Leveraging physics-based simulations for Operational Analysis · ballistics sonar radar Underwater...
Transcript of Leveraging physics-based simulations for Operational Analysis · ballistics sonar radar Underwater...
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© Her Majesty the Queen in Right of Canada (Department of National Defence), 2019
Dr. Peter Young CORA / CFMWC ORT
Presentation to the 36th ISMOR 23 – 26 July 2019
Leveraging physics-based simulations for Operational Analysis: Task Group Air Defence Case Study
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Background: OA tasking to study Task Group Air Defence
Late 2017 the Canadian Forces Maritime Warfare Centre (CFMWC) received an Operational Analysis (OA) tasking to study naval Task Group (TG) Air Defence (AD)
CFMWC stakeholders:
Above Water Battlespace (AWB): expertise in Anti-Air Warfare (AAW) weapon systems, tactics and doctrine
Modelling and Simulation (M&S): provision of computing infrastructure and M&S expertise supporting the CFMWC battlespaces
Operational Research Team (ORT): 3 defence scientists supporting AWB, Underwater Battlespace (UWB) and Joint Technology and Innovation Battlespace (JITB)
This presentations provides an overview of how physics-based simulations employed by M&S to support AWB are being used to underpin the TG AD OA study
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Acknowledgements to the officers and staff of CFMWC AWB, M&S and ORT for their contributions through the TG AD study
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Presentation Overview
Overview of CFMWC modelling and simulation capability
Modelling capability
Computing infrastructure
Case Study: Task Group Air Defence
Study design considerations
Analysis approach
MEZ construction using physics-based simulations
Ship stationing analysis with illustrative results
Summary with observations
Concluding remarks
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All data and examples shown are for illustrative purposes only and do not reflect actual systems.
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CFMWC modelling and simulation capability
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fidelity
low
high
abstraction
high
low
tactical
system
subsystem
operational
strategic
one vs. one
few vs. few
one vs. few
many vs. many
force on force
propagation
radar ballistics sonar
Underwater Warfare
Campaign
Joint
Task Group
physics-based Monte Carlo simulation
acoustics fly-out 3 DOF 6 DOF
{
Gunnery
Air Defence
platforms/weapons sensors
C2
atmosphere
EM
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CFMWC computing infrastructure
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syndicate rooms briefing/training theatre client
workstations
servers
computing cluster
Network Configuration
databases
gateway
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fidelity
low
high
abstraction
high
low
tactical
system
subsystem
operational
strategic
one vs. one
few vs. few
one vs. few
many vs. many
force on force
propagation
radarballistics sonar
UnderwaterWarfare
Campaign
Joint
Task Group
physics-basedMonte Carlosimulation
acousticsflyout3 DOF6 DOF
{Gunnery
Air Defence
platforms/weapons sensors
C2
atmosphere
EM
Industrialised M&S supporting the Royal Canadian Navy
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Applications: Concept Development & Experimentation (CD&E)
Training
Tactics development
Doctrine development
Operational Test & Evaluation (OT&E)
Planning
Post-trial reconstruction and analysis
Requirements development & verification
Operational Analysis
syndicate roomsbriefing/training theatre
servers
computing cluster
Network Configuration
databases
gateway
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storage processing
servers
High Throughput Computing (HTC) for M&S
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1. model & data deployment
3. data parsing & storage
2. model execution
4. result queries
1000+ processors
HTC enables Monte Carlo simulation as a viable means for exploring large parameter spaces using high fidelity models
2+ Petabytes
Parameter space dimensionality, run-time speeds and data management still provide limitations
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Case Study: Naval Task Group Air Defence
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Air threat • Fighter Bomber • Anti-Ship Missile (ASM)
Air Defences • Long Range (LR) Surface-to Air Missile (SAM) • Short Range (SR) SAM • Naval Gun
High Value Unit (HVU)
Air Defence (AD) Ships
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Study design considerations
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Threat presentation • Threat type and number • Main threat axis • Spacing and timing about threat axis
Task Group • Numbers and types of ships • AD ship capabilities • C3I systems • Mission • Threat assessment • Firing policies • Ship stationing Atmospheric conditions
• Temperature/Pressure • Ducting • Wind • Water surface
Use physics-based simulations to perform engagement assessments
Use high level “OA” approach to analyse TG configurations
AD Ship • Search radar • Tracking radar • SAM • Seeker • Propulsion • Flight dynamics •Warhead
Threat • Seeker • Flight dynamics • Signature • Vulnerability
System /subsystem aspects Scenario / study aspects
fidelity high
abstraction high
Key metric: SAM expenditure to counter the threat raid (expressed as cost)
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Problem parameter space
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Number of runs: NR = NC x NMC ≈ 45x109
11x21
Grid box for ship: 21x21 21x21
Number of TG configurations: NTG = (11 x 21) x (21 x 21) x (21 x 21) ≈ 45x10
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Number of threat axes: NTA = 20
Number of threats: NT = 4
Number of ships: NS = 3
Excessive, even with HTC Number of engagements: NE = NT x NR ≈ 18x10
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AD ship HVU threat
Number of cases: NC = NTG x NTA ≈ 9x108
Number of firing policies: NFP = ?
PK = 0.7
PS = 0.3 Monte Carlo runs per case: NMC = 50
AD Ship configuration: radar, C3, LR SAM, SR SAM Threat: Sub/supersonic, manoeuvring/non-man, seeker
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Analysis approach
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2.a Employ MEZs in a lookup manner to determine optimal firing policies
Coordinated AD: shoot1 – look –shoot2
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ship1 ship2
2.b Conduct ship stationing analysis 3. Cross-validate results with and perform focused runs using high fidelity simulations
model execution
cross-validation
focused runs
1. Use high fidelity simulations to construct Missile Engagement Zones (MEZs)
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results
HVU
MEZ
Air Defence Model 6 DOF Missile Model
Air Defence Model 6 DOF Missile Model
Commercial Monte Carlo simulation with integrated high fidelity models from DRDC and Allied partners
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Step 1. MEZ construction using physics-based simulation
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S1 initiate firing
S1 launch
S1 intercept
S1 assessment
timeline threat
velocity v
time to initialise
fly-out time
time for assessment
Descretised MEZ
cross-range
down- range
Grid box for MEZ: 41x61
Number of grid pts: NG = 21 x 61 = 1,281
model execution
LR & SR MEZs • 1 to 2 days for model execution • 1 week turn-around, including data
parsing and post-analysis
Monte Carlo runs per case: NMC = 50
Number of runs per threat type: N = NG x NSAM x NMC = 128,100 *
Number of SAM (LR & SR): NSAM = 2
Air Defence Model 6 DOF Missile Model
HVU
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* Plus other factors: e.g. threat flight profile and ducting height
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Step 2. Ship stationing analysis
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Inputs • SAM – threat MEZs •Number of AD ships •Number of threats • Range of TG configurations • Range of threat axes • Threat axis assumptions • Firing doctrine constraints • AD coordination
Functionality • 2.a For each threat axis/TG configuration,
determine optimal firing policy to achieve a desired PK against each threat whilst minimising SAM expenditure • 2.b For each threat axis assumption,
determine the TG configuration that minimises mean missile expenditure
Outputs •Optimal firing policy for each
threat axis/TG configuration •Optimal TG configuration for each
threat axis assumption
Ship Stationing Model
MEZs TG threats
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0.05
0.1
-90 -60 -30 0 30 60 90
Pro
bab
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Target Bearing
0
0.2
0.4
0.6
-90 -60 -30 0 30 60 90
Pro
bab
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Target Bearing
threat axis assumptions
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0.05
0.1
-90 -60 -30 0 30 60 90
Pro
bab
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Target Bearing
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0.2
0.4
0.6
-90 -60 -30 0 30 60 90
Pro
bab
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Target Bearing
aggregation
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2 3 1
2 3 4
Cmn = 5.8 Cmn = 6.2
FP: T1: S1S1 T2: S1S1 T3: S2LS1 T4: S2LS1 C = 6.6 PK = 0.9
2.a 2.b
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SSM showing illustrative MEZ for a crossing target
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MEZ captures key aspects of a complete single shooter – single target engagement • Detection by a search radar • Tracking by a fire control radar • Launch and fly-out of the SAM
with associated fly-out time • Intercept of the SAM with the
target with resultant single-shot PK
Launch points Intercept points
MEZ dependencies • Target type and flight profile
(height & velocity) • Search and tracking radar
performance • SAM guidance and flight
capabilities • SAM warhead effectiveness against
the target • Environment conditions (duct
height, atmosphere)
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2.a Determination of optimal firing policies
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Meshed engagement cells
threat
Ship 1 LR MEZ
Tree of permissible firing policies
Ship 2 LR MEZ
Ship 1 SR MEZ
Ship 2 SR MEZ
1. consider each cell for 1st shot
1.a. recursively, consider each cell and salvo size following assessment time
1.b. compute engagement timeline, PK, cost and add to possible firing policies
1.c. consider further shots if sufficient time
assessment time
Ship positioning, threat placement
S11 L S24 PK , Cost
earliest launch time for follow-on shot
cells ordered in increasing time
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HVU
launch
intercept Given 1st shot
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Evaluation of firing policy tree to determine the Pareto Efficient Boundary (PEB) for PK vs. Cost
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232876 nodes (firing policies)
138027 nodes
2792 nodes
Complete tree Leaf pruning Local pruning
Pareto dominance condition applied during tree construction to prune leaf nodes
PEB determined after construction of complete tree
Pareto dominance condition applied locally during tree construction to prune branch nodes
Methodology • Construct firing policy tree
• Apply Pareto dominance condition at local branch level to confine tree growth
• Parse tree to determine PEB • Recursive algorithm to identify dominant firing policies
Graphs show Pareto Efficient Boundaries (PEB) for illustrative results, where solution points below and to the right are dominated by solutions on the boundary
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Optimal firing policy to achieve a desired PK : Solution for TG configuration 1
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0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1 2 3 4 5 6 7 8
Kill
Pro
bab
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Mean Cost
bearing: 00
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1 2 3 4 5 6 7 8
Kill
Pro
bab
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Mean Cost
bearing: 0
bearing: 10
bearing: 20
bearing: 30
0
0
0
0
required PKTOT = 0.95
resulting cost C = 0.941 firing policy s2Ls2Ls1s1
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0.5
1
1.5
2
2.5
3
3.5
0 10 20 30 40 50 60 70 80 90
Me
an C
ost
Target Bearing
(0,1),(0,8)
TG configuration 1 S1: (0,1), S2: (0,8)
300
C = 3.166 S1S1S1Ls1s1
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C = 1.420 s2LS1Ls1s1
target bearing
1
2
1
(S2 1xSR) Look (S2 1xSR) Look (S1 2xSR)
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Optimal firing policy to achieve a desired PK: Solution for TG configuration 2
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0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1 2 3 4 5 6 7 8
Kill
Pro
bab
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Mean Cost
bearing: 00
required PKTOT = 0.95
resulting cost C = 3.166 firing policy S2S2S2Ls2s2
0
0.5
1
1.5
2
2.5
3
3.5
0 10 20 30 40 50 60 70 80 90
Me
an C
ost
Target Bearing
(-1,1),(1,1)
(0,1),(0,8)200
C = 3.082 S2S2LS2S2S2S2
300
C = 1.719 S2Ls2s2LS2S2
TG configuration 2 S1: (-1,1), S2: (1,1)
target bearing
1 2
1
(S2 3xLR) Look (S2 2xSR)
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SSM showing illustrative threat raid engagement
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Metrics for main threat axis of -200
Launch and missile support schedule
PEB for T1 Cost as function of main threat axis angle
SSM can rapidly assess a single raid presentation (approx. 0.3 s for this scenario)
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2.b Assessing TG configurations
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weighted mean cost •narrow: Cnarrow = 3.10 •wide: Cwide = 2.44
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0.2
0.4
0.6
-90 -60 -30 0 30 60 90
Pro
bab
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Target Bearing
“narrow”
0
0.05
0.1
-90 -60 -30 0 30 60 90
Pro
bab
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Target Bearing
“wide”
1 2
1
0
0.5
1
1.5
2
2.5
3
3.5
0 10 20 30 40 50 60 70 80 90
Me
an C
ost
Target Bearing
(0,1),(0,8)
weighted mean cost •narrow: Cnarrow = 1.34 •wide: Cwide = 2.53
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0.2
0.4
0.6
-90 -60 -30 0 30 60 90
Pro
bab
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Target Bearing
“narrow”
0
0.05
0.1
-90 -60 -30 0 30 60 90
Pro
bab
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Target Bearing
“wide”
1
2
1
TG conf. 2
TG conf. 1
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0.5
1
1.5
2
2.5
3
3.5
0 10 20 30 40 50 60 70 80 90
Me
an C
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Target Bearing
(-1,1),(1,1)
(0,1),(0,8)
preferred TG configuration given threat axis assumption
threat axis assumptions
cost – target bearing results
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Illustrative SSM results for 3 TG configurations
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1_188 17_409 22_273
Threat Axis Distributions (TAD)
TAD 1
“narrow”
TAD 2
“wide”
TAD 3
“flat”
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2.b Determining the optimal TG configuration to minimise missile expenditure
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• Heat maps used to identify optimal ship locations given assumptions on the main threat axis
• Permits what-if analysis, e.g. if one ship’s location is fixed • Production requires extensive number of TG
configurations to be considered
TAD 1
“narrow”
TAD 2
“wide”
TAD 3
“flat”
What-if analysis: Revised heat map given S1‘s location fixed, TAD 1
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Summary: TG AD study 1. MEZ construction using physics-based simulations
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storage processing
servers
1. model & data deployment
3. data storage
2. model execution
1000+ processors
4. result queries for MEZ construction
MEZs
• 1 to 2 days for HTC model execution • 1 week turn-around, including data
parsing and post-analysis
Air Defence Model 6 DOF Missile Model
Each MEZ captures radar detection, tracking, SAM fly-out, intercept and endgame aspects of an engagement as represented in the physics-based simulations
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storage processing
servers
1. model & data deployment
3. data storage
2. model execution
1000+ processors
Ship Stationing Model Command Line Interface
Summary: TG AD study 2. TG ship stationing analysis using MEZs
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FP: T1: S1S1 T2: S1S1 T3: S2LS1 T4: S2LS1 C = 6.6 PK = 0.9
1
2 3 1
2 3 4
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MEZs
TG configuration results
• 1 hour for HTC model execution of 105 configurations • 1 day turn-around for data storage and post-analysis
HTC permits extensive search of parameter spaces to support optimisation problems
Number of TG configurations: NTG = (11 x 21) x (21 x 21) x (21 x 21) ≈ 45x10
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Summary: TG AD study 3. TG aggregated results
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storage processing
servers
model execution
1000+ processors
TG configuration results
heat maps
Ship Stationing Model Graphical User Interface
FP: T1: S1S1 T2: S1S1 T3: S2LS1 T4: S2LS1 C = 6.6 PK = 0.9
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2 3 1
2 3 4
0
0.05
0.1
-90 -60 -30 0 30 60 90
Pro
bab
ility
Target Bearing
0
0.2
0.4
0.6
-90 -60 -30 0 30 60 90
Pro
bab
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Target Bearing
Cmn = 5.8 Cmn = 6.2
optimal TG configurations
threat axis assumptions
A simplified model, layered over and underpinned by physics-based simulations, can extend analysis to larger parameter spaces
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Concluding remarks (1 of 2)
Two layered approach, OA model underpinned by physics-based simulations, permits exploration of a large parameter space in a structured manner
Analysis benefits being realised through HTC
Permits high fidelity Monte Carlo simulations to be used in a wider context
Permits brute-force extensive search of large parameter spaces, but parameter space increases can still outgrow HTC capacity
Data management challenges arise from the vast amounts of data that can be rapidly generated
M&S fully embraced the approach to construct MEZs
Gives firm basis and credibility to the ship stationing analysis
Approach adopted to support planning for an upcoming trial
Exploring feasibility of peeling back the layers in a MEZ to separate out detection, tracking and SAM fly-out
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Concluding remarks (2 of 2)
OA model observations and benefits
In-house development responsive to evolving requirements
Fast running thereby permitting large number of cases to be quickly considered
Push to increase functionality:
Visualisation of MEZs from the physics-based simulations
Command Line Interface to utilise HTC, Graphical User Interface to visualise results
Data output control to minimise amount of data produced – better to quickly run large number of cases with minimal data recording and then rerun the cases of interest
Heat maps for visualising TG ship stationing results
Further work:
Validation and focused runs using physics-based simulations
Increase complexity of engagements represented
Explore exploitation of the engagement planner
Explore exploitation of the heat maps to support tactical planning
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Questions ?
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© Her Majesty the Queen in Right of Canada (Department of National Defence), 2019