Mine Burial Expert System for Change of MIW Doctrine

Transcription

1 Mine Burial Expert System for Change of MIW Doctrine Ronald Betsch, Peter Fleischer NAVOCEANO Chris Beuligmann, Peter C. Chu NPS 10 th International Mine Warfare Technology Symposium May 7 10, Embassy Suites, Monterey, California

2 Introduction Mine Burial Expert System (MBES) developed by JHU APL Transition to NAVO complete in 2007 Integration with Environmental Post Mission Analysis (EPMA) tool ongoing (scour) MBES greatly exceeds current prediction capabilities Analysis of system capabilities needed to bridge gap between current doctrine and new prediction system

3 Motivation for Analysis NAVOCEANO MIW DATABASES: SEDIMENT TYPE MINE BURIAL % ROUGHNESS DOCTRINE A track in the red area takes 3.5 times (per unit area) longer to clear than a track in the green area. CLUTTER DENSITY From Paul Elmore (NRL)

4 Motivation (cont.) 50% width and variable 10 bins 55% width and fixed 5 bins

5 Motivation (cont.) How should MBES prediction be used operationally? How many bins will effectively capture improved prediction resolution? How can MBES prediction be used to give risk analysis data to MCM Commanders? What impact will the MBES have on current doctrine and doctrinal bottom types?

6 Background

7 Impact Burial Modeling of impact burial began in 1970s with the Impact Burial and Prediction Model (IBPM) IBPM modeled three phases 1. Air 2. Water 3. Sediment From Chu et al. (2000)

8 Impact Burial Models IBPM (previous slide) Deterministic One dimensional (x) IMPACT25/28 Deterministic From Chu (2009) 2 D (x and z) 25 written in Basic, 28 written in MATLAB Evaluated by previous students in Monterey Bay Model consistently over predicted burial IMPACT28 foundation for the MBES

9 Impact Burial Models (cont.) IMPACT35 Deterministic 3 D (x,y,and z) First to include mine shapes other than cylinders Very robust and accurate All the impact models are deterministic From Chu (2009)

10 Scour Burial First wave induced prediction model developed in the 1960s by U.S. Navy Wave Induced Spreadsheet Prediction (WISSP) Nbury developed by German Navy in 1980s Latest scour model built in U.K. in 1990s Defense Research Agency Mine Burial Environment (DRAMBUIE)

11 Scour Burial (cont.)

12 Model Sensitivities Controlling factors for impact burial Sediment shear strength *** (Taber) Water depth Mine type Initial drop angle Controlling factors for scour burial Magnitude of near bed current velocity *** Function of wave height and period Water depth Sediment grain size Also depends on ripples

13 Bayesian Network

14 MBES Expert System Physics based models Observational data Expert knowledge Bayesian Network Causal Stochastic! Product rule Bayes theorem (alternate form) Sum rule P( A) P( A) 1 PA ( B) PBA ( ) PA ( ) P( B ) P( B A, C ) P( A) P( C ). k k i j i j i j

15 Notional Bayesian network consisting of two parent nodes and one child node, along with the conditional probability table (CPT) (from Rennie et al. 2007)

16 (From Rennie et al. 2007)

17 Formation of CPT from a histogram of process model results (Rennie et al. 2007)

18 MBES (cont.) The MBES provides state of the art mine burial prediction capability to U.S. Navy Validated using IMPACT28 model and variety of laboratory and field experiments Cylinder mine shapes

19 MBES Nodes Angle of Release Nosedown Tilted Horizontal BRM StoneFish NRL MK56 MK36nBomb Shal8 16 Int16 24m Deep ± 28 MineType ± 1.1 Water Depth ± 6.9 Sediment Impact Angle Nosedown to30 to45 to60 to75 Horizontal Sediment Impact Velocity VerySlow Slow Medium Fast VeryFast ± ± 0.97 R 0 10 R R R R R R R R R Area End State ± 28 Shear Strength VSoft0 2 Soft2 4 MSoft4 6 Med6 9 MStiff9 12 Stiff12 16 Hard16 30 Rock Input PDTs CPTs Output PDT 16.1 ± 21

20 Analysis

21 Method of Analysis Goal was to qualify binning scheme more effective than current doctrinal categories Change inputs (*.dne text file) to create realistic scenarios Run model with specified inputs Collect output distributions Plot MBES predictions to find necessary bin requirements (MATLAB)

22 Method of Analysis (cont.) 1. Unknown mine type a. Completely unknown inputs b. Known water depth c. Known sediment SS distribution d. Known water depth and SS distribution Reasoning Baseline measurement Current EPMA setup Complex sediment types

23 Method of Analysis (cont.) 2. Known mine type a. sand b. silt c. clay Reasoning INTEL, uncertainty captured in input PDT Input not available to current EPMA users Not in database does not mean not available

24 Unknown Mine Type

25 Unknown Mine Type Figure No. Scenario Mean burial (μ) Standard Deviation (σ) 10 All parameters unknown Shallow depth (8-16 m) Intermediate depth (16-24 m) Deep depth (24-36 m) Sand Silt Clay Sand SS distribution, Shallow depth Sand SS distribution, Inter. depth Sand SS distribution, Deep depth Silt SS distribution, Shallow depth Silt SS distribution, Inter. depth Silt SS distribution, Deep depth Clay SS distribution, Shallow depth Clay SS distribution, Inter. depth Clay SS distribution, Deep depth 74 20

26 Unknown Mine Results No new information gained Results seem to qualify original design of doctrinal categories Rock Cat 0 Sand Cat 1 or 2 (little burial) Silt Cat 3 (most difficult to predict) Clay Cat 4 (most burial)

27 Unknown Mine Results

28 Complex Sediment Types NAVO database contains over 400 enhanced sediment types Do all types qualify current doctrinal scheme? Category 3 is biggest problem with current doctrine Best sediments to test Cat 3 are silty clay and clayey silt

29 Complex Sediment Types

30 Known Mine Type

31 Mine type Length (m) Maximum diameter (m) Weight in air (kg) Center of Gravity offset BRM (FWG Burial Recording Mine) % Stonefish Exercise Mine % NRL MK56 Instrumented Mine % MK36n Destructor Mine %

32 Known Mine Type in Sand

33 Known Mine Type in Silt

34 Known Mine Type in Clay

35 Conclusions

36 Risk Parameterization Discussed in technical document for the MBES

37 Risk Parameterization

38 Conclusions All ten bins needed Need more model data in the MBES to account for mine types other than cylinders IMPACT35 The MBES in EPMA should allow user to input mine type Create link between EPMA and INTEL Use risk parameterization

39 Questions From Naval Historical Center