weapons eapons curvilinear trajectory and smart fuze fuze

WWeaponseapons Curvilinear trajectory and smart Curvilinear trajectory and smart fuzefuze calculations suitable for hard target defeat calculations suitable for hard target defeat

modelingmodeling

5252ndnd Annual Annual FuzeFuze ConferenceConference

Sparks, NV, USA, 13Sparks, NV, USA, 13--15 May 200815 May 2008

J-M Sibeaud Th. Lacaze

A. Lefrançois

Centre d’Etudes de Gramat BP 80200

F-46500 GRAMAT

Centre d ’Études de Gramat 52nd annual Fuze Conférence 13-15 May 2008 Slide N°2 / 19

MINISTÈRE DE LA DÉFENSE

CEGCEG’’ss mission mission

CEG is a Technical Center of the French MoD procurement Agency (DGA)

Bring better knowledge of Terminal effectiveness of Conventional Air-to-Ground weapons and missilesProvide support to program managers for new weapons development (SCALP/EG, AASM, MdCN, LRM NG)Provide Armée de l’air and Aéronavale (French Air Force and Navy Air Force) with means for determining effectiveness of strikes and perform mission planning

Capabilities against hardened targets (Hard target defeat)Improve warhead lethalityCapability to control weapon’s depth of burstMinimize collateral damage

Develop smart fuzing capabilities



ScheduleSchedule

Calpen3D, an analytical tool for smart fuzing

Validation against direct finite element simulationsValidation against experimentsSmart fuzing use



CalPenCalPen 3D : basic 3D : basic theorytheory of of curvilinearcurvilinear penetrationpenetration

Penetration module of PLEIADES/I – the Vulnerability / Lethality code currently in operation within the French MoD

Rigid body kinematics (Based on DAFL and SCE theory)Curvilinear trajectory

Hard and soft targets formulationsStandard ConcreteHigh resistance concreteReinforcementSoils (Wayne Young’s S number)

OutputResidual post perforation velocity and attitude …



Enhanced perforation algorithmEnhanced perforation algorithm

Perforation modeled with Interface Proximity Index (IPI)

IPI value based on user experienceEnhanced algorithm based on

differential treatment of ogive and afterbobyOgive IPI is slab thickness dependant

Goal : Predict accurately Projectile velocity andorientation

Projec

tile

Concrete slab



ScheduleSchedule

calpen3D, an analytical tool for smart fuzing

Validation against direct finite element simulations

Validation against experimentsSmart fuzing use



Validation against numerical simulationValidation against numerical simulation

-1.0

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OURANOS Finite Element Analysis

CALPEN3D enhanced algorithm

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Velo

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CALPEN3D Enhanced algorithm

260 m/s

Concrete thickness = 1.3 Cal.

Air thickness = 13 Cal.

( 4-slab)

Reference data provided by fully validated Ouranos FE calculations



ValidationValidation

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OURANOS Finite Element analysis


340 m/s

AOI = 30° CRH = 3 Slab thickness = 6.4 Cal.



Validation against numerical simulationValidation against numerical simulation

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OURANOS Finite Element Annalysis


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CALPEN3D Enhanced Algorithm

260 m/s

AOI = 60° CRH = 3 Slab thickness = 2.6 Cal.

ISIEMS sept 2007 in Orlando paper for full description



ScheduleSchedule

Calpen3D, an analytical tool for smart fuzingValidation against direct finite element simulations

Validation against experiments

Smart fuzing use



Validation against experimentsValidation against experimentsPre-damaged concrete target

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CalPen3D

Experiment 184

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Experiment 184

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CalPen3D

Experiment 181

In situ G load recorder

Large pre-damage => perforation complete Small pre-damage => perforation not complete

Siami sensor provided by T2M



Validation in more complex configurations resulting from Validation in more complex configurations resulting from vertical attacks against real building: the corner effectvertical attacks against real building: the corner effect

0.4 m

0.3 m

V

Vertical axis

AOI

AOA

Projectile axisd

Configuration V (m/s) AOI (°) AOA (°) d (m)T5-c 359 41 0 0.5 T5-d 349 29.7 +1.5 1.09

Configurations of impact representative of nearly vertical attacks of multi-story buildingsVertical wall next after horizontal slabPerform CalPen3D runs on these configurations

D = 160 mmL = 960 mm Mass = 79.5 kg

Reference : model scale experiments (scale .7) performed in CEA/DAM test site, France, reported by E. Buzaud CEG (see paper published in ISIEMS 11 in Germany (2003)

high speed cameras (HYCAM, 8000 f/s or 16000 f/s)AOI, AOATri-axis acceleration recording at projectile CoGScabbing process (HYCAM, 8000 f/s)Residual damage topography

Before impact

T5-C T5-D



t = 0 ms V = 359 m/s

t = 1.9 ms V = 297 m/s

t = 5.9 ms V = 210 m/s

t = 10 ms V = 210 m/s

T5-C

t = 1.9 ms V = 290 m/s

t = 2.05 ms V = 290 m/s

t = 5.9 ms V = 217 m/s

Experiment T5-C CalPen3D Simulation T5-C

Experiment T5Experiment T5--CC 359 m/s AOI 41° AOA 0° d 0.5

Projectile perforates both slabs as observed during the experimentVelocity and deviation of projectile wrt the original line of fire is accurately predicted-4

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T5-C CALPEN3D Analytical modeling - Initial versionExperimentCALPEN3D Enhanced algorithm



Experiment T5Experiment T5--DD 349 m/s AOI 29.7° AOA +1.5° d 1.09 m

t = 0 ms V = 349 m/s

t = 1.4 ms V = 306 m/s

t = 8.2 ms V = 284 m/s

t = 10 ms V = 252 m/s

t = 15 ms V = 229 m/s

T5-D

t = 1.4 ms V = 290 m/s

t = 9.4 ms V = 230 m/s

t = 7.7 ms V = 290 m/s

Experiment T5-D CalPen3D Simulation T5-D

Ricochet on vertical wall and velocity appear well predicted

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CALPEN3D Analytical modeling - Initial versionExperimentCALPEN3D Enhanced algorithm

T5-D



ScheduleSchedule

calpen3D, an analytical tool for smart fuzingValidation against direct finite element simulations

Validation against experiments

Smart fuzing use



Smart Smart fuzingfuzing

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Z (m

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Case#1-configuration#1: triaxial nose and tail sensing

Case#2-configuration#1: triaxial nose and tail sensing

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aFx: Radial acceleration at F

aFz: Axial acceleration at F

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aTx: Radial acceleration at T

aTz: Axial acceleration at T

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aFz: Axial acceleration at F

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aTx: Radial acceleration at T

aTz: Axial acceleration at T

Case 1 Case 2

Case 2 : T and F sensors output

Case 1 : T and F sensors output

Full knowledge of projectiles paths into the

target from embedded sensors output provided that initial attitudes and

velocities are known

aFx

F

T

z

x

F: Rearward located accelerometers

Impact point: {X;Z}={0;0}

Target

Z

X

T: Forward located accelerometers

aFz

aTx

aTz

Assuming : projectile not spinning, plane trajectory in (X,Z) = (z,x)

SoilConcrete

Concrete

Concrete



One single GOne single G--load recording in the projectile axisload recording in the projectile axis

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Case#1-configuration#1: triaxial nose and tail sensingCase#1-configuration#2: uniaxial tail sensingCase#2-configuration#1: triaxial nose and tail sensingCase#2-configuration#2: uniaxial tail sensing

Algorithms for smart fuzing may be based on void sensing and/or slab counting. Initial projectile velocity and attitude must be known upon impact (knowledge of this information can be obtained from the guided munition inertial plateform) Limitation : 1) projectile rotation – and therefore ricochet - cannot be detected 2) deceleration signature when perforating the soil is similar to the one observed during ricochet

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OtherOther limitation of limitation of voidvoid sensingsensing

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Vertical and horizontal slabs cannot be differentiated

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Summary and ConclusionSummary and Conclusion

Modelling of hard target defeat requires that penetration calculations are performed

Improving warhead lethality while improving control on weapon’s depth of burst requires embedded smart logic

Paper relates on how making use of CalPen3D to assess smart fuzing algorithms in various representative situations (4 slabs, semi-infinite, pre-damaged targets, corner effects)

Further effort on real world dataReal time kinematics using appropriate filtering and DSP3D signals (Euler angles)Non perfectly rigid projectile

weapons eapons curvilinear trajectory and smart fuze fuze

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