MP-091-19

NATO UNCLASSIFIED
Numerical Simulation of Reaction Violence
to Cook-Off Experiments
D. Desailly – Y. Guengant – B. Briquet – P. Brunet – D. Houdusse
Groupe SNPE/Centre de Recherches du Bouchet
rue Lavoisier
91710 Vert Le Petit
FRANCE
ABSTRACT
Violence level prediction for Cook-off events is a major concern for Insensitive Munitions, and moreover
for solid propellant rocket motor. Before they are accepted as being safe and suitable for introduction into
military inventories, systems undergo a wide range of expensive type qualification trials. There is a need,
therefore, to develop a greater understanding of the reaction mechanisms that affect the behaviour of
confined energetic materials when subjected to external stimuli, and computer models to control the
resulting hazard. Many research programs have been investigated at SNPE Propulsion to predict
reactions types (I, II, III, IV, V) in respect to IM STANAG.
SNPE Propulsion has developed a method for modelling Cook-off response in order to master the design
of Insensitive Munitions (IM). The method requires modular tools describing each physical phenomenon.
Works have been first aimed at the development of experimental facilities to characterise thermal and
chemical properties, and reactivity of thermally damaged energetic material. Consequently, the first part
of the model describing self–heating and pyrolysis processes was implemented in an implicit solver
(ABAQUS). Recent developments are now concentrated on the numerical modelling of the reaction
violence level prediction hence a thermal damage dependent burning model V.R.E.M.E. has been
incorporated in an explicit solver (LS-DYNA).
First results come from simulations performed on generic vehicles, full scale munitions, warheads and
rocket motors. They have shown quite accurate predictions of ignition temperature and time to event that
proved the effectiveness of this approach. Concerning the violence of reaction, no validation phase has
been performed at this time, but results accuracy appears to be encouraging and attractive for future
applications.
1.0 INTRODUCTION
Since the Second World War, in peacetime or during military operations, most of munitions accidents
have been due to cook off events. These have happened directly in case of unstable aged energetic
materials reaction at moderate temperatures, indirectly when conventional fires reached munitions
stockpiles, vehicles or combat platform. Everybody has heard comments about major accidents of
US Navy ships (Orraksiny, Forrestal, Entreprise, Nimitz) or the Camp Doha accident during Gulf War –
Desert Storm. We must not forget two land storage severe accidents during Vietnam War (Qui Nhon for
example), and more recently, in India (January 11th, 2002 Bikaner), Nigeria (January 27th, 2002, Lagos)
or Thailand (March 28th, 2002, Aranyaprathet).
Thus, predict the reaction and master Cook-off events are a major concern for Insensitive Munitions
designers, especially for solid propellant rocket motors. After the self-ignition phenomenon and under
Paper presented at the RTO AVT Specialists’ Meeting on “Advances in Rocket Performance Life and Disposal”,
held in Aalborg, Denmark, 23-26 September 2002, and published in RTO-MP-091.
RTO-MP-091
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some confinement and thermal conditions, burning can accelerate and lead to more or less violent
combustions, explosions or even to Deflagration to Detonation Transition events. It was shown that the
cook-off reaction level is ruled by the competition between combustion gas flow, the pressure build-up
and the stress release resulting from the break-up confinement.
Thus, the challenge is to predict reaction types (I, II, III, IV and V) according to Insensitive Munitions
codification and to respect IM STANAG requirements. Our objective is to develop and evaluate test
methods and computer models for predicting the response of pyrotechnical systems to the perceived
threats of Fast and Slow Cook-off. In order to reduce vulnerability while preserving performances,
sensitivity of the results to the dimensioning parameters can be considered (composition optimisation,
free volumes, confinement …).
2.0 BACKGROUND ON MUNITIONS BEHAVIOUR PREDICTION TO
COOK-OFF
Predictive methodology developed by SNPE, is based both on experimental characterisations and
numerical modelling. The first step is constituted by experiments involving measurements techniques
leading to chemical and physical mechanisms. This overall understanding of mechanisms and variables
controlling cook-off is essential for establishing predictive tools.
For each elementary mechanism, the model development is divided into four stages:
1. Experimental characterisations on Energetic Materials to achieve a database of intrinsic
properties,
2. Physics model development,
3. Fit of the numerical parameters from the experimental database,
4. Implementation of this physical model within a finite elements code to take into account
multidimensional munitions design.
2.1
Cook-Off Phenomena – Identification of Reaction Mechanisms
An Energetic Material subjected to a thermal threat exhibits exothermic reactions due to chemical
decompositions. Thus, this self-heating runaway process may induce self-ignition: the event that follows is
usually called Cook-off.
Typical time to ignition can be range from few seconds to a few tens hours. Due to thermal exchanges,
at high temperatures, ignition occurs close to the energetic material grain surface, at moderate
temperatures, the self-ignition occurs inside the grain.
The chemical degradation reactions generate also some gas by pyrolysis phenomena with cracks and
vacuoles as result. Energetic material porosity can increase drastically and when self-ignition occurs,
the combustion of this porous media can be very fast and violent. Then, the propagation of this
combustion can go from a conductive to a convective form (usually called a deflagration) with regard to
influent parameters.
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Figure 1: Sights of Pristine (on left) and Thermally Damaged Propellant (on right) Porosity in Binder and AP Particles.
Energetic materials behaviour subjected to a thermal threat is complex and follows different time scales:
self-heating process is counted in minutes or hours whereas pyrotechnic event may require less than a few
milliseconds. Thus, we have chosen to develop some modular tools for each phase and phenomenon.
2.2
Main Hypotheses of the Physical Model
The methodology developed by the SNPE Research Centre is macroscopic, the ambition is to model the
main physical mechanisms. Modelling each chemical degradation (a few tens for propellant) seems very
difficult through available resources.
In this way, we defined characterisation tests suitable with the main reactions identified previously.
More precisely, the objective is to quantify:
•
the self-heating phenomenon,
•
the amount of thermally damaged energetic material (quantified by pyrolysis gas flow and mass
loss),
•
the relation between the burning velocity and the reactivity of thermally damaged product.
Our methodology is based on correlation between thermal damage, mass loss and reactivity of energetic
materials. This assumption is that a one-to-one relation connects thermal damage (which is quantified
through mass loss) and the energetic material reactivity. Indeed, it is established that prior ignition,
thermal damage is a key parameter to reaction violence level. Thus, we can separately distinguish and
treat quasi-static phenomena (chemical and physical damages & self-heating) and dynamical ones
(combustion, deflagration, pressure burst, …).
3.0 CHARACTERISATION TESTS
Choice of experimental tests is essential to obtain confident data. Samples must be large enough to get all
phenomena and thermal conditioning must cover the wide range of full-scale munitions threats.
3.1
Unconfined Thermo-Ignition Test [8]
This test has been performed for about thirty years. It is devoted to define critical temperature, data that
lead to the self-heating kinetic parameters and potential violence level of each composition.
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Tests are performed for ten constant temperatures with cylindrical samples (50 mm diameter & length)
put in an oven where heating is a convective flux type. Time-to-ignition and temperature history curves at
the middle of the sample are recorded.
Critical temperature is the maximum temperature that will never lead to ignition.
oven temperature
THERMOCOUPLES
212
193
191
187
184
240
ALUMINIUM
CASE
SAMPLE CENTER
TEMPERATURE °C
230
STEEL OVEN
220
210
200
190
180
170
160
ELECTRICAL
RESISTANCE
UNITS
THE SAMPLE IS PACKED
WITH ALUMINUM PAPER
2000
0
4000
6000
8000
10000
12000
14000
TIME IN MINUTES
Figure 2: Set-Up of Unconfined
Thermo-Ignition Test.
3.2
200 196
Figure 3: Temperature History Curves
at Sample Central Point.
Under Confinement Pyrolysis Test [8]
This test was developed to establish pyrolysis kinetic through increasing pressure. Experimental results
enable us to fit kinetic parameters in order to predict the mass loss.
PRESSURE
POROUS
50
Pression (bars)
DISC
TRANSDUCER
STEEL
PART
ALUMINUM
40
CASE
ELECTRICAL
HEATING
RESISTANCES
206 °C
216 °C
30
203 °C
20
200 °C
196 °C
10
INSULATION
Temps (mn)
UNDER CONFINEMENT PYROLYSIS
0
Figure 4: Set-Up of Under
Confinement Pyrolysis Test.
3.3
1000
2000 3000
4000
5000 6000
7000
8000
9000
10000 11000 12000
Figure 5: Increasing Pressure Curves
according to Temperatures.
High Pressure Closed Vessel Bomb
To establish the correlation between reactivity and thermally damaged product, we perform tests in a
High Pressure Closed Vessel Bomb under temperature control.
The specific feature of such a system is to perform the tests at high temperature. Thus, we can estimate
burning rate of thermally damaged samples without relaxation process.
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120
dP/dt (MPa/ms)
100
80
60
40
20
Bouchon capteur +
allumage
0
Corps + chambre de
combustion montée+ joint
0
50
100
150
P(MPa)
200
250
300
Figure 7: Derived Pressure Curves according
to Different Temperatures.
Figure 6: “Diodon” High
Pressure Closed Vessel Bomb.
A combustion law of the Vieille type is deduced from pressure signals and sample geometry. A burning
rate versus pressure law is then established with regard to thermal conditioning, and so with regard to
mass loss γ:
V (γ ) = a(γ ) P n (γ )
This apparatus is adapted to work up to 5 Kbars: we can study a large scope of burning rate according to
porosity of thermally damaged energetic materials. Nevertheless, we now focus on experimental devices
development quantifying more violent reactions over the burning regimes (Deflagration to Detonation
Transition for instance).
4.0 PHYSIC MODEL DEVELOPMENT & IMPLEMENTATION IN
NUMERICAL SOLVERS
Numerical works was first focused on the time to ignition prediction and thermal damage state assessment,
quantified by the mass loss. Recent developments are thus concentrated on the numerical modelling of the
reaction violence.
4.1
Thermochemical Model
4.1.1
Thermo-Ignition Kinetic Model
A feature of some energetic materials is to exhibit some endothermic or exothermic decomposition
processes as a function of temperature. Thus, heat transfer computation is based on the balance equation:
λ ∆2 T
heat flow
λ
T
ρ
S
S
+
=
heat source of EM (Endoexothermic processes)
thermal conductivity
temperature
density
heat source
RTO-MP-091
ρ Cp
dT
dt
heat accumulation
∆2
Cp
t
Laplacian operator
specific heat
time
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Physical parameters ( ρ , C P , λ ) are known as functions of temperature. The heat source parameters are
fitted by a three steps Arrhenius law on the unconfined thermo-ignition test results:
Reactions:
Species:
Heat of reactions:
1
2
3
A→
B→
C→
Q1 > 0
Q2 < 0
Q3 > 0
D
This scheme originates in experimental observations.
Concentration evolution equations enable us to describe reactions rates of progress
dε i
(i = 1,2,3) :
dt
dε 1
− E / RT
= (1 − ε 1 ) Z 1e a1
dt
dε 2
− E / RT
= (ε 1 − ε 2 ) Z 2 e a2
dt
dε 3
− E / RT
= (ε 2 − ε 3 ) Z 3 e a3
dt
εi
=
Progress of reaction i
Zi
=
Frequency factor of reaction i
E ai / R =
Activation energy of reaction i / Perfect gas constant
T
Temperature
=
Volumetric heat flux S generated within the energetic material is written as:
S = ρ ∑ Qi
i =1, 3
Qi
=
dε i
dt
Heat of reaction i
The 9 kinetic parameters (Qi , Z i , E ai for i = 1, 2, 3) are fitted on experimental results (Unconfined
Thermo-ignition test) through a specific minimisation procedure based on simplex method. Heating rates
are assumed to reproduce thermal threats from Slow Cook-off to fast Cook-off.
Figures 8 shows, with adapted scale, the calculated temperature curve of the sample central point when
a 180 °C ambient air is given. Figures 9 gives reactions progress in the same conditions. Features
corresponding to the three reactions can be clearly distinguished.
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1
Temperature a coeur - T source = 180 C
Avancement 1
Avancement 2
Avancement 3
200
Cinetique tri-etage
Reaction 1
Reaction 3
190
0.8
ε1
Temperature en C
180
0.6
170
Reaction 2
ε2
0.4
160
150
ε3
0.2
140
0
10000
20000
30000
40000
Temps en s
50000
60000
70000
80000
0
0
Figure 8: Temperature Curve of the
Sample Central Point.
10000
20000
30000
40000
50000
60000
70000
80000
90000
100000
Figure 9: Progress of Reactions.
4.1.2
Pyrolysis Model
Pyrolysis kinetic is characterised from the “Under Confinement Pyrolysis test”. The gas production is
modelled with an Arrhenius law similar to the self-ignition kinetic. We use a one step kinetic and the gas
production is calculated from the formula:
G = g (1 - ε) Z e -Ea/RT
G
gas production
Ea
activation energy
g
gas mass produced per EM mass unit
R
gas constant
ε
reaction kinetic
T
temperature
Z
frequency factor
This Arrhenius law is determined analytically from the pyrolysis characterisation test.
4.1.3
Implementation in a Finite Elements Code
To take into account bulk effects of systems, the self-heating and pyrolysis models have been
implemented in a well-adapted finite elements solver. For designing its grains, SNPE usually operates
with the commercial software ABAQUS for thermomechanical analysis of structure under large strains.
Once the computation is completed results, can be analysed in terms of:
•
temperature distribution, location of ignition and time to reaction (self-heating modelling)
•
thermal degradation and pressure increasing (pyrolysis modelling)
All these data will be used for the prediction of the reaction violence.
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4.2
Numerical Model V.R.E.M.E. ([13], [14])
4.2.1
Main Ideas – Description
This chapter primarily concentrates on the implementation of the combustion model. A general description
of the governing processes and constitutive laws is introduced.
The simulation of the violence of reaction resulting from the chemical decomposition of the energetic
material requires a coupling between the energy issued from the combustion and the energy released by
the deformation process of the system. This coupling is ensured by the hydrodynamic model V.R.E.M.E.
(Violence of REaction of Energetic Materials).
V.R.E.M.E. is a two phases flow model composed of compressible solid and gas phases, combustion
inducing a mass transfer between phases. It requires, in addition to the usual conservation equations,
an equation describing the kinetics of reaction according to the thermal damage – reactivity relation.
The complete equation system requires for its closure an equation of state for each phase and laws of
mixture.
4.2.2
Thermodynamics
The hydrodynamic pressures of unreacted (s) and reacted (g) products are determined in terms of specific
volume and specific internal energy using JWL equations of state:
Ps = Ps (e s ,v s )
Pg = Pg (e g ,v g )
es , v s =
eg , v g =
specific volume and specific internal energy of the solid phase
Ps , Pg =
pressures of the solid and gaseous phases
specific volume and specific internal energy of the gaseous phase


ω  − R2V ωe
ω  − R1v
e
e
+
+ B 1 −
P = A 1 −
v
R
v
R
v
1 
2 


where A, B, R1 , R2 , ω depend on the composition.
The JWL coefficients of the solid phase are deduced from the Energetic material Hugoniot curve.
Given the chemical composition, the density and the enthalpy of formation of the explosive material,
the thermochemical TIGER code enables us to determine the CJ conditions and isentropic release of the
detonation products. The JWL coefficients of the gaseous phase are so established.
4.2.3
Laws of Mixture
Laws of mixture are necessary to fix a thermodynamical equilibrium:
•
The pressure is assumed to be in equilibrium between the two phases.
This assumption is justified because at the microscopic scale, during the combustion, the pressure gradient
disappears at once after some acoustic reflections between particles.
Ps = Pg
Ps
Pg
=
pressure of the solid phase
=
pressure of the gaseous phase
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•
We suppose adiabatic process within the solid phase:
des = − Pdv s
des
dv s
P
•
=
internal energy increment of the solid phase
=
internal volume increment of the solid phase
=
equilibrium pressure
Specific volume and specific internal energy of the two phases are supposed to be additive:
v = ∑ xi vi = (1 − λ)v s + λv g
i =1,2
e = ∑ xi ei = (1 − λ)e s + λe g
i =1,2
v
e
xi
λ
vi
ei
=
=
=
specific volume of the mixture
specific internal energy of the mixture
mass fraction of the phase i
=
=
mass fraction of gas
specific volume of the phase i
=
specific internal energy of the phase i
4.2.4
Surface Regression Models
Two models have been developed in Lagrangian and Eulerian formulations. The second one is more
effective in some cases involving large strains and mesh distorsions.
•
Surface regression in the Lagrangian Formulation
A geometrical method is used for propagating the burning front at a celerity according to the
porosity γ:
Vr (γ ) = a(γ ) P n (γ )
The reacted fraction λ results from the burning propagation model described above:
dλ
= αVr (γ )
dt
(where α is mesh dependant).
•
Surface regression in the Eulerian formulation
Most recently, a surface regression model based on the resolution of Hamilton-Jacobi equation has
been developed. We apply a level set formulation to the problem of surface advancement.
The level set formulation is based on solving a Hamilton-Jacobi type equation for a propagating level
set function, using techniques borrowed from hyperbolic conservation laws.
Given a moving closed hypersurface Γ(t ) , we wish to produce an Eulerian formulation for the
motion of the hypersurface propagating along its normal direction with celerity Vr (γ ) . The main idea
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is to embed this propagating interface as a level set of a higher dimensional function
φ ( φ (→x (t ), t ) = cte ), and thus:
→
∂φ ∂x →
+ ∇φ = 0
∂t ∂t
→
If the particle velocity is denoted u , we obtain:
→ →
∂φ → →
+ u . ∇ φ + Vr (γ ) n . ∇ φ = 0
∂t
→
Since the normal vector is given as n =
→
∇φ
→
, we have the evolution equation for φ :
∇φ
→
∂φ → →
+ u . ∇ φ = Vr (γ ) ∇φ
∂t
The main advantage of this formulation is that there are no significant changes to follow fronts from
two to three space dimensions. By simply extending the array structures and gradient operators,
propagating surfaces are easily handled.
4.2.5
Implementation in a Finite Element Solver
V.R.E.M.E. is implemented in the commercial software LS-DYNA that solves dynamic equations with an
explicit formulation. This finite elements code is currently used at SNPE Propulsion for solving non
linear phenomena: shocks, warhead functioning … Numerical simulations are performed on two and
three dimensional systems and lead to estimate pressurisation and expansion velocity of the confinement.
These informations are good indicators of the reaction violence.
5.0 APPLICATIONS
The purpose of this section is to illustrate our modelling capabilities for predicting both location of
ignition, time to event and reaction level of explosive scale model subjected to thermal threats.
5.1
Influence of the Heating Rate on Results
The first case deals with the behaviour of a small scale vehicle loaded with PBX explosive (PBHT, RDX).
ABAQUS charts are presented below. They point out heating rate influence on ignition location. At slow
heating rate (3.9 °C/h), ignition occurs at the centre, at 50 °C/h, it starts near end caps, and at fast heating
rate (360 °C/h), it occurs close to the tube.
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3.3 °C/h
50 °C/h
360 °C/h
Figure 10: Temperature Distributions in a Small Scale Vehicle submitted to Different Heating Rates –
Influence of the Heating Rate on the Ignition Locus – ABAQUS Software.
5.2
Reaction Violence Prediction of a Large Vehicle Charged with B2238/SNPE PBX
As an illustration of the methodology on large scale experiments, the test case below relates to a
Large Vehicle Cook-off Tube (8 litres generic vehicle) subjected to a fire (heating rate of 6 °C/mn).
This full-scale test vehicle is made of stainless steel confinement filled with B2238 SNPE PBX.
The prediction of the reaction violence of an ammunition subjected to a fire calls upon the methodology
developed and exposed previously.
The temperature distribution inside the vehicle is shown at two times just before the ignition occurs
(2140 and 2150 s):
NT11
VALUE
+2.06E+01
NT11
VALUE
+2.06E+01
+3.89E+01
+3.89E+01
+5.72E+01
+5.72E+01
+7.55E+01
+7.55E+01
+9.37E+01
+9.38E+01
+1.12E+02
+1.12E+02
+1.30E+02
+1.30E+02
+1.49E+02
+1.49E+02
+1.67E+02
+1.67E+02
+1.85E+02
+1.85E+02
+2.03E+02
+2.03E+02
+2.22E+02
+2.22E+02
+2.40E+02
+2.40E+02
+INFINITY
+2.42E+02
C
B
A
Figure 11: Temperature Distributions inside the Large Vehicle Cook-Off Test
charged with B2238/SNPE – ABAQUS Software.
It can be seen a spread in temperatures from 20 °C to 240 °C. Most notably, the simulation predicted the
ignition point to be located on the lateral surface near the confinement. Due to symmetrical considerations
(heating rate and tube design), the ignition occurs first near the midpoint of the tube.
Next figure shows a comparison of the temperature history plots along the (A-C) line. The ignition occurs
on the (A-B) line since temperature gradients are insignificant. The pyrotechnical event that follows
results from mechanical analysis.
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280
260
A
B
C
Temperature ( C)
240
220
200
180
160
140
1500
1600
1700
1800
1900
2000
2100
2200
Time (s)
Figure 12: Temperature History Plots on (A-C) Line.
In this case, the burning front propagates in an undamaged product with a burning rate law determined
through the high pressure closed vessel bomb. Calculation performed with LS-DYNA Lagrangian
formulation gives the vehicle cook-off tube expansion:
EUCLID WP2 : LARGE VEHICLE COOK-OFF TUBE
EUCLID WP2 : LARGE VEHICLE COOK-OFF TUBE
EUCLID WP2 : LARGE VEHICLE COOK-OFF TUBE
time = 0.00000E+00
time = 1.00000E+04
time = 2.22000E+04
Y
Z
Y
X
Z
Y
X
Z
LS-TAURUS 940.3 Feb99
T=0
X
LS-TAURUS 940.3 Feb99
T=10 m
LS-TAURUS 940.3 Feb99
T = 22 ms
Figure 13: Reaction Level Prediction – Mesh Distortions at Different Times – LS-DYNA Software.
The average velocity is about 2 m/s at 10 ms. At the same time, the pressure reached a maximum value of
1.7 Kbars and then shows a slope break. Its decrease is explained through thinning of the tube thickness
combined with the zero hardening modulus of the elasto-plastic steel model. That is, beyond yield stress,
the curves shapes are ruled by the hardening law of the confinement.
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EUCLID WP2 : LARGE VEHICLE COOK-OFF TUBE
EUCLID WP2 : LARGE VEHICLE COOK-OFF TUBE
1.70
4.25
1.60
4.00
1.50
3.75
3.50
1.40
3.25
1.30
3.00
1.20
2.75
1.10
2.50
1.00
2.25
0.90
2.00
0.80
pressure (Kbars)
x velocity (m/s)
1.75
1.50
1.25
1.00
0.75
Hardening law dependant
0.70
0.60
0.50
0.40
0.50
0.30
0.25
0.20
0.00
0.10
-0.25
time (ms)
minimum = -4.6843E-01
maximum = 4.3665E+00
time (ms)
node# 749
minimum = 1.0000E-03
maximum = 1.7078E+00
Figure 14: Ballistics Prediction –
Tube Velocity History.
28.00
26.00
24.00
22.00
20.00
18.00
16.00
14.00
12.00
8.00
10.00
6.00
4.00
2.00
0.00
32.00
30.00
28.00
26.00
24.00
22.00
20.00
18.00
16.00
14.00
12.00
8.00
10.00
6.00
4.00
2.00
0.00
0.00
shell element# 14
Figure 15: Pressure Raise in the Vehicle.
The failure is not modelled, but we can notice significant plastic strains after 17 ms. The confinement
early fails and the reaction is rapidly quenched, as experiments in the same heating rate conditions.
EUCLID WP2 : LARGE VEHICLE COOK-OFF TUBE
time = 1.70000E+04
fringes of eff. plastic strain
min= 0.000E+00 in element 1154
max= 3.241E-01 in element 673
ref. surface values for shells
fringe levels
0.000E+00 >
2.161E-02 >
4.321E-02 >
6.482E-02 >
8.643E-02 >
1.080E-01 >
1.296E-01 >
1.513E-01 >
1.729E-01 >
1.945E-01 >
2.161E-01 >
2.377E-01 >
2.593E-01 >
2.809E-01 >
3.025E-01 >
3.241E-01 >
Y
Z
X
LS-TAURUS 940.3 Feb99
Figure 16: Plastic Strains Contours Prediction.
5.3
Figure 17: Pressure Burst Observed
during Fast Cook-Off Conditions.
Results Accuracy
The accuracy of predictions are checked through data issued from trials. From a general point of view,
time to reaction and ignition temperature predictions are in a good agreement with the experiments since
variations do not exceed 10 %.
Because there are no experimental data available at this time, we have no indications how relevant the
results concerning the violence level are. Numerical tools may be possibly refined when precise
measurement devices recording tube expansion velocity will be available.
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6.0 CONCLUSIONS AND PROSPECTS
Due to the results of research undertaken on the knowledge of the energetic materials reaction
mechanisms, test methods and computer models have been developed for predicting the response of
generic explosive-loaded munitions to the perceived threats of Cook-off.
In this way, SNPE developed a methodology that integrates experimental and numerical tools.
The approach is macroscopic since global physical phenomena are characterised.
Numerical tools were improved and validated especially for thermochemical processes (self-heating and
pyrolysis phenomena). Further work should now concern the validation of the burning model by
performing instrumented trials quantifying precisely the violence of reaction. Future plans are to
incorporate physical components for simulating more violent events, particularly the Deflagration to
Detonation Transition.
Military requirements enabled the development of these innovative solutions. The first applications carried
out on an explosive load are promising. We are confident on the good behaviour of the method for systems
integrating other types of energetic materials.
7.0 REFERENCES
[1]
R. Kent and M. Rat, “Explosion Thermique des Propergols Solides”, Prop.Expl.Pyro. 7, 129-136
(1982).
[2]
J. Isler, “Combustion Mechanism of High Explosives and their Relation with DDT process”,
Prop.Expl.Pyro.16, 151-155 (1991).
[3]
J. Brunet and B. Salvetat, “Thermal Threat Behaviour of Solid Propellant Grains”, IM Tech. Symp.,
273-288 (June 1992).
[4]
B. Nouguez and J. Isler, “Insensitive Warhead Concept: SNPE Progress”, IM Tech. SYMP.,
99-11 (June 1992).
[5]
Y. Guengant, J. Isler and D. Houdusse, “Influence of Energetic Material Formulation on the
Reaction to Slow Cook-off”, International Symposium on Energetic Materials Technology, ADPA,
1994.
[6]
Y. Guengant, D. Houdusse and B. Briquet, “Knowledge and Prediction of Munitions Behaviour to
Slow Cook-off”, International Workshop on Measurement of Thermophysical and Ballistic
Properties of Energetic Materials, Politecnico di Milano, Italy, 1998.
[7]
Y. Guengant, D. Houdusse and B. Briquet, “Pyrolysis and Self-Heating Characterisations to Predict
the Munitions Responses to Slow Cook-Off Test”, Combustion and Detonation 30th International
Annual Conference of ICT, Karlsrue, 1999.
[8]
Y. Guengant, D. Houdusse, B. Briquet, “Pyrolysis and Self-Heating Characterisations to Predict the
Munitions Responses to Slow Cook-Off”, IM & EMTS, Tampa, November 1999.
[9]
W.W. Erikson, R.G. Schmitt, “Modeling and Analysis of Navy Cookoff Experiments”,
Sandia National Laboratories, IMEMTS 2001.
[10] R.G. Schmitt, T.A. Baer, “Millisecond Burning of Confined Energetic Materials during Cookoff”,
Sandia National Laboratories, 2001.
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[11] B.M. Dobratz, P.C. Crawford, LLNL Explosives Handbook, 1985.
[12] T.R. Gibbs, A. Popolato, LASL Explosive Properties Data.
[13] D. Desailly – Un modèle de prévision de la violence de réaction d’un matériau soumis à une
sollicitation thermique, introduction dans le code LS-DYNA en formulation lagrangienne,
V.R.E.M.E. version 1.0 – SNPE Propulsion, juillet 2001 – private communication.
[14] D. Desailly – Un modèle de prévision de la violence de réaction d’un matériau soumis à une
sollicitation thermique, introduction dans le code LS-DYNA en formulation Euler/Lagrange,
V.R.E.M.E. version 2.0 – SNPE Propulsion, juillet 2001 – private communication.
8.0 ACKNOWLEDGEMENT
This research program has been sponsored by DGA of the French Ministry of Defence and supported by
the EUCLID RTP2 project, a program performed collaboratively by six European countries involved in an
industrial consortium.
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SYMPOSIA DISCUSSION – PAPER NO: 19
Discusser’s Name: M.A. Bohn
Question:
In your presentation you have given a three-step consecutive chemical reaction model to describe the
chemical decomposition and the heat output of the chemical reaction. To what extent can such a simplified
model describe the real behavior and what different compositions can be described?
Author’s Name: David Desailly
Author’s Response:
To predict the response of pyrotechnical systems submitted to a thermal threat, we developed the
following three physical models for each reaction mechanism:
•
A procedure based on a three steps consecutive chemical reaction to predict self-heating
phenomenon. It is macroscopic since the ambition is not to model each chemical reaction that can
occur between all the constituents. This model has been tested and validated on PBX explosive
and AP based propellant. The results are accurate for these compositions since variations in the
time and temperature to ignition between calculation and test do not exceed 10 %.
•
A procedure to describe pyrolysis phenomenon.
•
A procedure to predict the reaction violence by implementing a burning model in LS-DYNA FE
solver. The response of pyrotechnical systems is assessed with this burning thermal damage
model.
Discusser’s Name: R. Derr
Question:
The work you describe here includes phenomenological models that are implemented with empirical data.
This approach for cook-off is embraced by the TTCP protocols now maintained by the NATO Insensitive
Munitions Information Center (NIMIC). Are you aware of these protocols and have you used them in your
work?
Author’s Name: David Desailly
Author’s Response:
SNPE representatives have participated in several NIMIC workshops. NIMIC’s contributions in the field
of predictive protocols and associated tests were appreciated (“Cookoff and XDT mechanisms”, 1996,
“IM Testing”, 1997, “Small scale testing and modeling”, 2000). The defined protocols give lists of
elementary physical & chemical phenomena that happen during vulnerability trials. To establish some
munitions predictive assessments, it is necessary to determine energetic material response for each
elementary phenomenon.
The SNPE paper describes small-scale tests and phenomenological models that have been developed to
establish the energetic material responses for each elementary phenomenon during Cook-off tests.
The SNPE approach is more physical than chemical, our goal was to develop pragmatic tools for
predicting events and reaction types; it is not to list each chemical degradation reaction.
Our tools need to be improved, especially for rocket motors, but first results are promising.
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