Thermal Analysis of an M256 120-mm Cannon 

by Joseph T. South and Robert H. Carter 

ARL-TR-3594 August 2005 

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Army Research Laboratory 

Aberdeen Proving Ground, MD 21005-5069 

ARL-TR-3594 August 2005 

Thermal Analysis of an M256 120-mm Cannon 

Joseph T. South and Robert H. Carter 

Weapons and Materials Research Directorate, ARL 

Approved for public release; distribution is unlimited. 
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 Thermal Analysis of an M256 120-mm Cannon 

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 Joseph T. South and Robert H. Carter (both of ARL) 

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Weapons and Materials Research Directorate 

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14. ABSTRACT 

In this work, the development and verification of a thermal finite element model for the M256 120-mm cannon is reported. 

Surface and interior temperatures of the M256 at various times and locations are reported after being subjected to a simulated 

ballistic event. This report details the development of the models with emphasis on mesh refinement for improved fidelity. The 

results of the model agree with experimentally obtained M256 thermal data. 

15. SUBJECT TERMS

 120-mm cannon; FEA; finite element analysis; M256; thermal 

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 Joseph T. South 

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iii

Contents 

List of Figures iv

List of Tables iv

1. Introduction 1

2. Approach 2

3. Input and Model Generation 2

3.1 Mesh Sensitivity Analysis...............................................................................................4 

4. Results 7

4.1 ID Temperature Results...................................................................................................7 

4.2 Sub-surfaceTemperature Results.....................................................................................8 

5. Summary 12

6. References 13 

Distribution List 14 
iv

List of Figures 

Figure 1. Heat flux of the propellant gas as a function of time and axial location........................ 3 

Figure 2. Cross section of the graded mesh density....................................................................... 4 

Figure 3. Results of the mesh sensitivity analysis ......................................................................... 5 

Figure 4. Through thickness temperature profiles at 640 mm from the RFT and 4.5 ms for a 

120-mm steel barrel with varying mesh densities................................................................... 5 

Figure 5. The effect of the number of elements on solution time and ID temperature.................. 6 

Figure 6. Predicted ID temperatures as a function of time at the four axial locations................... 7 

Figure 7. Predicted ID temperature at 640 mm from the RFT compared to the predicted 

temperature of Conroy et al. ................................................................................................... 8 

Figure 8. Sub-surface model prediction at 640 mm from the RFT compared to experimental 

measurements.......................................................................................................................... 9 

Figure 9. Sub-surface model prediction at 1050 mm from the RFT compared to experimental 

measurements.......................................................................................................................... 9 

Figure 10. Sub-surface model prediction at 1350 mm from the RFT compared to experimental 

measurements........................................................................................................................ 10 

Figure 11. Sub-surface model prediction at 1600 mm from the RFT compared to experimental 

measurements........................................................................................................................ 10 

Figure 12. Sub-surface model prediction at 640 mm from the RFT showing the perturbation 

that occurs near the ID. ......................................................................................................... 12 

List of Tables 

Table 1. Steel thermal properties used in the finite element model............................................... 2 
1

1. Introduction 

In this research, a two-dimensional (2-D) axisymmetric, transient, thermal finite element model 

of an M256 120-mm cannon has been generated. The goal of the research was to develop and 

validate a method to determine the surface and interior temperatures of an M256 at any time and 

location after it is subjected to a ballistic event. Previous thermal analyses of the M256 cannon 

have been performed; however, these investigations did not have the ability to continuously 

evaluate the temperatures within the cannon walls as function of time (1). 

The motivation for this research has been the development of advanced multi-material weapon 

systems. Advanced weapons systems for the Future Combat System may consist of hybrid 

barrels enabling these systems to achieve a lower combat load as well as offering the potential 

for increased muzzle velocity. The development of advanced gun systems requires a design that 

is robust mechanically as well as thermally. Mechanical strength is required so that the system 

will survive the ballistic event, and thermal stability is required so that the hybrid system does 

not deform or lose its mechanical integrity because of ballistic heating. 

D’Andrea et al. (2) found that the success of ceramic liners in cannon barrels resides in the 

controlling of longitudinal residual stresses, thermal gradients at the material interfaces, and the 

effects of differing coefficients of thermal expansion. They concluded that for a hybrid multimaterial weapon system to survive the ballistic event, a multi-axial constraint is necessary. This 

multi-axial constraint can be realized when the barrel liner is sheathed in such a way as to induce 

a compressive pre-stress. This sheath can be either a metallic or an organic based composite 

system. The temperature profile and thermal stresses become important because of the dissimilar 

materials of the liner and sheath. In the case of an organic based sheathing system, the 

temperature of the liner-sheath interface becomes paramount to the performance of the system 

because of the low operating temperature limits of the polymeric matrix materials. Knowledge 

of the temperature profile is essential for the stress states to be designed at the interface since 

they must remain in compression at all times. Given the extremely high flame temperatures of 

large caliber propellants, it is imperative that an approach be developed that would allow for the 

prediction of the axial as well as the radial temperature profiles. 

Before the finite element method is used to calculate the temperature profile for multi-material 

system, the models need to be developed and verified on all steel configurations. This will allow 

for observation of the effects of the different analytical parameters such as mesh density on the 

calculated temperature profile. 
2

2. Approach 

The finite element approach was chosen to evaluate the surface and interior temperature of an 

M256. The barrel dimensions were obtained from the specified technical drawings (3). Because 

of the nature of the problem, symmetry was employed to reduce the model from a threedimensional analysis to a 2-D axisymmetric analysis. A 2-D axisymmetric sketch of the barrel 

was created in a computer-aided drawing package (CAD) and then exported into the ANSYS1

finite element software. The use of this symmetric condition allowed for more economical 

computation time. 

3. Input and Model Generation 

In order to perform the analysis, the model required the material input over a range of temperatures. The thermal properties required were density, specific heat, and thermal conductivity. The 

steel thermal properties employed in the model are listed in table 1 (4). The material property 

data were limited to an upper temperature of 1143 K. If a material property was required at a 

higher temperature, the finite element software performed an extrapolation based upon table 1 to 

approximate the value. 

Table 1. Steel thermal properties used in the finite element model. 

Bulk Density 

(Kg m-2) 

Temperature 

(°C) 

Temperature 

(K) 

Specific Heat 

Cp (J kg-1K-1) 

Conductivity 

λ(W m-1K-1) 

7800 -239.55 33.45 411.69 12.28 

7800 -128.59 144.41 434.18 27.58 

7800 -17.64 255.36 459.03 34.89 

7800 93.32 366.32 486.99 38.07 

7800 204.28 477.28 515.64 38.63 

7800 315.23 588.23 551.37 37.61 

7800 426.19 699.19 594.63 35.87 

7800 537.14 810.14 654.27 33.51 

7800 648.10 921.10 730.34 30.71 

7800 759.06 1032.06 1493.83 26.99 

7800 870.01 1143.01 584.62 27.01 

The thermal forcing input into the model was provided by Conroy et al. (5) and computed through 

a coupled approach of interior ballistic calculations via XKTC2 (6) as well as the U.S. Army 

Research Laboratory version of XBR-2D (7,8). The boundary conditions were for an M829A2 

 1ANSYS, which is not an acronym, is a registered trademark of ANSYS, Inc.
3

projectile fired at 294 K. Ambient temperature was assumed to be 293 K. The interior ballistic 

code yielded the gas velocities, gas temperatures, and pressure. These core flow data were used as 

input to the ARL XBR-2D heat transfer/conduction code which then generated the heat transfer 

coefficients, film temperatures, and heat flux for 38 points along the inner diameter (ID) of the 

barrel at every microsecond. The described calculation methodology also produces barrel thermal 

profiles for arbitrary firing scenarios. The total time of the thermal forcing input was 0.9 second. 

A plot of the heat flux as a function of axial location and time is shown in figure 1. The data have 

been reduced for the ease of presentation. In the finite element model, either the heat flux or both 

the gas temperature and gas heat transfer coefficients were applied to simulate the ballistic event. 

0

0.508

1.016

1.905

2.921

4.699

1000

3000

5000

7000

9000

20000

40000

70000

0

100

200

300

400

500

600

700

800

Heat Flux [MW m-2 K-1]

Axial 

Location [m]

Time [μsec]

Figure 1. Heat flux of the propellant gas as a function of time and axial location. 

In the finite element model, the thermal forcing input was applied to the ID of the barrel and 

prescribed as tabular data. This yielded a data table consisting of 342,076 points. Application of 

the data as a table allowed for linear interpolation of the thermal data between the 38 known 

values along the ID. This linear interpolation, which was performed within the simulation, 

produced a smooth application of the input data during the analysis. To simulate operation of the 

cannon on an average day, thermal boundary conditions, constant heat transfer coefficient of 

11.45 W/m-2, and a temperature of 293 K were placed on the outer diameter (OD) of the cannon. 

The next step in the model generation was meshing of the barrel. The barrel was meshed with 

2-D axisymmetric eight-node elements. Higher order elements were used since the underlying 

quadratic shape function to the element is able to more accurately represent the results 

attributable to the ballistic event. The barrel was meshed with an element gradient that decreased 

in mesh density through the thickness from the ID to the OD. A cross-sectional view of this 
4

gradient is presented as figure 2. The purpose of the element gradient was to reduce the overall 

number of elements as the highly refined mesh was only required on the ID of the barrel where 

the combustion gases acted. 

Figure 2. Cross section of the graded mesh density. (The ID is 

at the bottom, the OD is on the top.) 

3.1 Mesh Sensitivity Analysis 

When the basic model was constructed, a mesh sensitivity analysis was performed to determine 

the optimum element size along the ID to yield accurate results. Four different variations of the 

model were created: coarse, medium, fine, and very fine. The coarse model has a base element 

size of 5 mm. In each of the following models, the elements along the ID of the barrel were 

refined to reduce their size. The very fine model possessed a final element size of 47 μm, 

providing an order of magnitude reduction in element size compared to the coarse model. 

The results of the mesh sensitivity analysis are presented in figures 3 through 5. Figure 3 shows 

the ID temperature at 640 mm from the rear face of the tube (RFT). The element size has a 

pronounced effect on the maximum predicted ID temperature. Increasing the mesh density 

greatly increases the predicted ID temperature. Both the coarse and the medium mesh density 

models appear to greatly under-predict the temperature at the ID, while the fine and the very fine 

models predict nearly the same temperature. An important feature to note is the fast rise of the 

temperature because of the ballistic event and the subsequent reduction of the temperature as the 

event passes and thermal energy can be diffused into the bulk of the barrel. 

The effect of the mesh density on the “through-thickness” temperature is plotted in figure 4. The 

figure shows the temperature through the thickness at 640 mm the RFT at 4.5 ms. The mesh 

density greatly affects the shape of the results. The figure shows a polynomial dependence of the 

temperature from the ID to the OD. This result is attributable to the quadratic shaped function of 

the eight-node element. The flux from the ballistic event, as shown in figure 1, is large enough 

and occurs over a small time period so that it places an extremely large ∆T across the element on 

the ID. The response of the element is limited to a quadratic shape function, which is not able to 

accurately represent the temperature profile. For a coarse mesh density, the quadratic element 

results in a response that over-predicts and then under-predicts the response of the system before 

reaching thermal equilibrium far from the ID. The magnitude and depth of this resulting error 
5

depend on the element size, and both are reduced as the element size is decreased. With a higher 

mesh density near the ID, the gradient is divided into sections that can be better represented by 

the quadratic shape function. The result is that the solution becomes more uniform; however, 

there are still minor deviations for the fine and very fine results. 

200

400

600

800

1000

1200

1400

1600

1800

2000

0 0.1 0.2 0.3 0.4 0.5

Time [sec]

Temperature [K]

Coarse Medium Fine Very Fine

Figure 3. Results of the mesh sensitivity analysis (ID temperature profiles at 640 mm RFT 

for a 120-mm steel barrel with varying mesh densities). 

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 0.002 0.004 0.006 0.008 0.01

Radial Distance from ID [m]

Temperaure [K]

Coarse Medium Fine Very Fine

Figure 4. Through thickness temperature profiles at 640 mm from the RFT and 4.5 ms for a 

120-mm steel barrel with varying mesh densities. 
6

0

50000

100000

150000

200000

250000

9851 38938 102583 163753

Number of Elements

Solution Time [sec]

300

500

700

900

1100

1300

1500

1700

1900

Coarse Medium Fine Very Fine

ID Temperature [K]

Time [sec] ID Temp [K]

Figure 5. The effect of the number of elements on solution time and ID temperature. 

The mesh density greatly affects the results on the ID as well as through the thickness. Increasing 

the mesh density can improve the results but at the expense of computation time. Figure 5 presents 

the effect of the number of elements on solution time and ID temperature. It is seen in the figure 

that there is very little difference between the predicted ID temperatures of the fine and very fine 

models; however, the solution time between the two differs by almost a factor of two. 

The areas of the model were meshed with the plane 82 ANSYS element. This is an eight-noded 

2-D element with plasticity and axisymmetric capability. The axis of symmetry was the y-axis. 

The results of the mesh sensitivity analysis showed that the results of the thermal model are 

highly dependent upon the element size for the ID as well as through the thickness. Because of 

the high heat flux that is placed on the barrel during the ballistic event, the first element on the 

ID of the barrel experiences a large ∆T. This large ∆T is similar to a step load. The model must 

use the underlying element shape function to achieve convergence. This produces both an 

under-prediction of the ID temperature and a quadratic dependence through the thickness of the 

model. Increasing the mesh density will reduce these effects but at the expense of solution time. 

Very fine element densities are appropriate for one-shot thermal models that require high 

fidelity; however, for models that require multiple shots or bulk material response, a fine element 

density would suffice. 
7

4. Results 

The model results were evaluated and compared to the experimental and predicted work of 

Conroy et al. (6-7). The primary goal was to validate the modeling methodology and approach. 

For this reason, the results of the very fine model were used since they produced the highest 

fidelity of the four models analyzed. Results of the model produced temperature, thermal 

gradients, and thermal fluxes as functions of both time and location. The model results were 

examined at four different axial locations: 640, 1050, 1350, and 1600 mm from the RFT. These 

locations were chosen so that the model results could be compared with the results of Conroy et 

al. (6-7) generated with the XBR2D-V29 code. 

4.1 ID Temperature Results 

The model predictions at the four axial locations as a function of time are presented in figure 6. 

Included in the figure is the predicted ID temperature at 640 mm from the RFT from 

Conroy et al. There are several features to note in the figure. First is that the highest ID 

temperatures occur at 640 mm from the RFT. Peak temperatures decrease as axial location 

moves toward the muzzle. A second feature to note is that all the ID temperatures exhibit an 

exponential decay with time. 

200

400

600

800

1000

1200

1400

1600

1800

2000

0 20 40 60 80 100

Time [ms]

ID Tempeature [K]

640 mm 1050 mm 1350 mm 1600 mm TR-770 640 mm Data

Figure 6. Predicted ID temperatures as a function of time at the four axial locations. (Included 

in the figure are the predicted data of Conroy et al.) 
8

An interesting result from the prediction is the difference in both the shape of the 640-mm RFT 

curve and the peak temperature compared to the data of Conroy et al. Figure 7 is a plot of only 

the data at 640 mm from the RFT. Both curves in the figure demonstrate roughly the same rise 

and fall after peak temperature, but there is a predicted difference for the peak temperature. The 

finite element analysis (FEA) model predicts a peak temperature of 1726 K, while the XBR2DV29 code predicts a peak of 1524 K. It appears that the difference may be attributable to a 

sampling error. The FEA predictions possess five points between the times of 3.5 and 6.5 ms, 

while none were output from the XBR2D-V29 code. The difference may also be because the 

FEA model did not include a chrome coating in the ID. The inclusion of the chrome, which 

possesses a higher thermal conductivity of 84 W m-1K-1 than steel would slightly reduce the ID 

temperature by being able to transport more thermal energy to the bulk material. However, with 

a chrome thickness of 140 μm, it is unlikely that the 200-K ∆T between the two models could be 

overcome. The difference between the two models is most likely attributable to the sampling 

rate. Regardless, the FEA model does appear to accurately predict the thermal response at the ID 

of the system. 

200

400

600

800

1000

1200

1400

1600

1800

2000

0 20 40 60 80 100

Time [ms]

ID Tempeature [K]

640 mm TR-770 640 mmData

Figure 7. Predicted ID temperature at 640 mm from the RFT compared to the predicted 

temperature of Conroy et al. 

4.2 Sub-surfaceTemperature Results 

To continue the validation of the model, the predicted sub-surface temperatures were compared 

to experimental data. In Conroy et al., an M256 gun tube was instrumented with in-wall 

thermocouples at the axial locations of 640, 1050, 1350, and 1600 mm from the RFT. The 

probes were ideally 1.27 mm from the bore surface. The results of the sub-surface analysis are 

presented as figures 8 through 11. The original experimental data for the figures possessed 

baseline temperatures between 280 and 283 K. In order to conduct an accurate comparison with 
9

the model results, the experimental data were increased to have a baseline temperature of 293 K. 

For each experimental axial location, five different radial model predictions were made. The 

exact radial locations vary slightly between the four axial locations since the model results can 

only be supplied where there is a node present in the finite element model. 

250

300

350

400

450

500

550

600

650

0 0.1 0.2 0.3 0.4 0.5

Time [sec]

Temperature [K]

640 Exp 0.4328 mm 0.8673 mm

1.3009 mm 1.7344 mm 2.1679 mm

Figure 8. Sub-surface model prediction at 640 mm from the RFT compared to experimental 

measurements. 

250

300

350

400

450

500

550

600

0 0.1 0.2 0.3 0.4 0.5 0.6

Time [sec]

Temperature [K]

1050 Exp 0.4343 mm 0.8686 mm

1.303 mm 1.7373 mm 2.715 mm

Figure 9. Sub-surface model prediction at 1050 mm from the RFT compared to experimental 

measurements. 
10

250

300

350

400

450

500

550

600

0 0.1 0.2 0.3 0.4 0.5 0.6

Time [sec]

Temperature [K]

1350 Exp 0.4339 mm 0.8679 mm

1.3019 mm 1.7359 mm 2.1751 mm

Figure 10. Sub-surface model prediction at 1350 mm from the RFT compared to experimental 

measurements. 

250

300

350

400

450

500

550

600

0 0.1 0.2 0.3 0.4 0.5 0.6

Time [sec]

Temperature [K]

1600 Exp 0.4343 mm 0.8686 mm

1.3029 mm 1.7372 mm 2.1715 mm

Figure 11. Sub-surface model prediction at 1600 mm from the RFT compared to experimental 

measurements. 

Figure 8 presents the sub-surface model predictions compared to the experimental thermocouple 

data. The experimental data best match the prediction at 1.7344 mm. This value falls within the 
11

range predicted by Conroy et al., who found the experimental data lying between 1.52 and 

1.78 mm. In their calculations, the experimental data were closer to the 1.52-mm prediction 

while the FEA prediction best matches the 1.7344-mm point. Given that their model predicted a 

lower ID temperature than the FEA, it makes sense that the FEA would predict the same 

temperature farther from the ID surface. In figure 8, the predicted and the experimental data do 

not asymptotically approach one another as time increases. This result was also found in 

Conroy et al. Most likely, the difference between the experimental and the predicted was 

attributable to the error associated with the thermocouple. 

There appears to be a delay of approximately 35 ms between the experimental and the predicted 

results. If data were shifted, by this time there would be closer agreement between the two data 

sets. It is believed that the difference in the time may be attributable to an overly simplistic 

model of the ignition delay and the flame spreading process. Although this research used a 

modern interior ballistics code, the physics of the codes have not changed but the fidelity has 

increased dramatically. The temporal disparities attributable to ignition and flame spreading are 

an intrinsic feature of interior ballistics codes (8). 

The results for the axial 1050, 1350, and 1600 mm from the RFT locations, shown in figures 9 

through 11, demonstrate similar behavior as the 640 mm from the RFT point. These temperatures 

versus time plots demonstrate similar shapes and magnitudes. In all four cases, the experimental 

data are bound by the FEA results within 1 mm of the ideal 1.25-mm radial location. The 

hypothesis that the 640-mm data were affected by the thermocouple is supported as the data 

asymptotically approach a steady state value for the 1050-, 1350-, and 1600-mm locations. 

A feature to note in figures 8 through 11 is that a perturbation still exists close to the ID. 

Figure 12 shows this perturbation for the 640-mm axial location. As discussed in the mesh 

sensitivity analysis section, the elements in the FEA have an underlying quadratic shape 

function. Despite the 47-μm size of the ID elements, the heat flux from the ballistic event is so 

great that a minor perturbation of the results can still occur through the thickness. The 

magnitude of this perturbation will approach 0 as the element size decreases; this was shown in 

figure 4. 
12

250

300

350

400

450

500

550

600

650

0 0.01 0.02 0.03 0.04 0.05

Time [sec]

Temperature [K]

640 Exp 0.4328 mm 0.8673 mm

1.3009 mm 1.7344 mm 2.1679 mm

Figure 12. Sub-surface model prediction at 640 mm from the RFT showing the perturbation 

that occurs near the ID. 

5. Summary 

FEA is capable of calculating the thermal profiles for an M256 cannon. Mesh density is 

important for accuracy and to minimize anomalous behavior because of interpolation errors 

attributable to large gradients across a single element. The cost of accuracy versus computation 

time is demonstrated as the ID temperature appears to asymptotically approach a value at the 

expense of computation time. When compared to previous analyses, the data appear to be very 

similar. The temperatures are accurate within a millimeter of the stated position of the 

thermocouple when compared to experimental test data. These results support using this 

modeling approach to calculate the temperature profiles for hybrid barrel designs. 
13

6. References 

1. Artus B.; Hasenbein R. Thermal Study of the 120-mm M256 Cannon Tube; TR ARCCB-TB89028; U.S. ARDEC, 1989. 

2. D’Andrea G.; Cullinan R. L.; Croteau P. J. Refractory-Lined Composite Pressure Vessels; 

Technical Report ARLCB-TR-78023; U.S. Army Research and Development Command: 

Watervliet, NY, 1978. 

3. M256 Technical Data Package, Armament Research Development and Engineering Center, 

Picatinny Arsenal, NJ 

4. Hummel R. E. Electronic Properties of Materials, 2nd Ed., Springer-Verlag, New York, 

New York, 1993. 

5. Conroy P. J.; Bundy M. L.; Kennedy J. L. Simulated and Experimental In-Wall 

Temperatures for 120-mm Ammunition; ARL-TR-770; U.S. Army Research Laboratory: 

Aberdeen Proving Ground, MD, 1995. 

6. Gough P. S. The XNOVAKTC Code; BRL-CR-627; U.S. Army Ballistic Research 

Laboratory: Aberdeen Proving Ground, MD, 1990. 

7. Conroy, P.J. Gun Tube Heating; BRL-TR-3300; U.S. Army Ballistic Research Laboratory: 

Aberdeen Proving Ground, MD, December 1991. 

8. Crickenberger, A.B.; Talley, R.L.; Taller, J.Q. Modifications to the XBR-2D Heat 

Conduction Code; ARL-CR-126; U.S. Army Research Laboratory: Aberdeen Proving 

Ground, MD, April 2004. 

9. Conroy, P. J.; Bundy, M. L.; Kennedy, J. L. Simulated and Experimental In-Wall 

Temperatures for 120-mm Ammunition. Defence Science Journal 1996, 46 (4), 223-232. 

10. Conroy P. J. personal communication, U.S. Army Research Laboratory: Aberdeen Proving 

Ground, MD, July 2005. 
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 ATTN AMSTA AR CCH A L MANOLE 

 S MUSALLI R CARR M LUCIANO 

 E LOGSDEN T LOUZEIRO 

 PICATINNY ARSENAL NJ 07806-5000 

 1 CDR US ARMY ARDEC 

 ATTN AMSTA AR CCH P J LUTZ 

 PICATINNY ARSENAL NJ 07806-5000 

 1 CDR US ARMY ARDEC 

 ATTN AMSTA AR FSF T C LIVECCHIA 

 PICATINNY ARSENAL NJ 07806-5000 

 1 CDR US ARMY ARDEC 

 ATTN AMSTA ASF 

 PICATINNY ARSENAL NJ 07806-5000 

 1 CDR US ARMY ARDEC 

 ATTN AMSTA AR QAC T C J PAGE 

 PICATINNY ARSENAL NJ 07806-5000 

 1 CDR US ARMY ARDEC 

 ATTN AMSTA AR M D DEMELLA 

 PICATINNY ARSENAL NJ 07806-5000 

 3 CDR US ARMY ARDEC 

 ATTN AMSTA AR FSA A WARNASH 

 B MACHAK M CHIEFA

 PICATINNY ARSENAL NJ 07806-5000 

 2 CDR US ARMY ARDEC 

 ATTN AMSTA AR FSP G M SCHIKSNIS 

 D CARLUCCI

 PICATINNY ARSENAL NJ 07806-5000 

 2 CDR US ARMY ARDEC 

 ATTN AMSTA AR CCH C H CHANIN 

 S CHICO

 PICATINNY ARSENAL NJ 07806-5000 
15

NO. OF 

COPIES ORGANIZATION

 1 CDR US ARMY ARDEC 

 ATTN AMSTA AR QAC T D RIGOGLIOSO 

 PICATINNY ARSENAL NJ 07806-5000 

 9 CDR US ARMY ARDEC 

 ATTN AMSTA AR CCH B P DONADIA 

 F DONLON P VALENTI 

 C KNUTSON G EUSTICE 

 K HENRY J MCNABOC 

 R SAYER F CHANG 

 PICATINNY ARSENAL NJ 07806-5000 

 1 PM ARMS 

 ATTN SFAE GCSS ARMS 

 BLDG 171 

 PICATINNY ARSENAL NJ 07806-5000 

 1 CDR US ARMY ARDEC 

 ATTN AMSTA AR WEA J BRESCIA 

 PICATINNY ARSENAL NJ 07806-5000 

 1 CDR US ARMY TACOM 

 PM COMBAT SYSTEMS 

 ATTN SFAE GCS CS 

 6501 ELEVEN MILE RD 

 WARREN MI 48397-5000 

 1 CDR US ARMY TACOM 

 PM SURVIVABLE SYSTEMS 

 ATTN SFAE GCSS W GSI H M RYZYI 

 6501 ELEVEN MILE RD 

 WARREN MI 48397-5000 

 1 CDR US ARMY TACOM 

 CHIEF ABRAMS TESTING 

 ATTN SFAE GCSS W AB QT 

 T KRASKIEWICZ 

 6501 ELEVEN MILE RD 

 WARREN MI 48397-5000 

 1 COMMANDER 

 US ARMY TACOM 

 ATTN AMSTA SF 

 WARREN MI 48397-5000 

 1 DIR AIR FORCE RSCH LAB 

 ATTN MLLMD D MIRACLE 

 2230 TENTH ST 

 WRIGHT PATTERSON AFB OH 45433-7817 

 1 OFC OF NAVAL RESEARCH 

 ATTN J CHRISTODOULOU 

 ONR CODE 332 

 800 N QUINCY ST 

 ARLINGTON VA 22217-5600 

NO. OF 

COPIES ORGANIZATION

 1 CDR WATERVLIET ARSENAL 

 ATTN SMCWV QAE Q B VANINA 

 BLDG 44 

 WATERVLIET NY 12189-4050 

 2 HQ IOC TANK 

 AMMUNITION TEAM 

 ATTN AMSIO SMT R CRAWFORD 

 W HARRIS 

 ROCK ISLAND IL 61299-6000 

 2 COMMANDER 

 US ARMY AMCOM 

 AVIATION APPLIED TECH DIR 

 ATTN J SCHUCK 

 FORT EUSTIS VA 23604-5577 

 1 NSWC 

 DAHLGREN DIV CODE G06 

 DAHLGREN VA 22448 

 2 US ARMY CORPS OF ENGINEERS 

 ATTN CERD C T LIU 

 CEW ET T TAN 

 20 MASSACHUSETTS AVE NW 

 WASHINGTON DC 20314 

 1 US ARMY COLD REGIONS 

 RSCH & ENGRNG LAB 

 ATTN P DUTTA 

 72 LYME RD 

 HANOVER NH 03755 

 4 CDR US ARMY TACOM 

 ATTN AMSTA TR R R MCCLELLAND 

 D THOMAS J BENNETT 

 D HANSEN 

 WARREN MI 48397-5000 

 4 CDR US ARMY TACOM 

 ATTN AMSTA JSK S GOODMAN 

 J FLORENCE D TEMPLETON 

 A SCHUMACHER 

 WARREN MI 48397-5000 

 5 CDR US ARMY TACOM 

 ATTN AMSTA TR D D OSTBERG 

 L HINOJOSA B RAJU 

 AMSTA CS SF H HUTCHINSON 

 F SCHWARZ 

 WARREN MI 48397-5000 
16

NO. OF 

COPIES ORGANIZATION

 6 BENET LABS 

 ATTN AMSTA AR CCB R FISCELLA 

 M SOJA E KATHE G FRIAR 

 M SCAVULO G SPENCER 

 WATERVLIET NY 12189-4050 

 4 BENET LABS 

 ATTN AMSTA AR CCB P WHEELER 

 S KRUPSKI J VASILAKIS 

 R HASENBEIN 

 WATERVLIET NY 12189-4050 

 4 BENET LABS 

 ATTN AMSTA CCB R S SOPOK 

 E HYLAND D CRAYON 

 R DILLON 

 WATERVLIET NY 12189-4050 

 1 USA SBCCOM PM SOLDIER SPT 

 ATTN AMSSB PM RSS A J CONNORS 

 KANSAS ST 

 NATICK MA 01760-5057 

 1 NSWC 

 TECH LIBRARY CODE B60 

 17320 DAHLGREN RD 

 DAHLGREN VA 22448 

 2 USA SBCCOM 

 MATERIAL SCIENCE TEAM 

 ATTN AMSSB RSS J HERBERT 

 M SENNETT 

 KANSAS ST 

 NATICK MA 01760-5057 

 2 OFC OF NAVAL RESEARCH 

 ATTN D SIEGEL CODE 351 J KELLY 

 800 N QUINCY ST 

 ARLINGTON VA 22217-5660 

 1 NSWC 

 CRANE DIVISION 

 M JOHNSON CODE 20H4 

 LOUISVILLE KY 40214-5245 

 2 NSWC 

 ATTN U SORATHIA 

 C WILLIAMS CODE 6551 

 9500 MACARTHUR BLVD 

 WEST BETHESDA MD 20817 

NO. OF 

COPIES ORGANIZATION

 2 CDR NSWC 

 CARDEROCK DIVISION 

 ATTN R PETERSON CODE 2020 

 M CRITCHFIELD CODE 1730 

 BETHESDA MD 20084 

 4 DIR US ARMY NGIC 

 ATTN D LEITER MS 404 

 J GASTON MS 301 

 M HOLTUS MS 301 

 M WOLFE MS 307 

 2055 BOULDERS RD 

 CHARLOTTESVILLE VA 22911-8318 

 4 DIR US ARMY NGIC 

 ATTN S MINGLEDORF MS 504 

 W GSTATTENBAUER MS 304 

 R WARNER MS 305 

 J CRIDER MS 306 

 2055 BOULDERS RD 

 CHARLOTTESVILLE VA 22911-8318 

 1 NAVAL SEA SYSTEMS CMD 

 ATTN D LIESE 

 1333 ISAAC HULL AVE SE 1100 

 WASHINGTON DC 20376-1100 

 4 US ARMY SBCCOM 

 SOLDIER SYSTEMS CTR 

 BALLISTICS TEAM 

 ATTN J WARD W ZUKAS 

 P CUNNIFF J SONG 

 KANSAS ST 

 NATICK MA 01760-5019 

 3 US ARMY SBCCOM 

 SOLDIER SYSTEMS CTR 

 MARINE CORPS TEAM J MACKIEWICZ 

 ATTN AMSSB RCP SS W NYKVIST 

 S BEAUDOIN 

 KANSAS ST 

 NATICK MA 01760-5019 

 7 US ARMY RESEARCH OFC 

 ATTN A CROWSON H EVERITT 

 J PRATER G ANDERSON 

 D STEPP D KISEROW J CHANG 

 PO BOX 12211 

 RSCH TRIANGLE PARK NC 27709-2211 

 1 AFRL MLBC 

 2941 P ST RM 136 

 WRIGHT PATTERSON AFB OH 45433-7750 
17

NO. OF 

COPIES ORGANIZATION

 1 DIRECTOR 

 LOS ALAMOS NATL LAB 

 ATTN F L ADDESSIO T 3 MS 5000 

 PO BOX 1633 

 LOS ALAMOS NM 87545 

 4 NSWC 

 ATTN J FRANCIS CODE G30 

 D WILSON CODE G32 

 R D COOPER CODE G32 

 J FRAYSSE CODE G33 

 DAHLGREN VA 22448 

 4 NSWC 

 ATTN T DURAN CODE G33 

 L DE SIMONE CODE G33 

 R HUBBARD CODE G33 

 DAHLGREN VA 22448 

 2 AFRL 

 ATTN F ABRAMS J BROWN 

 BLDG 653 

 2977 P ST STE 6 

 WRIGHT PATTERSON AFB OH 45433-7739 

 1 AFRL MLS OL 

 ATTN L COULTER 

 5851 F AVE 

 BLDG 849 RM AD1A 

 HILL AFB UT 84056-5713 

 1 OSD 

 JOINT CCD TEST FORCE 

 ATTN OSD JCCD R WILLIAMS 

 3909 HALLS FERRY RD 

 VICKSBURG MS 29180-6199 

 2 OAK RIDGE NATL LAB 

 ATTN R M DAVIS 

 C EBERLE MS 8048 

 PO BOX 2008 

 OAK RIDGE TN 37831-6195 

 3 DIR SANDIA NATL LABS 

 APPLIED MECHS DEPT 

 ATTN MS 9042 J HANDROCK 

 Y R KAN J LAUFFER 

 PO BOX 969 

 LIVERMORE CA 94551-0969 

 1 ALLIANT TECHSYSTEMS INC 

 4700 NATHAN LN N 

 PLYMOUTH MN 55442-2512 

NO. OF 

COPIES ORGANIZATION

 5 UNIV OF DELAWARE 

 CTR FOR COMPOSITE MTRLS 

 ATTN J GILLESPIE M SANTARE 

 S YARLAGADDA S ADVANI 

 D HEIDER 

 201 SPENCER LAB 

 NEWARK DE 19716 

 ABERDEEN PROVING GROUND

 1 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL CI OK (TECH LIB) 

 BLDG 4600 

 1 US ARMY ATC 

 ATTN CSTE DTC AT AC I W C FRAZER 

 BLDG 400 

 1 DIR USARL 

 ATTN AMSRD ARL CI 

 BLDG 

 1 DIR USARL 

 ATTN AMSRD ARL O AP EG M ADAMSON 

 BLDG 

 1 DIR USARL 

 ATTN AMSRD ARL SL BB D BELY 

 BLDG 328 

 2 DIR USARL 

 ATTN AMSRD ARL WM J SMITH 

 D LYON 

 BLDG 4600 

 2 DIR USARL 

 ATTN AMSRD ARL WM B CHIEF 

 T KOGLER 

 BLDG 4600 

 2 DIR USARL 

 ATTN AMSRD ARL WM BC P PLOSTINS 

 J NEWILL 

 BLDG 390 

 3 DIR USARL 

 ATTN AMSRD ARL WM BD B FORCH 

 R PESCE-RODRIGUEZ B RICE 

 BLDG 4600 

 3 DIR USARL 

 ATTN AMSRD ARL WM BD P CONROY 

 C LEVERITT A ZIELINSKI 

 BLDG 390 
18

NO. OF 

COPIES ORGANIZATION

 2 DIR USARL 

 ATTN AMSRD ARL WM BE R LIEB 

 M LEADORE 

 BLDG 4600 

 1 DIR USARL 

 ATTN AMSRD ARL WM BF 

 S WILKERSON 

 BLDG 390 

 2 DIR USARL 

 ATTN AMSRD ARL WM M J MCCAULEY 

 S MCKNIGHT 

 BLDG 4600 

 2 DIR USARL 

 ATTN AMSRD ARL WM MA 

 L GHIORSE E WETZEL 

 BLDG 4600 

 22 DIR USARL 

 ATTN AMSRD ARL WM MB 

 J BENDER T BOGETTI 

 J BROWN L BURTON 

 R CARTER K CHO W DE ROSSET 

 G DEWING R DOWDING 

 W DRYSDALE R EMERSON 

 D GRAY D HOPKINS R KASTE 

 L KECSKES M MINNICINO 

 B POWERS D SNOHA 

 J SOUTH M STAKER 

 J SWAB J TZENG 

 BLDG 4600 

 11 DIR USARL 

 ATTN AMSRD ARL WM MC J BEATTY 

 R BOSSOLI E CHIN 

 S CORNELISON D GRANVILLE 

 B HART J LASALVIA 

 J MONTGOMERY F PIERCE 

 E RIGAS W SPURGEON 

 BLDG 4600 

 11 DIR USARL 

 ATTN AMSRD ARL WM MD P DEHMER 

 B CHEESEMAN R DOOLEY 

 G GAZONAS S GHIORSE 

 M KLUSEWITZ W ROY J SANDS 

 S WALSH D SPAGNUOLO S WOLF 

 BLDG 4600 

 2 DIR USARL 

 ATTN AMSRD ARL WM RP J BORNSTEIN 

 C SHOEMAKER 

 BLDG 1121 

NO. OF 

COPIES ORGANIZATION

 1 DIR USARL 

 ATTN AMSRD ARL WM T B BURNS 

 BLDG 309 

 1 DIR USARL 

 ATTN AMSRD ARL WM TA W GILLICH 

 BLDG 309 

 7 DIR USARL 

 ATTN AMSRD ARL WM TA M BURKINS 

 B GOOCH T HAVEL C HOPPEL 

 E HORWATH J RUNYEON 

 M ZOLTOSKI 

 BLDG 393 

 1 DIR USARL 

 ATTN AMSRD ARL WM TB P BAKER 

 BLDG 309 

 1 DIR USARL 

 ATTN AMSRD ARL WM TC R COATES 

 BLDG 309 

 4 DIR USARL 

 ATTN AMSRD ARL WM TD D DANDEKAR 

 M RAFTENBERG S SCHOENFELD 

 T WEERASOORIYA 

 BLDG 4600 

 2 DIR USARL 

 ATTN AMSRD ARL WM TE CHIEF 

 J POWELL 

 BLDG 120