameter space (Table 4). Figure 32 shows a representative Mach 

contour at V∞

=14 km/s, γ=-7 deg, and β=63.4 kg/m2 for this geometry. By holding two 

parameters (entry velocity, flight path angle or ballistic coefficient) constant and varying the 

third, a correction factor to stagnation point heat flux was found to bring the Sutton–Graves 

correlation closer to the DPLR heat flux. All CFD simulations were run at peak heating. The 

correction factor to Sutton-Graves for the stagnation point was found to be 1.33. Figures 33, 34, 

and 35 show the correction factor agrees well with DPLR predictions. 

Table 4. CFD Trade Space for v1 Geometry 
45

Figure 32. Mach Contour for the v1 Geometry 

Figure 33. Correlated Heat Flux with Varying Velocity as Compared to DPLR and Sutton-Graves 
46

Figure 34. Correlated Heat Flux with Varying Entry Flight Path Angles as Compared to DPLR and 

Sutton-Graves

Figure 35. Correlated Heat Flux with Varying Ballistic Coefficient as Compared to DPLR and 

Sutton-Graves 
47

The second set of trajectories for the v2 and v3 geometries consisted of over 2700 cases each. 

Entry velocities for these trajectories range from 10 to 16 km/s. The range of ballistic 

coefficients is from 42 to 129 kg/m2

 and the range of entry flight path angles is from -5° to -25°

degrees. From these, a representative set of trajectories for the CFD analyses was selected based 

on the statistical average and standard deviations of peak heating and heat load. The aim of the 

CFD simulations of the v2 and v3 geometry was to confirm the 1.33 correction factor derived 

from a different geometry and trajectory space. Figures 36 and 37 show the DPLR stagnation 

point heat fluxes for one trajectory for each of the v2 and v3 geometries respectively. CFD 

results lie within the 1.33 correction indicating the correction factor is conservative. Figures 38 

and 39 show Mach contours after the grids were aligned with the shock. 

Figure 36. Stagnation Point Heat Flux Along a Trajectory for the v2 Geometry. 

V∞ = 14.0 km/s, γ = -15° degrees, β = 15 kg/m2

. 
48

Figure 37. Stagnation Point Heat Flux Along a Trajectory for the v3 Geometry. 

V∞ = 14.0 km/s, γ = -8° degrees, β = 25.67 kg/m2

. 

Figure 38. Mach Contour after Grid Adaptation for the v2 Geometry, 

V∞ = 14.0 km/s, γ = -15° degrees, β = 15 kg/m2

. 
49

Figure 39. Mach Contour after Grid Adaption for the v3 Geometry, 

V∞ = 14.0 km/s, γ = -8° degrees, β = 25.67 kg/m2

. 

Pressure 

For the v1 geometry, the stagnation point surface pressure was found with a curve fit of DPLR 

pressure values to the Newtonian relationship: 

m n

p = Cρ V (9) 

where C = 0.792, m = 1.00357 and n = 2.02529. The pressure fit is well characterized as seen in 

Figure 40 where the correlation goes directly through all of the DPLR data points. 

Figure 41 shows the same equation applied to the predication on the v3 geometry. The pressure 

fit again goes through all the DPLR data points. Also plotted in these graphs are the M-SAPE 

values for pressure. M-SAPE uses the equation: 

Cp = CP max sin2θ (10) 

CP max =

2

γM∞

2

( ) γ +1

2 M∞

2

4γM∞

2

− 2( ) γ −1





	








γ

( ) γ −1

1−γ + 2γM∞

2

γ +1





	






 −1





















 (11) 
50

p − p∞ = CP max

1

2

ρ ∞V∞

2

 (12) 

where θ = 90 degrees, CP max

 is the stagnation point pressure coefficient, γ is the ratio of 

specific heats and M∞

 is the freestream Mach. In the limits as and γ =1, CP max = 2. 

So the stagnation pressure becomes two times the freestream dynamic pressure. The M-SAPE

values for pressure are conservative against the DPLR values. 

Affect of Shoulder Radius 

A parametric study using the v3 geometry was conducted at the peak heating point of one 

trajectory, at Mach 30, to see the shoulder radius effect on maximum shoulder heating. Nose 

radius and vehicle diameter were kept constant while the shoulder radius was varied. Figure 42 

shows non-dimensionalized shoulder heat flux as a function of Mach number for the v3 

geometry. A power law correlation between the maximum shoulder heat flux and nondimensional shoulder radius was established, with decreasing heat flux corresponding to 

increasing shoulder radius. 

Figure 40. Stagnation Pressure for the v1 Geometry, 

V∞ = 14.5 km/s, γ = -7° degrees, β = 50.5 kg/m2

. 
51

Figure 41. Stagnation Pressure for the v3 Geometry,

V∞ = 11.0 km/s, γ = -5° degrees, β = 30.75 kg/m2

. 

Figure 42. Non-dimensionalized Shoulder Heat Flux as a Function of Mach Number for the v3 

Geometry, 

V∞ = 12.0 km/s, γ = -30° degrees, β = 70.73 kg/m2

. 
52

Turbulence 

Turbulent simulations were performed for all cases of the v3 geometry. The turbulent model 

used was the Baldwin-Lomax model. This model assumes a fully turbulent flow everywhere 

along the surface of heat-shield. Figures 43 and 44 show turbulent solutions plotted against the 

laminar solutions for two instances along one of the trajectories. 

Transition from laminar to turbulent flow is characterized by the value of the momentum 

thickness Reynold’s number, Reθ

. Experimental data has found transition to turbulence occurs at 

Reθ

 of 200 to 300 (Hollis 2012). Figure 45 shows the time history of the maximum value of Reθ

for all trajectories—this occurs in the vicinity of the shoulder region as seen in Figure 42. For the 

majority of the cases considered here, Reθ

 remains below 200 indicating laminar flow. It is only 

for Case 4 and Case 5 that Reθ

 gains a value above 200, but below 300, indicating transitional 

flow. Figure 45 shows the calculated Reθ

 along the probe for Case 5 corresponding to the 

solutions of Figure 43 and 44, respectively. 

Figure 43. Heat Flux Comparison between Laminar and Fully Turbulent Solutions at an Early 

Point in the Trajectory 

 
53

Figure 44. Heat Flux Comparison between Laminar and Fully Turbulent Solutions at Peak Heating 

Along the Trajectory 

Figure 45. Time History of the Maximum Value of Momentum Thickness Reynolds Number for 

the v3 Geometry. (Circled values correspond to peak heating for each trajectory.) 
54

3.5 TPS Sizing Module 

Mass estimating relationships (MERs) were formulated to determine a vehicle's Thermal 

Protection System (TPS) required thickness for safe Earth entry. The objective of this study was 

to develop MERs using simple correlations that were non-ITAR and matched as accurately as 

possible NASA’s high-fidelity ablation modeling tool, the Fully Implicit Ablation and Thermal 

Analysis Program (FIAT) (Chen and Milos 1999). These MERs would be a first-estimate for 

feasibility studies; it is understood that higher-fidelity modeling like FIAT would be necessary 

once a proposed trajectory was down-selected. The trajectory space for these MERS consisted of 

840 different trajectories, and a material’s heating limit was the main constraint for an allowable 

trajectory. MERs for the vehicle forebody included the ablating materials Phenolic Impregnated 

Carbon Ablator (PICA) and Carbon Phenolic atop Advanced Carbon-Carbon. For the backshell 

the materials were Silicone Impregnated Reusable Ceramic Ablator (SIRCA), Acusil II, SLA561V, and LI-900. The MER/FIAT ratio indicates MERs are accurate to within 14% (at one 

standard deviation) of FIAT prediction, and the most any MER can under-predict TPS thickness 

is 19% of FIAT prediction. MERs resulting from these analyses are used to determine the preflight mass of the vehicle TPS. 

3.5.1 Background 

A typical re-entry problem requires computational aerothermodynamics to understand the flow 

conditions around the vehicle and to determine the convective and radiative heating to its 

surface. Once the surface heating is known, appropriate TPS materials can be chosen. A TPS 

material response model would then be used to determine the amount of heat shield material 

required to keep its bond line temperature below a specified value and to know the amount of 

surface recession. The traditional approach for this coupled problem would be to first use a high 

fidelity computational fluid dynamics code such as DPLR or LAURA for the aerothermal 

component and then FIAT (Chen and Milos 1999), CMA (Curry 1965) or STAB (Moyers and 

Rindal 1968), for the TPS response. However, this coupled approach usually had a very slow 

turnaround time and was highly dependent upon analyst availability. To circumvent these issues, 

M-SAPE employs correlations to bypass these codes with as minimal a loss in accuracy as 

possible. 

To determine the aerothermal environment, M-SAPE uses the Sutton-Graves (Sutton and Graves 

1972) correlation for the convective heating and Tauber-Sutton (Sutton and Graves 1972) for 

radiative heating. To date, however, no correlations based on high-fidelity FIAT modeling have 

been determined. The current work was to develop MERs using FIAT-based correlations with as 

high accuracy to FIAT prediction as possible. Toward that end, six MERs have been developed. 

For the vehicle forebody, the ablators were Phenolic Impregnated Carbon Ablator (Hui and et al. 

1996) (PICA) and Carbon Phenolic (Clements and Ward 1966) atop Advanced Carbon-Carbon. 

For the vehicle backshell, the materials were Silicone Impregnated Reusable Ceramic Ablator 

(Tran 1996) (SIRCA), Acusil II (Edquist 2009), SLA-561V, and LI-900 because the aerothermal 

environment was so mild that ablation did not take place. As will be shown, the MERs were 

accurate to FIAT prediction within 14% at one standard deviation. 
55

Using these MERs, the M-SAPE tool can now be used to perform trade studies involving entry 

velocity, ballistic coefficient, vehicle geometry, entry flight path angle, etc. and include TPS 

thickness as an output variable. Design turnaround times for a possible Earth entry configuration 

have been reduced from weeks to minutes for a set of proposed flight conditions. For these 

MERs, no margins were added to the calculated TPS thickness requirement. 

3.5.2 MER Model Development 

Applications of MERs 

The MERs presented in this paper are statistical correlations developed to predict FIAT output. 

Each MER has a listed accuracy to FIAT prediction that was found by taking the standard 

deviation of the MER/FIAT value for all the trajectories. These correlations are only valid up to 

the TPS material limits (see Table 7) with respect to the total (convective and radiative) heat 

flux. If the trajectory were to predict a heating greater than allowed by the material capabilities, 

the analysis should be discarded. It is emphasized that the MER TPS predicated thickness is not 

the manufacturing limit of the material’s thickness. 

Finally, as for any statistical analysis, there are some trajectories for which the ratio of 

MER/FIAT prediction can exceed the listed MER accuracy at one SD. It is for this reason that 

full datasets are presented ion Appendix B showing MER/FIAT data with FIAT predicted 

thickness. The most any MER can under predict FIAT is by 19%. M-SAPE utilizes these MERs 

as a “rough approximation” to determine flight trajectories of interest, but always maintains that 

a true high-fidelity analysis would be a requirement as proposed sample return missions move 

forward in development. 

The flight trajectory range that was considered for MERs model development study is shown in 

Table 5. 

Table 5. Flight Trajectory Space Considered for the MERs 

Flight Trajectory Parameter Range of Values Resolution 

Entry Velocity [km/s] 10-16 1 

Entry Flight Path Angle [abs. deg.] 5°-25° 5°

Ballistic Coefficient [kg/m2] 41.95 – 128.74 15.5 (max) 

Total number of trajectories 840 - 

FIAT Modeling Constraints 

FIAT was run for each trajectory with the following constraints common for each developed 

MER: 

• Maximum temperature at bondline was 250°C. 

• Adiabatic back face of the material stack up. 
56

• Surrounding environment temperature of 21.3°C (for radiation from the spacecraft 

surface). 

• 1D planar geometry 

• FIAT v3 

It should be noted that FIAT, being a 1D code, was most applicable for regions on the heat shield 

that did not change shape quickly, such as at the stagnation point, along the flank or any other 

acreage location. For regions that do change shape quickly, like the shoulder, the material 

response code TITAN (Milos and Chen 2008)] would be more appropriate because it includes 

2D effects. In addition, PICA’s heat conduction was orthotropic, which also necessitates the use 

of TITAN along regions of the heat shield that change shape quickly. The range of heating rates, 

heat loads, and surface pressure that were found from the FIAT analysis of the 840 flight 

trajectories are shown in Table 6. The back shell aerothermal environment was estimated to be 

5% of the fore body stagnation point heating and 50% of the surface pressure. 

Table 6. Surface Heating and Pressure Ranges Found by Running FIAT over the 840 Trajectories 

 Forebody Backshell 

Maximum heat flux, 

cold wall [W/cm2

] 130 – 7900 5% of forebody 

Heat Load, cold wall 

[J/cm2

] 3175 – 39943 5% of 

forebody

Maximum pressure, atm 0.005 – 3.288 50% of 

forebody 

Sensitivity studies were conducted by plotting required TPS thickness against a variable of 

interest and looking to see if any correlation existed. Variables of interest included: peak heat 

flux, peak surface pressure, heat load, ballistic coefficient, entry velocity, and entry flight path 

angle. Of these, heat load and entry velocity showed the greatest sensitivity to required TPS 

thickness and were chosen as the variables to use in a power-law correlation. A power-law fit 

(Eq. 13) or quadratic equation (Eq. 14) were used to approximate the TPS thickness and are 

given in the form: 

b

V

HL TH a 











= 2

 (13) 

c

V

HL b

V

HL TH a  +











 +











= 2

2

2

 (14) 
57

Table 7. Summary of MERs Model Development Data 
58

 

Table 8. Summary of Arcjet, Galileo, Pathfinder, and MSL Materials Test Data (Acusil is missing)

Name Max Heat 

Flux (W/cm2) 

Max Heat 

Rate for Short 

Duration Heat 

Pulse 

(W/cm2) 

Short 

Duration (s) 

Max 

Pressure, 

(atm) 

Minimum 

Thickness 

(cm) 

Maximum 

Thickness 

(cm) 

PICA * 1,200 1,200 N/A 1.5 0.5 15 

CP** 30,000 30,000 N/A 7.5 0.5 N/A 

SLA-561V! 100 125 15 0.25 0.5 3 

LI-900 

(uncoated) 

75 100 15 2 0.5 15 

SIRCA 100 125 30 1 0.5 15 

Acusil II 100 125 20 1 0.25 10 

* Based on test data. The material performance may be better than this. 

** Based on Galileo entry. 

!Based on Pathfinder and MSL test data 

MERs have been presented for the vehicle forebody ablators PICA and Carbon Phenolic atop 

ACC, and for the backshell materials SIRCA, Acusil II, SLA-561V, and LI-900. Applications 

include quick estimates of TPS mass during early stages of vehicle design. These MERs have 

been integrated into M-SAPE and will be used with FIAT as an initial estimate of required 

material thickness to speed sizing estimates. When using these MERs, care needs to be taken so 

that sizing environments, such as peak heating, are within the capabilities of the material. 

3.6 Thermal Soak Module 

The survival of the entry vehicle and successful payload recovery are key to the success of 

sample return missions. Mission requirements for outer-space samples could be very stringent. 

For example, MSR requires maintaining temperature control below 200 C. Thus, knowledge of 

payload temperature history and peak payload temperature is critical for mission success. During 

the entry into Earth’s atmosphere, vehicles are subjected to severe thermal and mechanical 

loading due to aerothermal heating followed by the impact of landing. Figure 46 shows a 

schematic of different phases of the entry vehicle. In order to protect vehicles from atmospheric 

heating, a layer of ablative and/or insulative Thermal Protection System (TPS) materials is used 

on the fore and aft body. The relatively lower conductivity of thermal protection materials causes 

a slow absorption of thermal energy into the interior of these vehicles. In addition, low density 

porous foams are used surrounding the payload container to absorb the kinetic energy during the 

impact of landing. Thus, it could take several minutes to hours before the interiors (impact foam 
59

and shell) and payload of the vehicle start to show a rise in temperature. Because the retrieval 

process for the vehicle could take several hours, thermal soak analysis becomes very important 

to predict the survivability of the payload (Agrawal et al. 2012).

In the past, a preliminary thermal analysis of a nominal MSR entry vehicle for a given trajectory, 

including predictions of payload temperature, was performed by researchers at NASA Langley 

(Amundsen et al. 2000). The present work, designed as a thermal soak module for inclusion into 

the M-SAPE tool, has a broader objective. The objective is to build a parametric thermal soak 

model such that analysts could predict the peak payload temperature of a sample return vehicle 

for any given trajectory by entering the vehicle diameter and trajectory parameters, such as peak 

stagnation heat flux and heat load. 

To serve this objective, multiple thermal soak analyses were performed in a simplified 

parametric MSR-EEV-type geometry. To build the model, analyses were performed on 

trajectories with varying heat loads ranging from 3000 J/cm2

 to 38,000 J/cm2 and heat fluxes 

varying from 100 W/cm2 to 1500 W/cm2. In addition, the vehicle model was scaled from 0.8 

meter in diameter to 1.4 meters in diameter thereby including vehicle diameter as a variable in 

the parametric soak model. 
60

Figure 46. Different Phases of Earth Entry Vehicle and Time Span for Thermal Soak Inside the 

Vehicle 

Although the design foundation is based on MSR, the end result is a thermal soak module that 

could be used for other mission architectures, as well. The spatially and time varying aerothermal 

environments on both forebody and aftbody are applied to the present model as boundary 

conditions resulting in accurate representation of thermal energy input. 

The next section describes in detail the components, materials and finite element modeling 

approach for thermal soak model development. 

3.6.1 Finite Element Model Development 

To conduct finite element modeling, the CAD geometry of the vehicle design that was developed 

for the M-SAPE tool was imported into a commercial finite element software package, MarcMentat, developed by MSC Corporation. MSC Marc supports fully transient, non-linear, thermal 

Finite Element Analysis (FEA). It includes an integrated user interface, Mentat, for pre- and 

post-processing. A two dimensional cross-section of the nominal 1.05m diameter geometry that 

was developed for simple parametric MMEEV model is shown in Figure 47. The various subcomponents of the vehicle and their thickness along the centerline vertical position for nominal 

geometry are listed in Table 9. This geometry was scaled to vary the diameter from 0.8 meter to 

1.4 meters. 

The FE model was created based on this simplified MMEEV geometry and assumed to be 2-D 

axisymmetric for several reasons. It takes the solver significantly less time to analyze an 

axisymmetric geometry compared to a three dimensional geometry, which could be very time 

efficient when conducting thermal soak analysis for a wide spectrum of trajectories and 

sensitivity analyses for various parameters. In addition, it is significantly easier to impose the 

thermal boundary conditions like heat-flux distribution from 2-D axisymmetric Computation 

Fluid Dynamics (CFD) models and temperature distribution from thermal response models. 

The model was meshed in such a way that each of the main sub-components was represented as 

a separate element set. These sets include forward and aft-TPS, substructures, impact and body 

foam, wing and lid insulation, impact shell, and payload. The inclusion of various element sets 

will allow for flexibility in implementing different sets of materials properties when needed, 

shape changes after impact etc. The model was meshed using linear quad elements as shown in 

Figure 48. The average element size was about 1.8 mm which led to total 51,200 elements and 

51,700 nodes. 

The payload was assumed to be kept inside a hollow aluminum container. At this time, the focus 

of the thermal soak analysis is to provide the temperature history of the payload container and 

the impact foam so that it can meet the thermal requirements of sample return missions. 

The following materials were considered for the development of the parametric model: 

• Carbon phenolic (CP) and Phenolic Impregnated Carbon Ablator (PICA) for forebody 

TPS. Separate models were developed for both TPS materials. 

• PICA for aft TPS 
61

• Advanced carbon-carbon for substructure as well impact shell 

• Rohacell for all the foams and insulations 

• Aluminum 6061 T6 alloy for payload container 

Figure 47. MMEEV Thermal Soak Model Geometry 

Table 9. Thickness of the Subcomponents along the Vertical Centerline for MMEEV Vehicle 

Component Thickness (m) 

payload 0.1684 

forebody TPS 0.0402 

aft TPS 0.01 

fwd carrier structure 0.0068 

aft carrier structure 0.0055 

primary structure 0.0081 

impact shell 0.002 

impact foam 0.1767 

body foam 0.0275 

lid insulation 0.0583 

wing insulation 0.0759 
62

Figure 48. Finite Element Mesh with Component Sets 

The materials map of the FE model is shown in Figure 49. Temperature-dependent thermal 

properties were considered for each of the materials. The TPS and substructure materials were 

assumed to be transverse isotropic with thickness along the x–axis. The material properties for 

the first three thermal protection materials on the list were obtained from Configuration Managed 

(CM) database developed during the time of the Crew Exploration Vehicle (CEV) TPS advanced 

development program (Feldman et al. 2010 and Britt 2006). The properties for Rohacell foam 

were obtained from communications with the manufacturer and Langley Research Center and are 

listed in Tables 10 and 11. Aluminum 6061 values for the payload container were obtained from 

the MIL handbook. 

The heat flux values derived from CFD were input as surface boundary condition for the model. 

The next section provides a detailed discussion on aerothermal environment for various 

trajectories. Thermal conduction, surface re-radiation to ambient environment, and internal 

cavity re-radiation, and ablation was not modeled at this time. In general, absence of ablation 

should lead to higher peak temperature predictions as more energy will get absorbed by the 

system. The heating due to applied heat flux during re-entry as well as thermal soak during the 

cool down period after re-entry were analyzed. An adaptive time step based on the change in 

temperature was used for the solver to make the computations more efficient. One of the major 

events after re-entry is the touchdown, when the entry vehicle is subjected to severe impact that 
63

causes significant deformation and compression of impact foams. The foam compression causes 

an increase in density, which in turn, 

Figure 49. Materials Map for the Baseline Thermal Analysis 

 Table 10. Specific Heat Capacity of Rohacell-51 

Temperature Specific Heat Capacity (Cp) 

(K) (J/kg-K) 

T < 302.56 2399.04 

320.56 2399.04 

340.33 2687.93 

360.33 2394.85 

380.33 1896.62 

400.33 1565.86 

420.33 1423.51 

440.33 1394.20 

460.33 1193.24 

666.67 1193.24 

Table 11. Thermal Conductivity of Rohacell-51 
64

Temperature Thermal Conductivity 

(K) (W/m-K) 

95.83 0.0127 

134.94 0.0171 

154.72 0.0190 

174.44 0.0208 

228.89 0.0272 

288.33 0.0360 

367.22 0.0567 

479.44 0.0848 

666.67 0.0848 

changes the thermal properties of the foam. The touchdown and impact event would also cause 

changes in thermal pathways. For the present set of analyses, the changes to the model caused by 

impact have been ignored. 

3.6.2 Entry Environments and Boundary Conditions 

The entry environment for the MMEEV program encompasses a fairly large mission trade space 

that includes entry velocities ranging from 10-16 km/s, ballistic coefficients of 42-129 kg/m2

, 

and entry flight path angles ranging from -5° deg to -25° deg. These translate to a wide spectrum 

of trajectories, heat loads, and peak heat fluxes. One of the main objectives for thermal soak 

analyses is to be able to identify the important parameters and to develop correlation coefficients 

so that, for a given heat load and trajectory, one can estimate the peak payload temperature, the 

time to arrive at peak temperature, and the temperature histories of the interior components of the 

vehicle such as the impact foam and payload. For the first set of analyses a nominal trajectory for 

MSR with a very high heat load at the stagnation point was considered. Subsequently a trajectory 

space consisting of over 2700 cases was created from which a representative set of trajectories 

for the thermal soak analyses were selected based on statistical average and standard deviation. 

These trajectories have flight path angles ranging from -25° to -10° degrees and entry velocities 

of 10 to 14 km/s. Figure 50 shows the representative trajectory space, where maximum heat flux 

at the stagnation point of the probe is plotted against the corresponding heat load for each 

trajectory. The high heat load, nominal MSR trajectory is also included in this plot. The high 

heat load trajectory was selected because thermal soak analysis began with this trajectory and the 

geometry was developed for this trajectory. 
65

When the lower heat load trajectory space was generated, the initial, high heat load trajectory 

was retained for comparison as it provided a bounding high value to compare with other 

trajectories and could show the influence of heat load on thermal soak and peak payload 

container temperature. 

The representative trajectories selected for FE thermal soak analyses and development of 

parametric soak models are shown in red. The results from the highest heat load and one of the 

low heat load trajectories are highlighted in Section 3.5. 

Figure 50. Trajectory Space with Selected Trajectories 

The flow field was computed using the CFD code DPLR (Wright et al. 2009) (as described in 

Section 3.4) to generate spatially varying heat-flux values along entry trajectory. The MMEEV 

geometry was represented as a two-dimensional axisymmetric body. The surface of the vehicle 

was made up of 256 body points. 

Appendix C, Figure C1, presents CFD outputs showing the surface of the vehicle and every tenth 

body point plotted along the surface. The body point “zero” corresponds to the nose stagnation 

point of the vehicle where the highest heating occurs. The incoming flow is from left to right. 

The heat flux distribution at peak heating along the vehicle for both the high heat load and low 

heat load trajectories are plotted in Appendix C, Figure C2. For the high heat load trajectory, 

peak heating occurred between 60 and 70 seconds. The highest CFD value obtained was close to 

1000 W/cm2

 at 66 seconds. For the low heat load trajectory, peak heating of 692 W/cm2 occurred 

at 10.5 seconds.

The full time history of the aerothermal environment was obtained by curve fitting the CFD 

solutions. The interpolation function is a power-law fit using free stream velocity and density as 

the independent variables. In this manner a continuous time-history was obtained for each body 

point along the vehicle. In Figure C3, the heat flux distributions of ten discrete CFD solutions 
66

and the interpolated curves are plotted against both body location and time for the low heat load 

trajectory (red circles). These are the solutions which are interpolated in time. The curve fits 

make it possible to estimate the integrated heat load at all 256 locations along the vehicle. The 

integrated heat load for the stagnation point was 38,000 J/cm2

 and 3000 J/cm2 for the high and 

low heat load trajectories respectively. The FE software had in-built subroutine to apply the 

discrete heat flux values on 256 body points on the full outer mold line of the probe. 

The spatially and temporally varying heat flux values for the trajectories were used as a surface 

boundary condition for the FE model. Three dimensional data tables were generated to apply the 

heat flux values as a boundary condition to the model. At the payload interface the heat was reradiated due to hollow container. This would give a more conservative estimate of container 

heating. The next section describes the thermal modeling and results from the analyses from 

selected high and low heat load trajectories. 

3.6.3 Temperature Predictions in Entry Vehicle Design 

Transient thermal analyses were performed with two dimensional axisymmetric elements. The 

initial and ambient temperatures were assumed to be 290o

 K (17oC). The initial temperature is an 

important parameter and simulations will be performed in future to investigate the effects of 

initial temperature. The outer surface was modeled to be re-radiating to a sink temperature of 

290o

 K (17oC) during the entry and cool-down periods. The start time for analysis coincided with 

atmospheric entry. During re-entry the vehicle experiences a very steep temperature gradient on 

the forebody due to high heat flux. Figure C4 in Appendix C shows the temperature contours on 

the vehicle at the end of the heat pulse during the re-entry and right before the touchdown to the 

ground. While the surface temperatures are very high exceeding, 10000C during re-entry and 

exceeding 7000C during touch down on the forebody TPS, the interior of the vehicle (including 

the body foam and payload container) remains at the room temperature. Thermal analysis on the 

identical vehicle configuration (with same TPS thickness) was also performed for the low heat 

load trajectory discussed in previous section. Figure C5 shows the temperature contour plot at 

the end of the heat pulse and right before touchdown for this trajectory. As expected the in-depth 

temperature rise in the forebody TPS is significantly smaller compared to the high heat load 

trajectory, and a large portion of TPS and all of the interior stay at room temperature during the 

re-entry. The touchdown time (when altitude is close to zero) for this trajectory occurs at 2000 s, 

and by that time the whole vehicle shows temperature below 2500C. 

Thermal soak is slow due to low conductivity of the impact and body foam, and the interiors take 

several hours to show a temperature rise. Figure C6 shows the temperature contours after several 

hours for both trajectories. For the high heat load trajectory, the body foam shows a temperature 

rise of 2000C, and the payload container reaches 550C after several hours. In contrast, for the low 

heat load trajectory the body and impact foams stay under 1200C and payload container stays 

below 250C. 

The peak foam temperature comparisons are shown in Figure C7. For the high heat load 

trajectory, the peak foam temperature reaches 3000C, whereas for the low heat load trajectory, 

the foam temperature stays below 1000C through the entire thermal soak period. One point to be 

noted here is that these analyses do not consider foam compression due to ground impact. These 
67

numbers may increase to a higher value if foam compression is taken into account. FE models 

that would include foam compression after impact are proposed as a future MMEEV task that 

will be performed after the parametric studies with the present configuration. 

The present simulations were performed for 45 hours of cool-down period as the payload 

recovery operations could take more than a day. The temperature rise in the payload container 

for all the trajectories is shown in Figure C8 for PICA and CP TPS materials. PICA with low 

thermal conductivity acts as a good insulator, and for all the trajectories, the peak payload 

temperature stays below 300C. For CP TPS, for the low heat load trajectory the payload 

container temperature stays below 250C. This meets the science requirements of keeping the 

payload below 250C. In contrast, the payload temperature for high heat load trajectory eventually 

rises beyond 500C that could cause concerns if the payload recovery is not fast. These analyses 

lead to conclusion that while a thicker forebody TPS may seem very conservative for re-entry 

heating, it may be required to protect the interiors and payload from rising to very high 

temperature during the cool down period. Alternatively, a different architecture such as duallayer TPS approach may be needed. The data suggests that for the same TPS thickness the choice 

of TPS material affects the peak container temperature. In addition, for a given TPS material, the 

heat load magnitude is one of the key contributors for payload temperature rise. Therefore, 

selecting among different trajectories, it may be useful to keep in mind that a trajectory with 

lower heat load value can be more desirable from thermal soak perspective even if it meant a 

higher stagnation point heat flux value. The next section describes some of the analyses that were 

performed to generate a parametric thermal soak model for M-SAPE. 

3.6.4 Parametric Thermal Soak Model Development for M-SAPE 

One of the goals for FE thermal analysis is to identify key factors that affect the peak payload 

and foam temperatures and to develop simple correlations based on these factors that could lead 

to a parametric thermal soak model for M-SAPE. To accomplish this objective, several 

parametric studies have been performed for five selected trajectories by varying heat flux 

magnitude, vehicle diameter TPS materials etc. This section describes those analyses. 

The heat flux was varied by scaling the magnitude throughout the surface for the entire heating 

cycle for the high heat load and the low heat load trajectories. The magnitudes were scaled 

between 0.25 (25%) to 1.5 (150%) times the nominal heat flux values for the trajectory. The 

results from parametric studies for both a high and a low heat load trajectories are shown in 

Figure C9 and C10 respectively. In both the cases, the peak payload container temperature rose 

as the heat flux magnitude increased. For the heat flux range considered for parametric models, a 

linear relationship between the scaled heat flux magnitude and peak payload container 

temperature can be established for any given trajectory as shown in Appendix C, Figures C9b 

and C10b. For the nominal vehicle diameter of 1.05 meter, several simulations were run using 

PICA and CP TPS for various trajectories. 

The next step was to investigate the influence of vehicle diameter. To perform these parametric 

studies, the whole vehicle (FE model) was scaled to a smaller or a larger diameter, while the 

input heat flux magnitude was kept constant to see for a given heat flux magnitude and 

distribution how changes in size affected the peak heating. As expected, for a smaller diameter 

vehicle the peak payload temperature was significantly higher compared to a larger diameter 

vehicle, information that could be useful for determining the vehicle geometry for a given 
68

mission requirement when there is uncertainty in aerothermal environments. After performing 

several simulations for different vehicle diameters, the following correlations were established 

for PICA and CP TPS: 

fe g

TPayload = aξ + bξ + c,whereξ = dq Q D

2

 (14)

q = peak stagnation heat flux W/cm2 

Q = Total heat load at stagnation J/cm2 

D = Vehicle diameter in meters 

 Table 12. For PICA TPS 

Table 13. For CP TPS 

 

	





 

 


 





The goodness of fit for established correlations for peak payload container temperature for both 

PICA and Carbon Phenolic TPS are shown in Figure C12. The results were quite promising for 

this simplified architecture. 
69

3.6.5 Summary and Future work 

A finite element thermal soak model was developed for an MSR-based geometry. Preliminary 

thermal soak analyses were performed for different trajectories to study the temperature rise 

inside the vehicle. CFD analysis was conducted to generate an aerothermal environment for the 

entire vehicle including fore- and aftbodies for several high and low heat load trajectories. The 

temperature histories on the foam and payload were obtained by conducting FE thermal analysis 

for these trajectories and compared with each other. The data show that the magnitude of total 

heat load to the vehicle significantly influences the peak payload temperature, and thus it will be 

useful to include thermal soak requirement when sizing the TPS thickness for the vehicle to 

ensure low payload temperature after the touchdown. 

Parametric studies were performed by varying heat flux magnitude, vehicle diameter, and TPS 

materials. The results suggest that correlations can be established to develop a surrogate thermal 

soak model. There are some limitations to present parametric soak model that will be addressed 

in the future work: 

1. The TPS thickness in this model was not varied for various trajectories and kept out of 

the parametric model. Analyses are in progress for geometries with different TPS 

thicknesses to investigate the influence of TPS thickness in the prediction of peak 

payload temperature. In future, the model will include the TPS thickness variable. 

2. Ablation, which may have led to more conservative estimations of peak payload 

temperature as more energy would be soaked in the system, was not considered in the 

present analyses. In the future, for high-fidelity thermal soak models, ablation, when 

necessary and prominent, will be accounted for in the model by coupling the temperature 

maps from the thermal response model, FIAT (Chen and Milos 1999) to the finite 

element model. This approach will be investigated for final point designs for missions 

like MSR and other sample return missions. 

3. Post-impact crushed vehicle shape was not considered in the model. The change of foam 

density as well as deformation will affect the thermal soak. In the future studies, the 

analysis will be performed in two steps: 1) re-entry and time elapsed before impact and 2) 

cooling after impact. The geometric changes due to impact will be incorporated thorough 

significant mesh deformation, while the temperatures will be mapped from one model to 

the other model, keeping the same node and element numbers. The temperature 

distribution from the last time step of first analysis will be imposed as initial condition for 

the second analysis, and thermal soak will carry on. 

4.0 SAMPLE RESULTS 

The results for two sample test cases are presented in this section. Previous EEV baseline models 

and results (Dillman and Corliss 2008) were used to verify the integrated analysis tool. Figure 13 

shows the nominal MSR model, and Table 14 shows the list of input parameters for the baseline 

model. The payload mass includes mass of sample return material, as well as the orbiting 

canister mass. The results shown in Table 15 compare well with results of Dillman and Corliss 

2008. 
70

Figure 51 shows the results for various entry velocities and flight path angles for three diameters. 

The EEV mass ranges from 34-62 kg. The entry load is a strong function of flight path angles 

and can vary from 75 to 230 Earth g’s. The maximum heat rate is primarily a function of the 

entry velocity, and it varies between 1000-3000 W/cm

2

. 

The second case is the Galahad model, which is an asteroid sample return mission proposal 

response to the NASA New Frontiers solicitation. The mission goal is to return a sample from 

the binary C-asteroid 1996 FG3 and to make extensive orbital measurements. The plan is to 

return 60 g of samples to Earth. Figure 52 shows the Galahad EEV concept. Table 16 shows 

sample results for the Galahad EEV. The results are very similar: the total mass is within 4% of 

Maddock et al. 2010.

Table 14. Input Parameters for Case 1 



12.5 

!"4000 

#$"	$ .25 

%&$$"	$.07 

'!(30 

Table 15. M-SAPE Results Compared with Dillman and Corliss 2008 

 	
	
 





'


)!*"

 &!+",

 !-

.!*"


, !&!71

Figure 51. Sample M-SAPE Trade Space for MSR Model 

Figure 52. Galahad EEV Concept Maddock et al. 2010 
72

Table 16. M-SAPE Results Compared with Maddock et al. 2010 

Parameters 

circa 

2010 M-SAPE 

Total mass, kg 32.1 31.9 

Maximum entry load, g's 33.9 34.7 

Total peak heat rate, W/cm2 400 441 

Total heat load, kJ/cm2 11.2 12.0 

PICA thickness, cm 2.3 2.21 

Time of flight, sec 595 673 

Impact velocity, m/s 31 26.7 

Impact load, g's 470 456 

Impact stroke, cm 3.4 2.4 

5.0 CONCLUSION 

M-SAPE is an integrated system analysis tool for planetary entry. The current version of MSAPE can perform system analysis of Earth entry vehicle for sample return missions. The system 

includes geometry, mass sizing, impact analysis, structural analysis, flight mechanics, TPS, and a 

web portal for user access. 

M-SAPE enables rapid and consistent analyses for single runs, what if scenarios (e.g., change in 

payload mass), and technology evaluation (e.g., TPS concepts) in minutes verses weeks for 

traditional analyses. It also can provide system sensitivity analysis and system trade space 

analysis in hours verses months for traditional analyses. The system is design with uncertainty 

propagation and Monte Carlo analysis in mind. However, these capabilities have not been fully 

implemented yet. 

The system was used to generate a large database of 80,000 unique designs. Users can either run 

a single design or explore the existing designs through the web portal, which is located within 

Langley’s firewall. 

M-SAPE is still in development. A prototype system was completed in fiscal year 2012 and 

version 1 was completed in fiscal year 2013. The effort in fiscal year 2014 will be focused on 

adding Venus, Saturn, and Uranus. In addition, the plan is to extend M-SAPE to include 

advanced TPS concepts and various vehicle shapes.
73

6.0 FUTURE DEVELOPMENT AND OPPORTUNITIES FOR COLLABORATION 

The Multi-Mission System Analysis for Planetary Entry (M-SAPE) tool has been developed to 

facilitate the design and analysis of a family of Earth Entry Vehicles (EEVs). M-SAPE is a 

system analysis tool developed for use by EEV designers to provide enhanced information early 

in the design process to take advantage of design freedom existing at the initial stages of a 

vehicle design process. As a result, it is believed that use of M-SAPE will improve the 

probability of mission success through improved decision making and more effective use of 

resources early in the design process. M-SAPE provides the user the capability to perform rapid 

low-fidelity analysis, trades, and design optimization. M-SAPE also provides the potential for 

technology developers to define requirements for new technologies as well as determine their 

overall system benefit facilitating technology infusion. 

The NASA In-Space Propulsion Technology (ISPT) Program has funded the development of the 

M-SAPE tool and the Multi-Mission Earth Entry Vehicle (MMEEV) design trade space for the 

past several years with the intent to release for use. NASA’s software release policy will be 

employed to define authorized users. While M-SAPE is being provided to users at no cost it is 

recommended that users establish a collaborative arrangement with NASA for effective use the 

tool. Collaborative arrangements could include focused design and trade-space analysis, 

technology requirements and benefits development, updates or revisions to existing models, or 

the development of completely new models and capabilities. Currently M-SAPE is designed to 

address EEV design. However plans include evolving M-SAPE to include designs of probes to 

other planets as well as inclusion of new and emerging highly-promising technologies in support 

of technology development and infusion. In this context M-SAPE is being viewed more as a 

collaborative environment for EEVs and future planetary probe designs as well as supporting 

technology development and infusion aspects. Potential costs for collaboration would include 

only the costs for specific applications with the cost of prior development provided by NASA’s 

investment in M-SAPE. Potential cost-sharing collaborations would also be possible with 

appropriate support from NASA programs if the application had benefits to both the users and 

NASA. 

For collaborative use, please contact: 

1) Jamshid Samareh at LaRC (757) 864-5776, jamshid.a.samareh@nasa.gov 

2) Lou Glaab at LaRC (757)864-1159, louis.j.glaab@nasa.gov 

3) Parul Agrawal at ARC (650)604-3764, parul.agrawal-1@nasa.gov 

 
74

7.0 REFERENCES 

Agrawal, P., Prabhu, D.K., and Chen Y-K, Thermal Analysis of Small Re-entry Probes, AIAA paper 2012-

0219, Jan. 2012. 

Agrawal, P., Sepka, S. A., Aliaga, J.F., Venkatapathy, E., and Samareh, J.A. 2012, Thermal Soak Analysis 

of Earth Entry Vehicles, proceedings, 43rd AIAA Thermophysics Conference, New Orleans, 

Louisiana, June 2012, AIAA 2012-3010. 

Aliaga-Caro, A. Zarchi, K., and Sepka, S. A., Entry Vehicle Technology: Aerothermal Analysis of 

Trajectory Environments II, 10th International Planetary Probe Workshop, 2013. 

Amundsen, R.M., Dec, J.A., Mitcheltree, R.A., Lindell, M.C., and Dillman R.A., Preliminary Analysis of a 

Mars Sample Return Earth Entry Vehicle, AIAA paper 2000-2584, June 2000. 

Bauer, G.L., Cornick, D.E., and Stevenson, R., Capabilities and Applications of the Program to Optimize 

Simulated Trajectories (POST), NASA CR-2770, February 1977. 

Britt S., Mechanical and Thermal Testing of ENKA Rayon Based MX4926 Carbon Cloth Phenolic, 

Southern Research Institute Report. SRI-ENG-06-37-11610.01, August 2006. 

Cheatwood, F. McNeil, User’s Manual for the Langley Aerothermodynamic Upwind Relaxation Algorithm 

(LAURA), NASA TM-4674, 1996. 

Chen, y.-K., and Milos, F.S., Fully Implicit Ablation and Thermal Analysis program (FIAT), Journal of 

Spacecraft and Rockets, Vol. 36, No. 3, pp 475-483, May-June 1999. 

Clements, H.R. and Ward, G.T., Fabrication of Ablative Liners for Large Solid Booster Nozzles, Journal of 

Spacecraft and Rockets, Vol. 3, No. 4, April, 1966.

Curry, D. M., “An Analysis of a Charring Ablation Thermal Protection System, NASA TN D-3150, 

November 1, 1965. 

Dillman, R., Corliss, J., Overview of the Mars Sample Return Earth Entry Vehicle, Sixth International 

Planetary Probe Workshop, Atlanta, Georgia, USA, June 2008. 

Edquist, Karl T., Dyakonov, Artem A., Wright, Michael J., and Tang, Chun Y., Aerothermodynamic 

Design of the Mars Science Laboratory Backshell and Parachute Cone, AIAA Paper 2009-4078, 

presented at 41st AIAA Thermophysics Conference, San Antonio, Texas, USA, June 22-25, 2009. 

Fasanella, E. L., Jones, Y., Knight, N. F., and Kellas, S., "Low Velocity Earth-Penetration Test and 

Analysis," AIAA-2001-1388. 

Feldman J. et al., Material Properties of PICA, NASA TM-2010-216399, June 2010. 

Hollis, B.R., Blunt-Body Entry Vehicle Aerothermodynamics: Transition and Turbulent Heating, Journal of 

Spacecraft and Rockets, Vol.49, No. 3, May-June 2012. 

Hui, T., Johnson, C., Rasky, D., Hui, F., Hsu, M., Chen, Y-K., Phenolic Impregnated Carbon Ablators 

(PICA) For Discovery Class Mission, NASA Tech Briefs, AIAA paper 1996-1911, presented at 

31st AIAA Thermophysics Conference, New Orleans, LA, June, 1996. 

Justus, C.G. and Johnson, D.L., The NASA/MSFC Global Reference Atmospheric Model: 1999 Version 

(GRAM-99), NASA TM-1999-209630. 

Justus, C.G. and Leslie, F.W., Earth Global Reference Atmospheric Model (GRAM99): Short Course, 

Denver, CO., August 7, 2007. 

Leslie, F.W. and Justus, C.G., The NASA Marshall Space Flight Center Earth Global Reference 

Atmospheric Model, 2010 Version. June, 2011, NASA TM-2011-216467. 

MacCormack, R.W. and Candler, G.V., The solution of the Navier-Stokes Equations Using Gauss-Seidel 

Line Relaxation, Computers and Fluids, Vol.17, pp. 135-150, Nov. 1, 1989. 

Maddock, R. W., et al., Multi Mission Earth Entry Vehicle Design Trade Space and Concept Development 

Status, proceedings, 7th International Planetary Probe Workshop, Barcelona, Spain, June 2010. 

Maddock, R. W., et al., Sample Return Challenges Multi-Mission Earth Entry Vehicle Design Trade Space 

and Concept Development Strategy, proceedings, 6th International Planetary Probe Workshop, 

Atlanta, Georgia, USA, June 2008. 

Maddock, R.W., Multi-Mission Earth Entry Vehicle Design Trade Space and Concept Status (Version 2.0), 

8th International Planetary Probe Workshop, Portsmouth, VA, USA, 2011. 

Marko, Wayne J., Static Aerodynamic Characteristics of the Three Blunted Sixty-Degree Half-Angle Cones 

at Mach Numbers from 0.6 to 1.3, NASA JPL-TR-32-1298. July 1968. 

Mattingly, R., and May, L., Mars Sample Returns as a Campaign, proceedings, 2011 IEEE Aerospace 

Conference, Big Sky, Montana, USA, 2011. 
75

Mercer, R.J., Physical Constants for Satellite Calculations, Aerospace Corporation Report, TOR469(5110-02)-2, El Segundo, CA, January 1965. 

Milos, F.S., and Chen, Y.-K., Two-Dimensional Ablation, Thermal Response, and Sizing Program for 

Pyrolyzing Ablators, AIAA paper 2008-1223, 46th AIAA Aerospace Sciences Meeting and 

Exhibit, January 7 "“ 10, 2008, Reno, Nevada, USA. 

Mitcheltree, R. A., Fremaux, C. M., and Yates, L. A., Subsonic Static and Dynamic Aerodynamics of Blunt 

Entry Vehicles, AIAA 99-1020. 

Mitcheltree, R.A., Kellas, S., Dorsey, J.T., Desai, P.N., and Martin, C.J., A Passive earth-Entry Capsule for 

Mars Sample Return, AIAA paper 1998-2851. 

Mitcheltree, R.A., Wilmoth, R.G., Cheatwood, F.M., Brauckmann, G.J., Greene, F.A., Aerodynamics of 

Stardust Sample Return Capsule, AIAA Journal of Spacecraft and Rockets, Vol. 36, No. 3, May "“ 

June 1999. 

Moyer, C. B., and Rindal, R. A., An Analysis of the Coupled Chemically Reacting Boundary Layer and 

Charring Ablator Part II. Finite Difference Solution for the In-Depth Response of Charring 

Materials Considering Surface Chemical and Energy Balances, NASA CR-1061, 1968. 

Perino, S.V., Bayandor, J., and Siddens, A., A Comprehensive Structural Dynamic Analysis Approach for 

Multi Mission Earth Entry Vehicle (MMEEV) Development, June 2013, NASA/CR-2013-218003. 

Perino, S.V., Bayandor, J., Samareh, J.A., ad Armand, S.C., Numerical Impact Analysis of a Novel 

Spherical Impact Absorber for Mars Sample Return, in preparation for publication in International 

Journal of Impact engineering (2013). 

Samareh J.A., Maddock R. W. and Winski R.G., An Integrated Tool for System Analysis of Sample Return 

Vehicle, proceedings, 2012 IEEE Aerospace Conference, Big Sky, Montana, March 3-10, 2012. 

Samareh, A., A Multidisciplinary Tool for Systems Analysis of Planetary Entry, Descent, and Landing 

(SAPE), NASA-TM--2009-215950. 

Squire, T., Milos, F., Agrawal, P., Analytical Predictions of Thermal Stress in the Stardust PICA 

Heatshield Under Reentry Flight Conditions, National Space and Missile Materials Symposium, 

28 Jun. - 1 Jul. 2009, Scottsdale, AZ, USA. 

Steinberg, S., Experimental Pitch Damping Derivative for Candidate Viking Entry Configurations at Mach 

Numbers from 0.6 thru 3.0, June 1970, Martin Marietta Corporation. 

Striepe, S. A., Powell, R. W., Desai, P. N., Queen, E. M., Brauer, G. L., Cornick, D. E., Olson, D. W., 

Peterson, F. M., Stevenson, R., Engel, M. C., Marsh, S.M., and Gromko, A.M., Program To 

Optimize Simulated Trajectories (POST II): Utilization Manual, Vol. 2, 2004. 

Sutton, K., Graves, R.A., A General Stagnation-Point Convective Heating Equation for Arbitrary Gas 

Mixtures, NASA TR R-376, November 1972. 

Tauber, M., Sutton, K., Stagnation-Point Radiative Heating Relations for Earth and Mars Entries, AIAA 

Journal of Spacecraft, Vol. 28, No. 1, pp. 40-42, Jan-Feb 1991. 

Tran, H., Johnson, C., Rasky, D., and Hui, F., Silicone Impregnated Reusable Ceramic Ablators for Mars 

Follow-on Missions, AIAA Paper 96-1819, June 1996. 

U.S. Standard Atmosphere, 1976, National Oceanic and Atmospheric Administration, National Aeronautics 

and Space Administration, United States Air Force, NOAA-S/T 76-1562, Oct. 1976, Washington, 

D.C. 

Walker, B., Weaver, R.W., Static Aerodynamics Characteristics of Blunted Cones in the Mach-Number 

Range from 2.2 to 9.5, December, 1967, NASA TR 32-1213. 

Wright, M.W., White, T., and Mangini, N., Data Parallel Line Relaxation (DPLR) Code User Manual, 

Acadia Version 4.01.1, NASA TM-2009-215388, October 2009 

Yee, H.C., A Class of High-Resolution Explicit and Implicit Shock Capturing Methods, NASA TM-101088, 

Feb. 1989. 


APPENDIX A 

AERODYNAMICS SUPPLEMENTAL DATA 

Aerodynamic Data for Rarefied and Hypersonic Flow Conditions 

-0.3

-0.2

-0.1

0

0.1

0.2

0 50 100 150 200

 cm (XCG=0)

Cm

 alpha_t

-0.2

0

0.2

0.4

0.6

0.8

1

0 50 100 150 200

Rarefied

 cn

CN

 alpha_t

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

0 50 100 150 200

 ca

CA

 alpha_t

Figure A1 - MMEEV Aero Database for Rarefied Flow Conditions. Moment Reference Point is the 

Vehicle Virtual Nose Apex. 


-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

0 50 100 150 200

 ca

 ca

 alpha_t

-0.1

0

0.1

0.2

0.3

0.4

0.5

0 50 100 150 200

V-021-M24

 cn

CN

 alpha_t

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0 50 100 150 200

 cm

Cm

 alpha_t

Figure A2 - MMEEV Aero Database for M=24. Moment Reference Point is the Vehicle Virtual 

Nose Apex. 


-0.14

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0 5 10 15 20 25 30 35

 Cm

Cm

 alpha_t

0

0.1

0.2

0.3

0.4

0.5

0 5 10 15 20 25 30 35

V-021-M9.8

 CN

CN

 alpha_t

1

1.1

1.2

1.3

1.4

1.5

0 5 10 15 20 25 30 35

 CA

CA

 alpha_t

Figure A3 - MMEEV Aero Database for M=9.8. Moment Reference Point is the Vehicle Virtual 

Nose Apex. 


1

1.1

1.2

1.3

1.4

1.5

0 5 10 15 20 25 30 35

 ca

CA

 alpha_t

-0.05

0

0.05

0.1

0.15

0.2

0 5 10 15 20 25 30 35

V-021-M3.98

 cn

CN

 alpha_t

-0.14

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

0 5 10 15 20 25 30 35

 cm

Cm

 alpha_t

Figure A4 - MMEEV Aero Database for M=3.98. Moment Reference Point is the Vehicle Virtual 

Nose Apex. 


-0.05

0

0.05

0.1

0.15

0.2

0 5 10 15 20 25 30 35

V-021-M3.01

 cn

CN

 alpha_t

1.1

1.2

1.3

1.4

1.5

1.6

0 5 10 15 20 25 30 35

 ca

CA

 alpha_t

-0.14

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0 5 10 15 20 25 30 35

 cm

Cm

 alpha_t

Figure A5 - MMEEV Aero Database for M=1.3. Moment Reference Point is the Vehicle Virtual 

Nose Apex. 


-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0 5 10 15 20 25 30

V-021-M1p15

 cn

CN

 alpha_t

1.1

1.15

1.2

1.25

1.3

1.35

0 5 10 15 20 25 30

 ca

CA

 alpha_t

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0 5 10 15 20 25 30

 cm

Cm

 alpha_t

Figure A6 - MMEEV Aero Database for M=1.15. Moment Reference Point is the Vehicle Virtual 

Nose Apex. 


-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0 5 10 15 20 25 30

V-021-M0p85

 cn

CN

 alpha_t

0.75

0.8

0.85

0.9

0.95

1

1.05

0 5 10 15 20 25 30

 ca

CA

 alpha_t

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0 5 10 15 20 25 30

 cm

Cm

 alpha_t

Figure A7 - MMEEV Aero Database for M=0.85. Moment Reference Point is the Vehicle Virtual 

Nose Apex. 
	

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0 5 10 15 20 25 30

V-021-M0p75

 cn

CN

 alpha_t

0.75

0.8

0.85

0.9

0.95

1

0 5 10 15 20 25 30

 ca

CA

 alpha_t

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0 5 10 15 20 25 30

 cm

Cm

 alpha_t

Figure A8 - MMEEV Aero Database for M=0.75. Moment Reference Point is the Vehicle Virtual 

Nose Apex. 



-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0 5 10 15 20 25 30

V-021-M0p6

 cn

CN

 alpha_t

0.7

0.75

0.8

0.85

0.9

0.95

0 5 10 15 20 25 30

 ca

CA

 alpha_t

-0.08

-0.06

-0.04

-0.02

0

0.02

0 5 10 15 20 25 30

 cm

Cm

 alpha_t

Figure A9 - MMEEV Aero Database for M=0.6. Moment Reference Point is the Vehicle Virtual 

Nose Apex. 


0.6

0.62

0.64

0.66

0.68

0.7

0.72

0.74

0 5 10 15 20 25 30

CA-M0.5

CA

 alpha_t

-0.1

0

0.1

0.2

0.3

0.4

0 5 10 15 20 25 30

CN-M0.5

CN

 alpha_t

-0.15

-0.1

-0.05

0

0.05

0 5 10 15 20 25 30

Cm-M0.5

Cm

 alpha_t

Figure A10 - MMEEV Aero Database for M=0.5. Moment Reference Point is the Vehicle Virtual 

Nose Apex.
86

APPENDIX B: MERS DEVELOPMENTAL DATA 

PICA-Only 

Figure B1. PICA-only MER GoF vs. FIAT-predicted PICA Thickness 

 
87 

Carbon Phenolic Atop Advanced Carbon-Carbon 6 

Table B1. Material Stack-up for Carbon Phenolic Atop Advanced Carbon-Carbon 6 

Material Thickness, cm 

Carbon Phenolic variable 

HT-424 (adhesive) 0.0381 

Advanced Carbon-Carbon (ACC) version 6 0.250

Figure B2. Carbon Phenolic over ACC6 MER, GoF (need your latest result in here) 

 
88

SIRCA-Only 

Figure B3. SIRCA MER GoF 
89 

Acusil II MER 

Figure B4. Acusil II MER GoF 
90

SLA-561V-Only 

Figure B5. SLA-561V MER GoF 
91 

LI-900 

Figure B6. LI-900 MER GoF 
92

APPENDIX C 

THERMAL SOAK ANALYSES DATA 

Entry Environments and Boundary Conditions 

Figure C1 - The 256 Surface Body Points for Full Body Aerothermal Analysis 

Figure C2 - Heat Flux Distribution Along Vehicle at Peak Heating 
93

Figure C3 - DPLR Solutions with the Curve Fits Superimposed for the Low Heat Load Trajectory 

Figure C4 – Temperature Contours for the High Heat Load Trajectory 
94

Figure C5 - Temperature Contours for the Low Heat Load Trajectory 

Figure C6 - Temperature Contours in the Probe After Touchdown. a and b Correspond to the High 

Heat Load Trajectory; c and d Correspond to the Low Heat Load Trajectory 
95

Figure C7 - Peak Foam Temperature for the Two Trajectories 

Figure C8 - Payload Temperature History and Peak Payload Temperature for Various Trajectories 
96

 a. b 

 Figure C9 - Parametric Studies for Heat Flux Magnitude Variation for the High Heat Load 

Trajectory 

 a. b. 

Figure C10 - Parametric Studies for Heat Flux Magnitude Variation for the Low Heat Load 

Trajectory 

Figure C11 - Peak Payload Temperature Variations with Heat-load for a Given Vehicle Diameter 
97

Figure C12 - The GoF for Established Correlations for Peak Payload Temperature with Vehicle 

Diameter, Peak Stagnation Heat Flux and Heat-load 
98

APPENDIX D 

M-SAPE USER GUIDE 
Project Lead: Dr. Jamshid A. Sam

Database Design and User Inter

Development: Charles A. Lile

Mar 19, 2014

M

-SAPE
 New users must sign in before accessing the system
 Once you are logged in, you will be directed to the main query page

 You can supplement pre-existing database data with your own data; your 

be visible to other regular users

 To upload your data, select “Choose File”
 Then select the file that you would like to upload

 Only CSV formatted files can be uploaded 
 Assuming the file uploads correctly, you will be given an option of which datasets matc

 Select the radio button next to the dataset name to which you want to append your da Press “Append” Make sure that you wait for the process to finish; DO NOT CLOSE OR REFRESH THE BRO
 To query a data set, select the dataset’s name in Section (1) of the M

then click “Submit”
 In Section (2), you may enter min/max values for numeric variables and s

nominal variables

 Each value that you specify will have to be true in the query result (Logica Select the “Query” button in Section (3) to start the query
 Wait for the query results to return (it may take some time)

 Only the first 200 results will be displayed on the screen You can export all results to a CSV file by selecting “Export Results t
 To Run a KD-Tree / RBF contour plot, select the “Create a KD-Tree / 

Using this M-SAPE Type” hyperlink from the Main Query Page
 When the KD-Tree Contour Plot Page loads, the M-SAPE type which you had previously 

in Section (1)

 You can change the M-SAPE type in Section (1) by selecting it in the combo box and th In Section (2), you must select a nominal value for EACH combo box before proceeding 

Nominal Variables (be patient, it may take some time before the page reloads)
 When the page reloads, you can then select variables for the X and Y 

 These variables must be different from eachother Hit “Submit Axes” to continue
 Next, you can enter values for independent, numeric variables in Sec

 In Section (5), check all variables that you would like to see plotted Then click “Plot” in Section (6)
 A new window will then pop up (you may have to enable the pop up in so

Opera)

 The new window will continuously refresh until the image has been create Do not close the pop up window until the image loads or you get an error 
 The image should look something like the one below

 You can plot multiple images and leave them open in their respective pop up windows Do not try to run two plots at once Once you close the pop up window, you will not be able to access it again unless you r

again
 After proceeding past Section (2), if you decide to change a nominal varia

MUST THEN SELECT “Submit Nominal Variables” AGAIN FOR THE CHANGE 

 Click the “Return to MSAPE Query Page” hyperlink at the top to get back t
 To Run a Neural Network contour plot, select the “Create a Neural N

hyperlink from the Main Query Page
 Select the dataset which you want to plot from the dropdown menu

 On the right is a list of dataset’s with associated neural networks which h

for plotting by an Administrator

 Contact an administrator to enable these neural networks for plotting
 If your selected dataset has nominal independent variables, select a value 

appropriate dropdown menu

 If your selected dataset has shading for nominal variables, the nominal sh

with all variable shades selected

 Uncheck the nominal shading checkboxes if you do not want to plot nomi Select X and Y Axes variables for your contour plot and click “Submit”
 Next input specific values for any numeric, independent variables wh

the X or Y axes

 Check each dependent, numeric variable which you would like to see  Finally, hit “Plot”
 Your plot should appear shortly afterwards in a pop-up window

 If you get the error message: “An error occurred while generating you

deselecting some numeric, dependent variable checkboxes and then again (errors may be due to a faulty neural network specific to one odependent variable(s))
 To run M-SAPE with user input, select the “Run a New M-SAPE Iterat

the Main Query Page
 On the M-SAPE Call page, enter a descriptive name for your run in Section (1) (it 

unique)

 In Section (2), enter input parameters for your entry vehicle design (default valu

page load

 Click the “Run M-SAPE” button in Section (3) to start the run
 A pop up page will appear (once again, you may need to enable pop ups d

browser)

 Depending on your design and the number of iterations you selected in S

some time for the design to close (i.e. four minutes for an 18 iteration ru
 If the design closed successfully, you will see a the pop up page eve

the one below

 Otherwise, you should see an error message; contact a M-SAPE adm

assistance
 Unlike the Contour Plotting page, your previous M-SAPE runs will be 

 Previous M-SAPE runs can be accessed from the hyperlinks in the ta Select the “Return to MSAPE Query Page” at the top to navigate to the 
 You can change your M-SAPE password from the Main Query Page b

“Change Password” link at the top 

 To exit the system, click the “Sign Out” button in the top right of mo
REPORT DOCUMENTATION PAGE Form Approved

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Multi-Mission System Analysis for Planetary Entry (M-SAPE) Version 1 

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Samareh, Jamshid A.; Glaab, Louis, J.; Winski, Richard G.; Maddock, 

Robert W.; Emmett, Anjie L.; Munk, Michelle, M.; Agrawal, Parul; Sepka, 

Steven; Aliaga, Jose; Zarchi, Kerry; Mangini, Nancy; Perino, Scott; 

Bayandor, Javid; Liles, Charles A.

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This report describes an integrated system for Multi-mission System Analysis for Planetary Entry (M-SAPE). The system in its 

current form is capable of performing system analysis and design for an Earth entry vehicle suitable for sample return 

missions. The system includes geometry, mass sizing, impact analysis, structural analysis, flight mechanics, TPS, and a web 

portal for user access. The report includes details of M-SAPE modules and provides sample results. Current M-SAPE vehicle 

design concept is based on Mars sample return (MSR) Earth entry vehicle design, which is driven by minimizing risk 

associated with sample containment (no parachute and passive aerodynamic stability). By M-SAPE exploiting a common 

design concept, any sample return mission, particularly MSR, will benefit from significant risk and development cost 

reductions. The design provides a platform by which technologies and design elements can be evaluated rapidly prior to any 

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