This research was in support of exploring the need for more flexible "center of gravity (CG) specifications than those currently established by NASA for the Multi-Purpose Logistics Module (MPLM). The MPLM is the cargo carrier for International Space Station (ISS) missions. The MPLM provides locations for 16 standard racks, as shown in Figure 1; not all positions need to be filled in any given flight. The MPLM coordinate system (X(sub M), Y(sub M), Z(sub M)) is illustrated as well. For this project, the primary missions of interest were those which supply the ISS and remove excess materials on the return flights. These flights use a predominate number of "Resupply Stowage Racks" (RSR) and "Resupply Stowage Platforms" (RSP). In these two types of racks, various smaller items are stowed. Hence, these racks will exhibit a considerable range of mass values as well as a range as to where their individual CG are located

Tester, John T.

NASA Technical Reports Server

   
 
 
 
 
 
2004 
 
NASA FACULTY FELLOWSHIP PROGRAM 
 
 
 
 
MARSHALL SPACE FLIGHT CENTER 
 
THE UNIVERSITY OF ALABAMA  
THE UNIVERSITY OF ALABAMA IN HUNTSVILLE 
ALABAMA A&M UNIVERSITY 
 
 
 
 
 
RACK DISTRIBUTION EFFECTS ON  
MPLM CENTER OF MASS 
 
 
 
 
 
 
Prepared By:     John T. Tester 
 
Academic Rank:    Associate Professor 
 
Institution and Department:   Northern Arizona University  
       Department Mechanical Engineering 
         
NASA/MSFC Directorate:   Flight Projects 
 
MSFC Colleague:    Jon Holladay 
 
 
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https://ntrs.nasa.gov/search.jsp?R=20050215330 2019-08-29T19:46:37+00:00Z
   
Introduction 
 
This research was in support of exploring the need for more flexible "center of gravity” (CG) 
specifications than those currently established by NASA for the Multi-Purpose Logistics Module 
(MPLM).  The MPLM is the cargo carrier for International Space Station (ISS) missions.  The 
MPLM provides locations for 16 standard racks, as shown in Figure 1; not all positions need to 
be filled in any given flight.  The MPLM coordinate system (XM, YM, ZM) is illustrated as well.  
For this project, the primary missions of interest were those which supply the ISS and remove 
excess materials on the return flights.  These flights use a predominate number of “Resupply 
Stowage Racks” (RSR) and “Resupply Stowage Platforms” (RSP).  In these two types of racks, 
various smaller items are stowed.  Hence, these racks will exhibit a considerable range of mass 
values as well as a range as to where their individual CG are located.   
 
XM 
       
YM 
ZM 
PORT 
Figure 1:  MPLM (left) and schematic of racks arrangement (right). 
 
There are two aspects to this packing problem:  Designing the MPLM rack organization such 
that the CGM will exist within a specified allowable space (Figure 2) and ensuring that calculated 
CGM and mass will be sufficiently accurate to match the measured corresponding values once 
the MPLM is packed.  For this report, the first scenario is termed “packing design;” the second 
comparison process is termed “packing validation.”  For the purposes of this summer study, any 
RSR or RSP can go into any of the 16 MPLM positions for the ISS support missions.   
 
A Manifest Flexibility Task (MFT) study, conducted by the Johnson Space Center (JSC), 
evaluated a limited number of alternative MPLM payload scenarios for their resulting loads on 
the individual rack fittings.[3]  This study was used as a baseline for establishing numerous CGM 
alternatives based upon variations in individual rack CG.  In past flights, the RSR and RSP 
exhibited a wide range of masses and a relatively wide range of CG.  The MFT used a series of 
analysis categories:  Liftoff, Heavy Landing and Light Landing.  The Liftoff and Light Landing 
categories were conducted with the rack masses at (essentially) the same masses as those 
recorded in previous STS/MPLM flights.  The Heavy Landing category used heavier masses for 
the racks, about 80 – 100 lbm heavier than that used in the true flight record.  Within each of 
 
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these mass categories, the racks had further alternative settings per computer simulation.  These 
options were established as low and high values of the rack CG within ranges based upon past 
flight histories; for brevity, a summary of these cases is given in Table 1, and Figure 3 illustrates 
these CG locations.  As historical data was only available for the landing scenarios, the Light 
Landing and Heavy Landing situations were the only scenarios explored this summer. 
Values in Inches
-15
-10
-5
0
5
10
15
-15 -10 -5 0 5 10 15
Y_M
Z_
M
887.76
890.925
897.285
903.635
909.985
900.46
906.81
894.11
 
Table 1.  Rack CG alternatives for each 
MFT loads analysis. 
CASE
Æ A B C D 
Nominal
 (Nom) 
Xrack
NOM 
(RSP) 
LOW 
(RSR) 
NOM 
(RSP) 
 LOW 
(RSR) 
NOM 
(RSP) 
HI 
(RSR) 
NOM 
(RSP) 
HI 
(RSR) 
NOM 
Yrack
LOW 
(RSP) 
NOM 
(RSR) 
LOW 
(RSP) 
NOM 
(RSR) 
HI 
(RSP) 
LOW 
(RSR) 
HI 
(RSP) 
LOW 
(RSR) 
NOM 
Zrack LOW HI HI LOW NOM 
X_M 
        Figure 2:  Allowable envelope of CGM. [1] 
 
RSP C B 
∑∑
==
=
N
i
i
N
i
iiXM mmxCG
11
_
 
Figure 3:  Alternative sub-cases for RSR and RSP local CG. 
Problem Definition 
The CM of any object is given at right, , where N is the number of 
racks used in the manifest, xi is the x-axis position of rack i  in the 
MPLM frame, mi is the mass of rack i,  and  i = 1,2,…N.  
Equivalent equations are apparent for Y and Z of the CGM .  For the problem at hand, MSFC 
desired to have many different, discrete alternatives for the CG of the racks examined for trends 
in overall CG location.  Thus, a program was developed in MatLab in order to enumerate 
combinations of rack locations for a given manifest, based upon Table 1.  It is noted that with n 
rack positions, n racks to install, with k of type RSP (with only RSR being the other type 
allowed), the number of alternate examinations would be given in the equation below.  The 
number of alternatives for return flight 6A, for example, had n = 16, m = 8, and k = 4.  Such a  
A
D 
C
A
D 
B
RSR 
 
XLIV-3
   
⎟⎟⎠
⎞
⎜⎜⎝
⎛
−⎟⎟⎠
⎞
⎜⎜⎝
⎛
−⎟⎠
⎞⎜⎝
⎛=
)!(!
!
)!(!
!33
mkm
k
knk
nkesalternativ
situation produced a total of 2.544x1017 
different CGM results.  Even if each 
calculation took only 0.001 second, it would 
require 70 x 109 hours of computer time.  
Thus, only variations in the end racks were considered for this summer, as the end racks 
contained racks with the largest xM values, resulting in the greatest moment arm shifts in the 
CGM from the CG equation.   
Results 
Many different computation runs were examined, more that can be illustrated in this summary 
report.  Figure 4 shows the packing difference of the MFT cases.  23 out of 30 results exceeded 
the 1” specification.  One conclusion in this limited study is that varying the racks tolerances 
significantly in the Verification Loads Analysis (VLA) process will result in a higher potential 
for larger packing differences.  This trend is confirmed in the enumerated studies as well. 
All worst case:  
MFT CG within SPEC Envelope
-15.00
-10.00
-5.00
0.00
5.00
10.00
15.00
-15.00 -5.00 5.00 15.00
Y
Z
6A; X=909
7A; X=898
UF-1; X=901
LIMIT@X=897"
LIMIT@X=901"
LIMIT@X=910"
DIFFERENCE:  MFT VS. MEASURED
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Nominal A B C D
D
is
ta
nc
e 
fro
m
 M
EA
SU
R
ED
 C
G
6A LT; X=903
6A HVY; X=906
7A LT; X=900
7A HVY; X=902
UF-1 LT; X=900
UF-1 HVY; X=900
Figure 4:  MFT Packing differences.   Figure 5.  Worst case CGM in envelope. 
Figure 5 shows the 6A, 7A, and UF-1 return flight worst-case scenarios for off-axis CGM in the 
enumerated case studies.  In the lightest-loaded flight (6A), the CGM is trending towards its 
envelope limit.  This chart, along with other data, suggests that lightly-loaded MPLM missions 
have much more variable potential to be loaded “off-balance.”  However, even for the worst case 
of this specific manifest, the CGM is still within the dimensional envelope.  Additional data show 
also that heavier missions tend to have less packing differences than lighter missions.   
Future Applications for the MSFC 
In the case studies examined, the center of gravity was calculated in lieu of the maximum loads 
exerted on the MPLM fittings.  This focus was based upon the assumption that the CGM position 
is correlated to such maximum loads.  Work from the MFT should have data, not reported, which 
should allow correlation analyses to be conducted as a preliminary confirmation.  If the CGM – 
maximum loads correlation is proved valid, then using the CGM as a screening tool for extreme 
loading cases would be a valuable in STS mission planning and future vehicle design studies.  
 
As part of investigating the VLA process, the author discovered that many different dynamic and 
structural computer models were required for validating each mission payload.[2]  This multi-
layered and time-consuming is likely due to limitations of computing power over two decades 
ago, when the STS was initially developed and deployed.  This project, combined with new 
 
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computing technology, may encourage streamlining of the VLA process by constructing a single 
VLA model.  Additionally, modern search techniques can be used with the integrated model to 
examine the safety of multiple missions simultaneously. 
Resources 
This research utilized routine office personal computer resources available in the Pressurized 
Carriers Group.  Additionally, MatLab was used to conduct the comparisons and enumerations.  
MatLab was also used to construct plotted data.  Microsoft Excel was also used to prepare tables 
for MatLab input, as well as providing some plotted results.  If the correlation analysis were 
performed in the future, the MatLab Statistics Toolbox module should be purchased for use.  
Consideration should also be given to access the Engineering Directorate computational 
resources in the dynamics and structural areas for future research. 
Conclusions 
The analysis of the MPLM CG is a potentially complex problem to address.  By restricting early 
studies to a few manifest alternatives, the problem becomes manageable, but the results are only 
valid for those studied manifests.  The rationale for restricting such studies is understandable, in 
light of the literally trillions of alternative scenarios to examine if all possible manifests were 
considered.  However, the MFT report and the results of the CG enumerations conducted by this 
researcher are only truly valid for the specific manifests under examination.  For a more 
thorough understanding of physical manifest variations (as opposed to the rack variations) to be 
accomplished, a probabilistic-based analysis is recommended, whereby the discrete factors 
amongst the racks are assigned estimated values.  This approach allows for an estimated CGM 
mean to be determined, with an associated deviation and even confidence level.  This data can 
subsequently be input into a probabilistic dynamics and structures model, enabling far fewer 
computational runs, yet giving statistically valid results. 
 
In order to study the manifest design problem, advanced computer tools should be surveyed in 
order to determine the best approach for handling the large number of manifest variations.  The 
use of recent computational design tools, such as genetic algorithms, simulated annealing, or 
neural networks, may aid in future manifest packing designs.   
Acknowledgements 
 
The author thanks the Pressurized Carriers Group in the Marshall Space Flight Center’s Flight 
Projects Directorate.  Scott Prichard, from Teledyne Brown Engineering, was especially helpful 
in providing historical rack data for this project.   
References 
 
[1]  MPLM Interface Definition Document, 11/14/2003, ISS-MPLM-IDD-006 Rev E. 
 
[2]  Structural Integration Analysis Responsibility Definition for Space Shuttle Vehicle and 
Cargo Element Developers, 1/19/01, NSTS 37329 Rev B..   
 
[3]  Gardner, S., Manifest Flexibility Task – Loads Analysis Summary, Technical Memorandum 
SCIS-TM-03-017, 3/31/03. 
 
XLIV-5


Rack Distribution Effects on MPLM Center of Mass

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