International Workshop 28 Jan – 2 Feb 2011 Phoenix, AZ, USA Modeling & Simulation Interoperability (MSI) Challenge Team INCOSE MBSE Initiative http: //www. omgwiki. org/MBSE/doku. php? id=mbse: modsim Team Update @ MBSE Workshop Speakers: Russell Peak and Dirk Zwemer 30 January 2011 1
Modeling & Simulation Interoperability Team (MSI) Team Objectives • Overall Objective: Advance how models interact together throughout the system lifecycle • Key Sub-Objective: Better interconnect system specification & design models with diverse engineering analysis and simulation models – Ex. Interconnecting Sys. ML-based system models with traditional models: CAD, CAE, reliability, costing, programmatics, PLM, . . . 2
Modeling & Simulation Interoperability Team (MSI) Team Members http: //www. omgwiki. org/MBSE/doku. php? id=mbse: modsim 3
Modeling & Simulation Interoperability Team (MSI) New Members & New Collaborations in 2010 -2011 • Jeffery Banks (Northrop Grumman) – Sys. ML parametrics modeling & simulation for information systems using Rhapsody/Melody • Bruce Beihoff (Whirlpool) – Sys. ML applications for physics-based modeling • Dirk Zwemer (Inter. CAX) – Sys. ML parametrics applications (smart grid, supply chains, . . . ) • Challenge Teams: Space Systems, Smart Grid – Sys. ML interoperability with orbit simulation (AGI/STK) – Sys. ML parametrics-bsaed smart grid model • Sandia – Sys. ML interoperability with embedded systems simulation (Orchestra) • Systems Engineering Research Center (SERC) UARC – RT 21 VV&A project, RT 24 Integrated M&S/Do. DAF project 4
Contents • Sys. ML parametrics advances 2010 -2011 – – – 5 -minute primer: fuel tank Advanced modeling constructs: complex aggregates Debugging and visualization: DNA signatures Scalability testing & metrics Expanding applications • Smart grid modeling – D Zwemer (Inter. CAX) • Information systems modeling – J Banks (NGC), Fire. SAT, biomedical, VV&A, . . . – Sys. ML-LVC simulation interoperability example: STK – Expanding tool support and deployment • • Embedded systems simulation applications (with Sandia) Additional team progress – – – MBSE & manufacturing – Sys. ML & DEVS – Mc. Ginnis et al. Sys. ML and optimization with Model. Center – Paredis et al. Sys. ML-Modelica transformation spec – Paredis et al. SERC RT 21 Verification, Validation, and Accreditation project (VV&A) Growing education opportunities (short courses, undergrad/grad courses, . . . ) 5
Sys. ML Parametrics Primer: Fuel_Tank block & instances on block definition diagram (bdd), parametrics diagram (par) ft 310 gauge 10. 2 gal Sys. ML parametrics diagram Capturing equation-based knowledge ft 330 gauge 5. 5 gal Copyright © Georgia Tech and Inter. CAX. All Rights Reserved. Sys. ML and MBSE: A Quick-Start Course 6
Fuel_Tank parametrics execution Para. Magic interoperating w/ equation solvers such as Mathematica instance ft 330 state 1. 0 (before solving) Given my current_amount, how full is my tank? state 1. 0 (before solving) instance ft 330 state 1. 1 (after solving) Copyright © Georgia Tech and Inter. CAX. All Rights Reserved. Sys. ML and MBSE: A Quick-Start Course 7
Fuel_Tank parametrics execution Changing input/output direction (causality) in the same instance ft 330 state 2. 0 (after changing causalities, and before solving) What current_amount will give me a tank that is half full? state 2. 1 (after solving) instance ft 330 state 2. 1 (after solving) Copyright © Georgia Tech and Inter. CAX. All Rights Reserved. Sys. ML and MBSE: A Quick-Start Course 8
Fuel_Tank “DNA signature” Interacting with equation graph structure via Panorama tool Model DNA Signature of instance ft 330 (flattened equation structure auto-generated from Sys. ML) Copyright © Georgia Tech and Inter. CAX. All Rights Reserved. Sys. ML and MBSE: A Quick-Start Course 9
Exercise 0: Automobile Fuel Capacity & Mileage Stage 3 Model (p 1/3) Copyright © Georgia Tech and Inter. CAX. All Rights Reserved. Sys. ML and MBSE: A Quick-Start Course 10
Exercise 0: Automobile Fuel Capacity & Mileage Stage 3 Model (p 2/3) Example Instances (after solving) Model DNA Signature Copyright © Georgia Tech and Inter. CAX. All Rights Reserved. Sys. ML and MBSE: A Quick-Start Course 11
Exercise 0: Automobile Fuel Capacity & Mileage Stage 3 Model (p 3/3) state 1. 1 (after solving) Copyright © Georgia Tech and Inter. CAX. All Rights Reserved. Sys. ML and MBSE: A Quick-Start Course 12
Contents • Sys. ML parametrics advances 2010 -2011 – – – 5 -minute primer: fuel tank Advanced modeling constructs: complex aggregates Debugging and visualization: DNA signatures Scalability testing & metrics Expanding applications • Smart grid modeling – D Zwemer (Inter. CAX) • Information systems modeling – J Banks (NGC), Fire. SAT, biomedical, VV&A, . . . – Sys. ML-LVC simulation interoperability example: STK – Expanding tool support and deployment • Additional team progress – – – MBSE & manufacturing – Sys. ML & DEVS – Mc. Ginnis et al. Sys. ML and optimization with Model. Center – Paredis et al. Sys. ML-Modelica transformation spec – Paredis et al. SERC RT 21 Verification, Validation, and Accreditation project (VV&A) Growing education opportunities (short courses, undergrad/grad courses, . . . ) 13
Complex Aggregates Enabling advanced scalable modeling object-oriented, multi-directional, multi-dimensional do-loops using exact same structure model 14
Complex Aggregates Enabling advanced scalable modeling object-oriented, multi-directional, multi-dimensional do-loops using exact same structure model 15
Contents • Sys. ML parametrics advances 2010 -2011 – – – 5 -minute primer: fuel tank Advanced modeling constructs: complex aggregates Debugging and visualization: DNA signatures Scalability testing & metrics Expanding applications • Smart grid modeling – D Zwemer (Inter. CAX) • Information systems modeling – J Banks (NGC), Fire. SAT, biomedical, VV&A, . . . – Sys. ML-LVC simulation interoperability example: STK – Expanding tool support and deployment • Additional team progress – – – MBSE & manufacturing – Sys. ML & DEVS – Mc. Ginnis et al. Sys. ML and optimization with Model. Center – Paredis et al. Sys. ML-Modelica transformation spec – Paredis et al. SERC RT 21 Verification, Validation, and Accreditation project (VV&A) Growing education opportunities (short courses, undergrad/grad courses, . . . ) 16
“DNA Signatures” Autogenerated from Sys. ML parametrics Updates 2010 -2011 - Complex and primitive aggregates - Animation - Hide/show based on Sys. ML structure Model DNA Signature of instance ft 330 (flattened equation structure auto-generated from Sys. ML) Copyright © Georgia Tech and Inter. CAX. All Rights Reserved. Sys. ML and MBSE: A Quick-Start Course 17
Model “DNA Signatures” Using Sys. ML Parametrics Panorama Tool by Andy Scott (Undergrad Research Asst. ) and Russell Peak (Director, Modeling & Simulation Lab) Examples as of ~9/2009 — Low/Medium Complexity a. Snowman e. Cactus Test: Match the actual model titles (below) to their “DNA signatures” with imagined titles (left). __g__ 1. South Florida water mgt. (hydrology) model _____ 1. South Florida water mgt. (hydrology) model __a__ 2. 2 -spring physics model _____ 2. 2 -spring physics model b. Mini Snowman f. ? __e__ 3. 3 -year company financial model _____ 3. 3 -year company financial model __c__ 4. UAV road scanning system model _____ 4. UAV road scanning system model __b__ 5. Car gas mileage model _____ 5. Car gas mileage model __d__ 6. Airframe mechanical part model _____ 6. Airframe mechanical part model c. Snowflake g. Robot __f__ 7. Design verification model _____ 7. Design verification model (automated test for two Item 6. designs) d. Mouse www. msl. gatech. edu 18
Recent Models: ~Medium Complexity 2010 -10 Model size = O(100 s) equations, O(1000+) variables supply chain metrics mfg. sustainability: airframe wing “Turtle” “Galaxy with Black Hole” electronics recycling network “Tumbleweed” mfg. sustainability: automotive transmissions “Angler Fish” “Turtle Bird” 19
Recent Models: ~Medium Complexity F-86 Cast Wing Section [adapted from Bras, Romaniw, et al. ] – p 1/3 Sys. ML parametrics stats === structural stats 23 blocks 218 value properties 38 part properties 0 reference properties 0 shared properties 12 complex aggregate properties 0 primitive properties 195 constraint properties - regular 0 constraint properties - xfw. External 0 constraint properties - c. Mathematica cast wing – total assembly (Join. Noses. To. Spar highlighted) === instance stats 184 block instances 1879 value property slots 165 part property slots 0 reference property slots 0 shared property slots 53 complex aggregate members 0 primitive aggregate members 346 constraint property eqns - regular 0 constraint property eqns - xfw. External 0 constraint property eqns - c. Mathematica 20
Recent Models: ~Medium Complexity F-86 Cast Wing Assembly [adapted from Bras, Romaniw, et al. ] – p 2/3 cast wing – Join. Noses. To. Spar (machine highlighted) 21
Requirements Verification in Fire. Sat Sources: INCOSE SSWG and Inter. CAX LLC; Georgia Tech ASE 6006 NGDMC 22
Req. Verification in Fire. Sat Sys. ML model (including operational costs, etc. ) “DNA signature” auto-generated from Sys. ML parametrics model Model source: Dirk. Zwemer@Inter. CAX. com 23
Contents • Sys. ML parametrics advances 2010 -2011 – – – 5 -minute primer: fuel tank Advanced modeling constructs: complex aggregates Debugging and visualization: DNA signatures Scalability testing & metrics Expanding applications • Smart grid modeling – D Zwemer (Inter. CAX) • Information systems modeling – J Banks (NGC), Fire. SAT, biomedical, VV&A, . . . – Sys. ML-LVC simulation interoperability example: STK – Expanding tool support and deployment • Additional team progress – – – MBSE & manufacturing – Sys. ML & DEVS – Mc. Ginnis et al. Sys. ML and optimization with Model. Center – Paredis et al. Sys. ML-Modelica transformation spec – Paredis et al. SERC RT 21 Verification, Validation, and Accreditation project (VV&A) Growing education opportunities (short courses, undergrad/grad courses, . . . ) 24
Jan. 30, 2011 MBSE Workshop INCOSE IW 2011 Smart Grid Sys. ML Model Dirk Zwemer INCOSE Smart Grid Challenge Team and Modeling & Simulation Interoperability Team dirk. zwemer@intercax. com 25
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Smart Grid Model Summary • Objective – simulate effect of “Smart Meters” on electricity consumption • Tools - Magic. Draw Sys. ML, Para. Magic, Mathematica, MS Excel • Metric - Total Daily Expense for all Users 27
Smart. Grid Parametric Model 28
Source Subtypes 29
Customer Subtypes 30
Smart. Grid Parametric Model 31
Smart. Grid Parametric Model For one to many power plants, output (MW) is defined over 24 hour period. Cost model for each power plant is based on variable, fixed and capacity costs. 32
Smart. Grid Parametric Model At Bulk Generation level, output supply is aggregated and weighted average cost is calculated. 33
Smart. Grid Parametric Model At Operations level, cost and supply data are read and pricing signals are generated. 34
Smart. Grid Parametric Model Customers with Smart Meters read pricing signals and shift demand pattern during day. Demand shift obeys elasticity function. 35
Smart. Grid Parametric Model Customer demand daily expense is aggregated. Key metric is total daily expense. 36
Bulk Generation Capacity MW Name Gas Nuclear Solar Total Costs Power Lifetime Variable Fixed Capacity Period 1 Years $/k. W-hr $M/yr $/KW MW 50 25 0. 02 1 500 5 50 20 0. 007 3 5000 25 50 10 0. 001 0. 5 2000 1 150 7500 Period 2 MW 5 25 1 Period 3 MW 5 25 1 Period 4 MW 5 25 1 Period 5 MW 5 25 1 Period 6 MW 7. 5 25 5. 5 Period 7 MW 10 25 10 Period 8 MW 10 25 20 31 31 38 45 55 31 Supply 80 Bulk Supply (MW) 70 60 50 40 30 20 10 0 1 2 3 4 5 6 7 8 Gas 1 9 10 11 12 Nuclear 1 13 14 Solar 1 15 16 17 Total 18 19 20 21 22 23 24 Time Period (24 hour day) 37
Customer Demand Base Power Demand Price Period 1 Period 2 Period 3 Period 4 Period 5 Period 6 Period 7 Period 8 Name Elasticity $/k. W-hr MW MW Factory_1 3 0. 035 5 7. 5 10 15 20 30 Neighborhood_1 1 0. 035 10 10 10 25 40 30 Total 15 15 15 17. 5 20 40 60 60 Demand 80 Demand (MW) 70 60 50 40 30 20 10 0 1 2 3 Factory_1 4 5 6 7 8 Neighborhood_1 9 10 11 12 13 14 Total_Base_Demand 15 16 17 18 19 20 21 22 23 24 Time Period (24 hour day) 38
Results: Smart. Grid vs Dumb. Grid Supply-Demand Balance 100 90 Supply/Demand (MW) 80 70 60 50 40 30 20 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time Period (24 hour day) Total Supply (MW) Daily Expense: Effective Demand Base Demand Smart. Grid $60, 228 Dumb. Grid $66, 477 39
Contents • Sys. ML parametrics advances 2010 -2011 – – – 5 -minute primer: fuel tank Advanced modeling constructs: complex aggregates Debugging and visualization: DNA signatures Scalability testing & metrics Expanding applications • Smart grid modeling – D Zwemer (Inter. CAX) • Information systems modeling – J Banks (NGC), Fire. SAT, biomedical, VV&A, . . . – Sys. ML-LVC simulation interoperability example: STK – Expanding tool support and deployment • Additional team progress – – – MBSE & manufacturing – Sys. ML & DEVS – Mc. Ginnis et al. Sys. ML and optimization with Model. Center – Paredis et al. Sys. ML-Modelica transformation spec – Paredis et al. SERC RT 21 Verification, Validation, and Accreditation project (VV&A) Growing education opportunities (short courses, undergrad/grad courses, . . . ) 40
Snowflake Composition Five (5) Levels Snowflake de Spring 41
Snowflakes de Physica 42
Recent Models: ~Medium Complexity 2010 -10 Model size = O(100 s) equations, O(1000+) variables supply chain metrics mfg. sustainability: airframe wing “Turtle” “Galaxy with Black Hole” electronics recycling network “Tumbleweed” mfg. sustainability: automotive transmissions WIP 12 K equations 100 K, 1 M, . . . “Angler Fish” “Turtle Bird” 43
Contents • Sys. ML parametrics advances 2010 -2011 – – – 5 -minute primer: fuel tank Advanced modeling constructs: complex aggregates Debugging and visualization: DNA signatures Scalability testing & metrics Expanding applications • Smart grid modeling – D Zwemer (Inter. CAX) • Information systems modeling – J Banks (NGC), Fire. SAT, biomedical, VV&A, . . . – Sys. ML-LVC simulation interoperability example: STK – Expanding tool support and deployment • Additional team progress – – – MBSE & manufacturing – Sys. ML & DEVS – Mc. Ginnis et al. Sys. ML and optimization with Model. Center – Paredis et al. Sys. ML-Modelica transformation spec – Paredis et al. SERC RT 21 Verification, Validation, and Accreditation project (VV&A) Growing education opportunities (short courses, undergrad/grad courses, . . . ) 44
System M&S Examples in STK Based on original models by AGI. 45
Two-way interoperability Sys. ML-STK (throughout simulation run-time) - Changeable inputs (Sys. ML to STK): satellite and ground station properties - Results (STK to Sys. ML ): duration of ea. link session with ea. ground station 46
Initial prototype: STK & Sys. ML parametrics (for req. verification, . . . ) 47
Contents • Sys. ML parametrics advances 2010 -2011 – – – 5 -minute primer: fuel tank Advanced modeling constructs: complex aggregates Debugging and visualization: DNA signatures Scalability testing & metrics Expanding applications • Smart grid modeling – D Zwemer (Inter. CAX) • Information systems modeling – J Banks (NGC), Fire. SAT, biomedical, VV&A, . . . – Sys. ML-LVC simulation interoperability example: STK – Expanding tool support and deployment • Additional team progress – – – MBSE & manufacturing – Sys. ML & DEVS – Mc. Ginnis et al. Sys. ML and optimization with Model. Center – Paredis et al. Sys. ML-Modelica transformation spec – Paredis et al. SERC RT 21 Verification, Validation, and Accreditation project (VV&A) Growing education opportunities (short courses, undergrad/grad courses, . . . ) 48
Productionizing/Deploying GIT Xai. Tools™ Technology for Executing Sys. ML Parametrics www. Inter. CAX. com Tool Vendor Atego Sys. ML Authoring Tools Embedded. Plus E+ Sys. ML / RSA Para. Solver™ c. 2005 (formerly Artisan) Products by Inter. CAX LLC Yes Studio Prototypes by GIT 1 st release: 2010 -3 Q Yes — c. 2006 No Magic Yes Para. Magic® c. 2007 Telelogic/IBM Magic. Draw 1 st release: 2008 -Jul-21 — Melody™ Rhapsody 1 st release: 2010 -1 Q Sparx Systems Enterprise Architect — EA Parametrics Coming 2011 n/a XMI import/export Yes <tbd> c. 2006 Others <tbd> [1] Full disclosure: Inter. CAX LLC is a spin-off company originally created to commercialize technology from RS Peak’s GIT group. GIT has licensed technology to Inter. CAX and has an equity stake in the company. RS Peak is one of several business partners in Inter. CAX. Commercialization of the Sys. ML/composable object aspects has been fostered by the GIT Venture. Lab incubator program (www. venturelab. gatech. edu) via an Inter. CAX Venture. Lab project initiated October 2007. Copyright © Georgia Tech and Inter. CAX. All Rights Reserved. Sys. ML and MBSE: A Quick-Start Course 49
Contents • Sys. ML parametrics advances 2010 -2011 – – – 5 -minute primer: fuel tank Advanced modeling constructs: complex aggregates Debugging and visualization: DNA signatures Scalability testing & metrics Expanding applications • Smart grid modeling – D Zwemer (Inter. CAX) • Information systems modeling – J Banks (NGC), Fire. SAT, biomedical, VV&A, . . . – Sys. ML-LVC simulation interoperability example: STK – Expanding tool support and deployment • • Embedded systems simulation applications (with Sandia) Additional team progress – – – MBSE & manufacturing – Sys. ML & DEVS – Mc. Ginnis et al. Sys. ML and optimization with Model. Center – Paredis et al. Sys. ML-Modelica transformation spec – Paredis et al. SERC RT 21 Verification, Validation, and Accreditation project (VV&A) Growing education opportunities (short courses, undergrad/grad courses, . . . ) 50
Sys. ML-Enabled Design & Simulation of Embedded Systems • Exploring use of Sys. ML as front-end design tool for embedded systems to support system simulation • Collaborative effort between Sandia and Inter. CAX (funded by Sandia) • Tools interoperating in proof-of-concept prototypes: – Sys. ML authoring tool: Magic. Draw (No Magic Inc. ) – Embedded systems simulation tool: Orchestra (Sandia) – DSL/interface enabler: Maestro (Inter. CAX) Orchestra POC Greg Wickstrom Sandia National Laboratories PO Box 5800 MS 0340 Albuquerque, NM 87185 -0340 glwicks@sandia. gov (505) 844 -7708 51
Test Case: Hypothetical Machine Original Document-based Design Capture (Visio) 52
Test Case: Hypothetical Machine As captured in Sys. ML — a rich, user-friendly, computer-sensible formulation — view 1 (ibds) 53
Test Case: Hypothetical Machine As captured in Sys. ML — a rich, user-friendly, computer-sensible formulation — view 2 (bdds) 54
Sample Resulting XML-based Interface Content Automatically transforming Sys. ML-based design intent into Orchestra simulation inputs 55
Sample Benefits • Richer, more flexible/modular/reusable design capture vs. traditional Visio-based approach • Automated transformation to support simulation • Enhanced consistency • Additional design views (at no-extra-charge ): bdds, requirements, . . . 56
Contents • Sys. ML parametrics advances 2010 -2011 – – – 5 -minute primer: fuel tank Advanced modeling constructs: complex aggregates Debugging and visualization: DNA signatures Scalability testing & metrics Expanding applications • Smart grid modeling – D Zwemer (Inter. CAX) • Information systems modeling – J Banks (NGC), Fire. SAT, biomedical, VV&A, . . . – Sys. ML-LVC simulation interoperability example: STK – Expanding tool support and deployment • Additional team progress – – – MBSE & manufacturing – Sys. ML & DEVS – Mc. Ginnis et al. Sys. ML and optimization with Model. Center – Paredis et al. Sys. ML-Modelica transformation spec – Paredis et al. SERC RT 21 Verification, Validation, and Accreditation project (VV&A) Growing education opportunities (short courses, undergrad/grad courses, . . . ) 57
MBSE & Manufacturing Sys. ML-discrete event simulation interoperability (Mc. Ginnis et al. ) DSL + Model Transformation = 10 x reduction in simulation development time and effort 58
Exploring System Architectures Using Sys. ML and Model. Center Optimization Interoperability (C Paredis et al. ) Given: – Component models – Objectives / preferences Excavator Find: – Best system architecture – Best component parameters – Best controller engine pump_vdisp accum cylinder v_3 way How to connect and size these? © 2010 Chris Paredis 59
An Overview of the Sys. ML-Modelica Transformation Specification http: //www. omgwiki. org/OMGSys. ML/doku. php? id=sysml-modelica: sysml_and_modelica_integration Chris Paredis (Georgia Tech) Y. Bernard (Airbus), R. Burkhart (Deere & Co), H. de Koning (ESA/ESTEC), S. Friedenthal (Lockheed Martin Corp. ), P. Fritzson (Linköping University), N. Rouquette (JPL), W. Schamai (EADS) Presentation for the INCOSE Symposium 2010 Chicago, IL USA 60
What is Modelica? (www. modelica. org) Ø State-of-the-art Modeling Language for System Dynamics – Differential Algebraic Equations (DAE) – Discrete Events Ø Formal, object-oriented language Ø Standardized by the Modelica Association – Open language specification – tool independent Ø Multi-domain modeling Ø Ports represent energy flow (undirected) or signal flow (directed) Ø Acausal, equation-based, declarative (f-m*a=0) 61
motor torque A Robot Example in Modelica 62
Sys. ML-Modelica Transformation Specification: Context & Objective Ø Two complementary languages for Systems Engineering: – Descriptive modeling in Sys. ML – Formal equation-based modeling for analyses and trade studies in Modelica Ø Objective: – Leverage the strengths of both Sys. ML and Modelica by integrating them to create a more expressive and formal MBSE language. – Define a formal Transformation Specification: § a Sys. ML 4 Modelica profile § a Modelica abstract syntax metamodel § a mapping between Modelica and the profile Presentation for the INCOSE Symposium 2010 Chicago, IL USA 63
Sys. ML-Modelica Robot Example: Use Cases & Requirements 64
Sys. ML-Modelica Robot Example: Analysis and Trade Study Analysis models depend on descriptive models 65
Sys. ML 4 Modelica Analytical Model: Relation to Modelica Native Model 66
Sys. ML-Modelica Robot Example: Modelica model with simulation results 67
Specification Adoption Timeline & Status Ø Sys. ML – Sys. ML RFP: March 2003 – 1. 0 Specification: September 2007 – Currently: Revision Task Force 1. 3 Ø Modelica – 1. 0 Specification: September 1997 – 3. 1 Specification: May 2009 Ø Sys. ML-Modelica – – – Initial idea: July 2005 INCOSE MBSE Challenge Project: August 2007 – now OMG Working Group established: December 2008 Approved for public comment (RFC): June 2010 Adoption as OMG specification: 2011 (wip) 68
Summary Ø Objective: – Leverage the strengths of both Sys. ML and Modelica by integrating them to create a more expressive and formal MBSE language. Descriptive Modeling in Sys. ML + Formal Equation-Based Modeling for Analyses and Trade Studies in Modelica http: //doc. omg. org/syseng/2010 -6 -8 69
Contents • Sys. ML parametrics advances 2010 -2011 – – – 5 -minute primer: fuel tank Advanced modeling constructs: complex aggregates Debugging and visualization: DNA signatures Scalability testing & metrics Expanding applications • Smart grid modeling – D Zwemer (Inter. CAX) • Information systems modeling – J Banks (NGC), Fire. SAT, biomedical, VV&A, . . . – Sys. ML-LVC simulation interoperability example: STK – Expanding tool support and deployment • Additional team progress – – – MBSE & manufacturing – Sys. ML & DEVS – Mc. Ginnis et al. Sys. ML and optimization with Model. Center – Paredis et al. Sys. ML-Modelica transformation spec – Paredis et al. SERC RT 21 Verification, Validation, and Accreditation project (VV&A) Growing education opportunities (short courses, undergrad/grad courses, . . . ) 70
Activity 2 a in GIT RT 21 Project Leveraged existing capabilities/examples status as of 2011 -01 -20 (with completed examples listed) 71
Activity 3 a in GIT RT 21 Project status as of 2011 -01 -20 (with completed examples listed) Extended capabilities/examples and created new ones 72
Curriculum History & Formats Offered u Statistics as of Sept 2010 — www. pslm. gatech. edu/courses Full-semester Georgia Tech academic courses – ISYE / ME 8813 & 4803: Since Fall 2007 (~95 students total) u Industry short courses – Collaborative development & delivery with Inter. CAX LLC – Multiple [offerings, ~students] and formats since Aug 2008 » Sys. ML 101 [14, ~260]; Sys. ML 102 (hands-on) [12, ~205] – Modes: » Onsite at industry/government locations » Open enrollment via Georgia Tech (Atlanta, DC, Orlando, Vegas, . . . ) » Web-based “live” since Apr 2010 – Coming soon: 201/202, 301/302 (int/adv concepts, OCSMP prep, . . . ) u Georgia Tech Professional Masters academic courses – Professional Masters in Applied Systems Engineering www. pmase. gatech. edu – ASE 6005 Sys. ML-based MBSE course - Summer 2010 Copyright © Georgia Tech and Inter. CAX. All Rights Reserved. Sys. ML and MBSE: A Quick-Start Course 73
Good Progress. . . More Welcome Members • Sys. ML parametrics advances 2010 -2011 – – – 5 -minute primer: fuel tank Advanced modeling constructs: complex aggregates Debugging and visualization: DNA signatures Scalability testing & metrics Expanding applications • Smart grid modeling – D Zwemer (Inter. CAX) • Information systems modeling – J Banks (NGC), Fire. SAT, biomedical, VV&A, . . . – Sys. ML-LVC simulation interoperability example: STK – Expanding tool support and deployment • Additional team progress – – – MBSE & manufacturing – Sys. ML & DEVS – Mc. Ginnis et al. Sys. ML and optimization with Model. Center – Paredis et al. Sys. ML-Modelica transformation spec – Paredis et al. SERC RT 21 Verification, Validation, and Accreditation project (VV&A) Growing education opportunities (short courses, undergrad/grad courses, . . . ) 74
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