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INCOSE IW 10 Feb 5, 2010 Phoenix Model-Based Systems Engineering (MBSE) Challenge Modeling & INCOSE IW 10 Feb 5, 2010 Phoenix Model-Based Systems Engineering (MBSE) Challenge Modeling & Simulation Interoperability (MSI) Team Status Update . . . with Applications to Mechatronics, Other Cyber-Physical Systems, and Beyond. . . Presenter Russell Peak - Georgia Tech Other Team Leaders Chris Paredis, Leon Mc. Ginnis, Sandy Friedenthal, Roger Burkhart, Manas Bajaj v 2. 0 Portions are Copyright © 2010 by Georgia Tech Research Corporation, Atlanta, Georgia 30332 -0415 USA. All Rights Reserved. Permission to reproduce and distribute without changes for non-commercial purposes (including internal corporate usage) is hereby granted provided this notice and a proper citation are included. Page 1

Collaboration Approach Primary Current Team Leadership • Deere & Co. – Roger Burkhart • Collaboration Approach Primary Current Team Leadership • Deere & Co. – Roger Burkhart • Georgia Institute of Technology (GIT) – Russell Peak, Chris Paredis, Leon Mc. Ginnis, & co. – Leveraging collaborations in PSLM Center Sys. ML Focus Area (www. pslm. gatech. edu) • Inter. CAX – Manas Bajaj • Lockheed Martin – Sandy Friedenthal • Vendor Support Page 2

Georgia Tech Project Team Cumulative list of people involved to date [18 total] • Georgia Tech Project Team Cumulative list of people involved to date [18 total] • Project Leadership [3] – R Peak (MARC), C Paredis (ME), L Mc. Ginnis (ISy. E) • Other Researchers/Professionals [3] – S Cimtalay, M Wilson, V Ustun • Student Research Assistants—Graduated [5] – Undergrad: B Wilson – Masters: J Jobe, T Johnson, A Kerzhner – Ph. D: M Bajaj (joined Inter. CAX LLC) • Student Research Assistants—In-process [8] – Undergrad: B Aikens, M Qin, A Scott (Inter. CAX intern) – Masters: J Bankston, A Shah – Ph. D: E Huang, A Kerzhner (JPL intern), K Kwon 3

Contents • Phase 1 Synopsis (8/2007 -7/2008) • Phase 2 Highlights (8/2008 -Present) Addressing Contents • Phase 1 Synopsis (8/2007 -7/2008) • Phase 2 Highlights (8/2008 -Present) Addressing key needs per Phase 1 experiences: – Education – Research & Development – Productionization / Commercialization – Applications • Summary • Elaborations on Selected Topics • Related Resources 4

MBSE Challenge Team Objectives Phase 1: 2007 -2008 Overall Objectives • Define & demonstrate MBSE Challenge Team Objectives Phase 1: 2007 -2008 Overall Objectives • Define & demonstrate capabilities for advanced modeling & simulation interoperability (MSI) • Phase 1 Scope – Domain: Mechatronics – Capabilities: Methodologies, tools, requirements, and practical applications – MSI subset: Connecting system specification & design models with multiple engineering analysis & dynamic simulation models • Test & demonstrate how Sys. ML facilitates effective MSI Note: The objectives to date are primarily based on projects in the GIT PSLM Center sponsored by industry and government—see backup slides. Page 5

The 4 Pillars of Sys. ML Automotive Anti-Lock Braking System Example 1. Structure 2. The 4 Pillars of Sys. ML Automotive Anti-Lock Braking System Example 1. Structure 2. Behavior interaction state machine activity/ function definition use 3. Requirements Copyright © Georgia Tech and Inter. CAX. All Rights Reserved. 4. Parametrics Sys. ML and MBSE: A Quick-Start Course 6

Interoperability Method Objectives 7 Interoperability Method Objectives 7

Excavator Modeling & Simulation Testbed Tool Categories View 8 Excavator Modeling & Simulation Testbed Tool Categories View 8

Excavator Modeling & Simulation Testbed Interoperability Patterns View (MSI Panorama per MIM 0. 1) Excavator Modeling & Simulation Testbed Interoperability Patterns View (MSI Panorama per MIM 0. 1) 9

Simulation & Analysis Using Sys. ML Dec 2008: Final Phase 1 Overview Presentation Experiences Simulation & Analysis Using Sys. ML Dec 2008: Final Phase 1 Overview Presentation Experiences Applying Sys. ML in an Excavator Testbed and More Abstract This talk overviews Phase 1 experiences and lessons learned from an excavator testbed that interconnects simulation models with associated diverse system models, design models, and manufacturing models. The goal is to enable advanced model-based systems engineering (MBSE) in particular and model-based X (MBX) [1] in general. Our method employs Sys. ML as the primary technology to achieve multi-level multi-fidelity interoperability, while at the same time leveraging conventional modeling & simulation tools including mechanical CAD, factory CAD, spreadsheets, math solvers, finite element analysis (FEA), discrete event solvers, and optimization tools. This work is sponsored by several organizations including Deere and Lockheed and is part of the Modeling & Simulation Interoperability Team [2] in the INCOSE MBSE Challenge (with applications to mechatronics as an example domain). [1] The X in MBX includes engineering (MBE), manufacturing (MBM), and potentially other scopes and contexts such as model-based enterprises (MBE). [2] http: //www. pslm. gatech. edu/projects/incose-mbse-msi/ Citation RS Peak, CJJ Paredis, LF Mc. Ginnis, DA Zwemer (2008 -12) Simulation & Analysis Using Sys. ML—Experiences Applying Sys. ML in an Excavator Testbed and More. OMG Sys. ML Information Days, Burlingame CA. http: //eislab. gatech. edu/pubs/seminars-etc/2008 -12 -omg-sysml-info-days-peak/ Contact Russell. Peak@gatech. edu, Georgia Institute of Technology, Atlanta, www. msl. gatech. edu 10

Contents • Phase 1 Synopsis (8/2007 -7/2008) • Phase 2 Highlights (8/2008 -Present) Addressing Contents • Phase 1 Synopsis (8/2007 -7/2008) • Phase 2 Highlights (8/2008 -Present) Addressing key needs per Phase 1 experiences: – Education – Research & Development – Productionization / Commercialization – Applications • Summary 11

Curriculum History & Formats Offered Statistics as of Feb 2010 — www. pslm. gatech. Curriculum History & Formats Offered Statistics as of Feb 2010 — www. pslm. gatech. edu/courses u Full-semester Georgia Tech course – ISYE 8813: Fall 2007, 2008, 2009 (~60 students total) u Industry short courses – Multiple [offerings, ~students] since Aug 2008 » Sys. ML 101 [8, ~160]; Sys. ML 102 (hands-on) [6, ~110] » Onsite at industry locations » In Atlanta at the Georgia Tech Global Learning Center – Collaborative development & delivery with Inter. CAX LLC u Professional Masters course – Professional Masters in Applied Systems Engineering www. pmase. gatech. edu – ASE 6005 Sys. ML course starting Summer 2010 Copyright © Georgia Tech and Inter. CAX. All Rights Reserved. Sys. ML and MBSE: A Quick-Start Course 12

Industry Short Course Contents (p 1/2) Sys. ML 101: Tool-Independent Concepts Focus Copyright © Industry Short Course Contents (p 1/2) Sys. ML 101: Tool-Independent Concepts Focus Copyright © Georgia Tech and Inter. CAX. All Rights Reserved. Sys. ML and MBSE: A Quick-Start Course 13

Industry Short Course Contents (p 2/2) Sys. ML 102: Hands-on Execution-Oriented Focus Copyright © Industry Short Course Contents (p 2/2) Sys. ML 102: Hands-on Execution-Oriented Focus Copyright © Georgia Tech and Inter. CAX. All Rights Reserved. Sys. ML and MBSE: A Quick-Start Course 14

Mobile Robot Exercise from myro import * initialize( Mobile Robot Exercise from myro import * initialize("com 29") Executable Sys. ML Activity Model [after live update] Resulting python script senses() beep(1, 440) forward(1, 1) turn. Right(1, . 4) forward(1, 1) beep(1, 440) turn. Right(1, . 4) forward(1, 1) stop() 15

Sys. ML Activities Exercise @ JPL Team Contest Using Myro. Magic Plugin & Scribbler Sys. ML Activities Exercise @ JPL Team Contest Using Myro. Magic Plugin & Scribbler Rovers Copyright © Georgia Tech and Inter. CAX. All Rights Reserved. Sys. ML and MBSE: A Quick-Start Course 16

Mobile Robot Exercise Executable Sys. ML Activity Model with Sensors & Decision Nodes decision Mobile Robot Exercise Executable Sys. ML Activity Model with Sensors & Decision Nodes decision node guard condition (with sensor reading) 17

Mobile Robot Context (a cyber-physical system) Copyright © Georgia Tech and Inter. CAX. All Mobile Robot Context (a cyber-physical system) Copyright © Georgia Tech and Inter. CAX. All Rights Reserved. Sys. ML and MBSE: A Quick-Start Course 18

Auto-Generated Structured Python Scripts New format generated by Buzz. Toys Myro. Magic v 0. Auto-Generated Structured Python Scripts New format generated by Buzz. Toys Myro. Magic v 0. 3. 1 — a Magic. Draw plugin by GIT. Copyright © Georgia Tech and Inter. CAX. All Rights Reserved. Sys. ML and MBSE: A Quick-Start Course 19

Contents • Phase 1 Synopsis (8/2007 -7/2008) • Phase 2 Highlights (8/2008 -Present) Addressing Contents • Phase 1 Synopsis (8/2007 -7/2008) • Phase 2 Highlights (8/2008 -Present) Addressing key needs per Phase 1 experiences: – Education – Research & Development – Productionization / Commercialization – Applications • Summary 20

Phase 2: Research & Development Thrusts • Sys. ML-Modelica mapping • “Model DNA” signatures Phase 2: Research & Development Thrusts • Sys. ML-Modelica mapping • “Model DNA” signatures – parametric graph visualization, debugging, . . . • System-E/MCAD/CAE interoperability • Design-mfg interoperability; mfg simulation • Others (not shown here) – Graph transformations – Etc. 21

The following slides are excerpts from this presentation: Sys. ML-Modelica Transformation Specification (OMG ADTF The following slides are excerpts from this presentation: Sys. ML-Modelica Transformation Specification (OMG ADTF Meeting, Long Beach, 12/9/2009) Chris Paredis Georgia Tech On behalf of the Sys. ML-Modelica Working Group 22

What is Modelica? • State-of-the-art Modeling Language for System Dynamics – Differential Algebraic Equations What is Modelica? • State-of-the-art Modeling Language for System Dynamics – Differential Algebraic Equations (DAE) – Discrete Events • Formal, object-oriented language • Ports represent energy flow (undirected) or signal flow (directed) • Acausal, equation-based, declarative • Multi-domain modeling • Standardized by the Modelica Association 23

motor torque Modelica: Standard Library 24 24 motor torque Modelica: Standard Library 24 24

Working Group Focus and Scope • Objective: – Leverage the strengths of both Sys. Working Group Focus and Scope • 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 and a mapping between Modelica and the profile • Scope: – Cover the Modelica constructs needed for the Modelica Standard Library to be used in Sys. ML – Generate corresponding Sys. ML constructs that fit within the profiling mechanism 25

Simple Example Sys. ML Descriptive Model in Analysis Context Modelica Model Sys. ML 4 Simple Example Sys. ML Descriptive Model in Analysis Context Modelica Model Sys. ML 4 Modelica Analytical Model 26

Formal, Bidirectional Transformation Sys. ML 4 Modelica 27 Formal, Bidirectional Transformation Sys. ML 4 Modelica 27

Current Status • Draft of Transformation Specification § § Part I — Introduction Part Current Status • Draft of Transformation Specification § § Part I — Introduction Part II — Sys. ML 4 Modelica profile Part III — Modelica meta-model Part IV — Sys. ML-Modelica mapping, a bidirectional mapping between the Sys. ML 4 Modelica profile and the Modelica meta-model § Annex A – Robotic Sample Problem 28

Sys. ML-Modelica Summary • Objective: – Leverage the strengths of both Sys. ML and Sys. ML-Modelica Summary • 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 • Next Steps: – Open source reference implementations – Submit RFC for vote at March OMG meeting http: //www. omgwiki. org/OMGSys. ML/doku. php? id=sysml-modelica: sysml_and_modelica_integration 29

Phase 2: Research & Development Thrusts • Sys. ML-Modelica mapping • “Model DNA” signatures Phase 2: Research & Development Thrusts • Sys. ML-Modelica mapping • “Model DNA” signatures – parametric graph visualization, debugging, . . . • System-E/MCAD/CAE interoperability • Design-mfg interoperability; mfg simulation • Etc. 30

“Model DNA” Signatures Using Sys. ML Parametrics Panorama Tool by Andy Scott (Undergrad Research “Model DNA” Signatures Using Sys. ML Parametrics Panorama Tool by Andy Scott (Undergrad Research Asst. ) and Russell Peak (Director, Modeling & Simulation Lab) a. Snowman e. Cactus Test: Match the actual model titles (below) to their “DNA signatures” with imagined titles (left). _____ 1. South Florida water mgt. (hydrology) model _____ 2. 2 -spring physics model b. Mini Snowman f. ? _____ 3. 3 -year company financial model _____ 4. UAV road scanning system model _____ 5. Car gas mileage model _____ 6. Airframe mechanical part model c. Snowflake g. Robot _____ 7. Design verification model (automated test for two Item 6. designs) [see answers at the end of this presentation] d. Mouse www. msl. gatech. edu 31

Satellite Tutorial Highlights: Simple. Sat Sys. ML par view and Para. Magic tool for Satellite Tutorial Highlights: Simple. Sat Sys. ML par view and Para. Magic tool for execution “Object-Oriented Spreadsheet” plus more. . . Copyright © Georgia Tech and Inter. CAX. All Rights Reserved. Sys. ML and MBSE: A Quick-Start Course 32

Satellite Tutorial Highlights: Simple. Sat Two views of same model: par and flattened graph Satellite Tutorial Highlights: Simple. Sat Two views of same model: par and flattened graph par (Sys. ML parametrics view) Model DNA signature (a. k. a. flattened graph) auto-generated from Sys. ML model Copyright © Georgia Tech and Inter. CAX. All Rights Reserved. Sys. ML and MBSE: A Quick-Start Course 33

Model DNA Signature Example Parametrics Model for an Analysis Tool Test Suite 34 Model DNA Signature Example Parametrics Model for an Analysis Tool Test Suite 34

Phase 2: Research & Development Thrusts • Sys. ML-Modelica mapping • “Model DNA” signatures Phase 2: Research & Development Thrusts • Sys. ML-Modelica mapping • “Model DNA” signatures – parametric graph visualization, debugging, . . . • System-E/MCAD/CAE interoperability • Design-mfg interoperability; mfg simulation • Etc. See also “Elaborations on Selected Topics” after Summary 35

Emerging Tools: Connecting a System Model to Domain Models via Sys. ML Title: Composable Emerging Tools: Connecting a System Model to Domain Models via Sys. ML Title: Composable Mission Framework for Rapid End-to-End Mission Design and Simulation Principal Investigator: Dr. Manas Bajaj, Inter. CAX LLC Phase 1: Jan – Jul, 2009 [NASA SBIR-08 -1 -S 4. 02 -9130] — NASA SBIR project Technical Abstract: The innovation proposed here is the Composable Mission Framework (CMF)—a model-based software framework that shall enable seamless continuity of mission design and simulation from early stage advanced studies to detailed mission design and development. The uniqueness of our approach lies in using an open standard for systems modeling and design (Sys. ML) to wrap mission models including the mission development process thus providing a coherent map of mission knowledge. Inter. CAX's Composable Object technology provides the backend wrapping, model management, and simulation orchestration capabilities to the visual Sys. ML-based mission model at the front end. The Composable Object technology has already demonstrated the ability to power Sys. ML-based models with math simulation capabilities for early design stages. Para. Magic is a commercially available tool being used by early adopters of Sys. ML at JPL. The Composable Object technology has also demonstrated the ability to associate detailed design and simulation models such as those created in CAD and FEA tools. However, a big gap exists in the Sys. ML-based world for conceptual system design and the detailed system design-based world. If the detailed system design and simulation models could be wrapped as Sys. ML objects and the simulations and workflows orchestrated by the Composable Object technology, it will cover the entire gamut of complex system modeling and analysis world from trade studies and optimization to project scheduling. The key objective of Phase 1 is to wrap both conceptual and detailed system design and simulation models as Sys. ML objects which has not been done before, and to demonstrate continuity of mission concepts from simple to detailed implementation. Copyright Inter. CAX – All rights reserved 36

System Design & Analysis Integrating and Executing Diverse Models System Sub-system 1 Sub-system 2 System Design & Analysis Integrating and Executing Diverse Models System Sub-system 1 Sub-system 2 Sub-system n … Comp 11 … Comp 1 m 1 Design See also “Elaborations on Selected Topics” after Summary Comp 11 – Comp 1 m 1 – Behavior 1 Behavior i 1 Mapping Relationships (Parametrics) m. CAD model in Sys. ML e. CAD model in Sys. ML FEA models in Sys. ML (assembly structure, properties, constraints) (key system-level entities and properties) (analysis conditions & results) System model in Sys. ML External tools and models m. CAD models e. CAD models CAE models Other simulation models (NX, Pro/E, CATIA, …) (Board Station, CR 5000, …) (FEA, CFD, …) (STK, DEVS, …) Copyright Inter. CAX – All rights reserved 37

Connecting system model and domain models PCA = printed circuit assembly PCB = printed Connecting system model and domain models PCA = printed circuit assembly PCB = printed circuit board (bare substrate w/ metal traces. . . ) BGA = ball grid array (a type of electronic component) MCAD ECAD Copyright Inter. CAX – All rights reserved 38

“System Model”- “X Domain Model” Integration Ex. for X = Mechanical CAD Systems Engineering “System Model”- “X Domain Model” Integration Ex. for X = Mechanical CAD Systems Engineering Domain Design Domain Magic. Draw Sys. ML System Model NX MCAD Component Z CAD Model Component Z System Model Property a 1 Property a 2 Step 1 a Step 1 b Step 2 Step 3 Step 4 a 2 = b 1+b 2 Property b 1 Property b 2 Property b 3 Component Z CAD Design Parameter b 1 Parameter b 2 Parameter b 3 Create a system model (e. g. with Magic. Draw Sys. ML) Create a CAD domain model (e. g. with Siemens NX) Import the CAD model into Sys. ML as a CAD Model block Connect (map) the CAD model to the system model using Sys. ML parametrics Control an auto-synch process: updates in CAD model ↔ updates in system model Copyright Inter. CAX – All rights reserved 39

Para. Magic is used to execute the resulting total model. It computes systemlevel cost Para. Magic is used to execute the resulting total model. It computes systemlevel cost & weight from all nested subsystem-level & component-level models (originating from MCAD / ECAD /… tools), and it verifies related requirements. Weight requirement satisfied Cost requirement not satisfied Copyright Inter. CAX – All rights reserved 40

Phase 2: Research & Development Thrusts • Sys. ML-Modelica mapping • “Model DNA” signatures Phase 2: Research & Development Thrusts • Sys. ML-Modelica mapping • “Model DNA” signatures – parametric graph visualization, debugging, . . . • System-E/MCAD/CAE interoperability • Design-mfg interoperability; mfg simulation • Etc. 41

Integrating Mfg Design and Simulation L Mc. Ginnis et al. — http: //www. pslm. Integrating Mfg Design and Simulation L Mc. Ginnis et al. — http: //www. pslm. gatech. edu/projects/incose-mbse-msi/ 42

Excavator Modeling & Simulation Testbed Tool Categories View 43 Excavator Modeling & Simulation Testbed Tool Categories View 43

Excavator Modeling & Simulation Environment Interoperability Patterns View (MSI Panorama per MIM 0. 1) Excavator Modeling & Simulation Environment Interoperability Patterns View (MSI Panorama per MIM 0. 1) 44

Manufacturing Model Interdependencies 45 Manufacturing Model Interdependencies 45

Detailed Process Planning 46 Detailed Process Planning 46

On Demand Simulation “On demand” simulation puts simulation methodology in the hands of the On Demand Simulation “On demand” simulation puts simulation methodology in the hands of the “problem owners” 47

e. M-Plant Simulation 48 e. M-Plant Simulation 48

Contents • Phase 1 Synopsis (8/2007 -7/2008) • Phase 2 Highlights (8/2008 -Present) Addressing Contents • Phase 1 Synopsis (8/2007 -7/2008) • Phase 2 Highlights (8/2008 -Present) Addressing key needs per Phase 1 experiences: – Education – Research & Development – Productionization / Commercialization – Applications • Summary 49

Excavator Modeling & Simulation Testbed Interoperability Patterns View (MSI Panorama per MIM 0. 1) Excavator Modeling & Simulation Testbed Interoperability Patterns View (MSI Panorama per MIM 0. 1) 50

Productionizing/Deploying GIT Xai. Tools™ Technology for Executing Sys. ML Parametrics www. Inter. CAX. com Productionizing/Deploying GIT Xai. Tools™ Technology for Executing Sys. ML Parametrics www. Inter. CAX. com Vendor Artisan Sys. ML Tool Studio Prototype by GIT Product by Inter. CAX LLC Yes (2009 -4 Q beta) Embedded. Plus E+ Sys. ML / RSA Yes No Magic. Draw Yes Para. Magic™ (Jul 21, 2008 release) Telelogic/IBM Rhapsody — Melody™ (2010 -1 Q release) Sparx Systems Enterprise Arch. n/a XMI import/export Others Yes [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. 51

Products & Services 52 Products & Services 52

Contents • Phase 1 Synopsis (8/2007 -7/2008) • Phase 2 Highlights (8/2008 -Present) Addressing Contents • Phase 1 Synopsis (8/2007 -7/2008) • Phase 2 Highlights (8/2008 -Present) Addressing key needs per Phase 1 experiences: – Education – Research & Development – Productionization / Commercialization – Applications • Summary 53

Broadly Applicable Technology Examples of Executable Sys. ML Parametrics • • • • Road Broadly Applicable Technology Examples of Executable Sys. ML Parametrics • • • • Road scanning system using unmanned aerial vehicle (UAVs) UAV-based missile interceptor system trade study Space systems (tutorials): orbit planning; mass/cost roll-ups Space systems (studies/pilots): Fire. Sat (INCOSE SSWG), . . . Space systems (actuals): science merit function, . . . Environmentally-conscious energy systems / smart grid Manufacturing “green-ness” / sustainability assessments Regional water management systems (e. g. South Florida). . . ~Next-generation Mechanical part design and analysis (FEA) object-oriented. . . spreadsheets Wind turbine supply chain management (and more) Insurance claims processing and website capacity model Financial model for small businesses Banking service levels model. . . 54

Supply Chain Model for Global Supply Chain Management & Optimization - Generic (shown) - Supply Chain Model for Global Supply Chain Management & Optimization - Generic (shown) - Wind turbine-specifics (not shown) Copyright Inter. CAX – All rights reserved Sources: Dirk. Zwemer@Inter. CAX. com and Georgia Tech 55

Supply Chain Model – Sys. ML Parametrics Connect to Optimization Models, Compute Value-at-Risk Ex. Supply Chain Model – Sys. ML Parametrics Connect to Optimization Models, Compute Value-at-Risk Ex. Given 100’s of product orders and sourcing plans for the next 12 months, what percent of my business is at-risk if Supplier X does not deliver, or if Part Y becomes obsolete? Copyright Inter. CAX – All rights reserved 56

Broadly Applicable Technology Examples of Executable Sys. ML Parametrics • • • • Road Broadly Applicable Technology Examples of Executable Sys. ML Parametrics • • • • Road scanning system using unmanned aerial vehicle (UAVs) UAV-based missile interceptor system trade study Space systems (tutorials): orbit planning; mass/cost roll-ups Space systems (studies/pilots): Fire. Sat (INCOSE SSWG), . . . Space systems (actuals): science merit function, . . . Environmentally-conscious energy systems / smart grid Manufacturing “green-ness” / sustainability assessments Regional water management systems (e. g. South Florida). . . ~Next-generation Mechanical part design and analysis (FEA) object-oriented. . . spreadsheets Wind turbine supply chain management (and more) Insurance claims processing and website capacity model Financial model for small businesses Banking service levels model. . . 57

Regional Water Mgt. System: Hydrology Model Sources: www. sfwmd. gov and Dirk. Zwemer@Inter. CAX. Regional Water Mgt. System: Hydrology Model Sources: www. sfwmd. gov and Dirk. Zwemer@Inter. CAX. com [System. B_v 2 h_rsp. mdzip] 58

Regional Water Mgt. System: Hydrology Model DNA signature (flattened graph “panorama” view) (auto-generated from Regional Water Mgt. System: Hydrology Model DNA signature (flattened graph “panorama” view) (auto-generated from Sys. ML parametrics model) [System. B_v 2 h. mdzip] 59

Broadly Applicable Technology Examples of Executable Sys. ML Parametrics • • • • Road Broadly Applicable Technology Examples of Executable Sys. ML Parametrics • • • • Road scanning system using unmanned aerial vehicle (UAVs) UAV-based missile interceptor system trade study Space systems (tutorials): orbit planning; mass/cost roll-ups Space systems (studies/pilots): Fire. Sat (INCOSE SSWG), . . . Space systems (actuals): science merit function, . . . Environmentally-conscious energy systems / smart grid Manufacturing “green-ness” / sustainability assessments Regional water management systems (e. g. South Florida). . . ~Next-generation Mechanical part design and analysis (FEA) object-oriented. . . spreadsheets Wind turbine supply chain management (and more) Insurance claims processing and website capacity model Financial model for small businesses Banking service levels model. . . 60

Using Sys. ML to Evaluate Sustainability Metrics (similar to Other Metrics: Design Flexibility, . Using Sys. ML to Evaluate Sustainability Metrics (similar to Other Metrics: Design Flexibility, . . . ) F-86 wing section test case Aluminum Cast and Machined Components More Room for Internal Parts Fewer Manufacturing Operations Heavier Rolled, Bent, Stamped Sheet Metal Less Room for Internal Parts More Manufacturing Operations Lighter Source: Bras, Romaniw, et al. 10/2009 www. sdm. gatech. edu 61 61

F-86 Wing Section Test Case in Sys. ML Parametrics Comparing Sustainability Metrics for Design F-86 Wing Section Test Case in Sys. ML Parametrics Comparing Sustainability Metrics for Design Alternatives “Ob ject 12/21/09 -Or ien plus ted Sp re mo re. adshe. . et” Source: Bras, Romaniw, et al. 10/2009 62 www. sdm. gatech. edu 62

Contents • Phase 1 Synopsis (8/2007 -7/2008) • Phase 2 Highlights (8/2008 -Present) Addressing Contents • Phase 1 Synopsis (8/2007 -7/2008) • Phase 2 Highlights (8/2008 -Present) Addressing key needs per Phase 1 experiences: – Education – Research & Development – Productionization / Commercialization – Applications • Summary • Elaborations on Selected Topics • Related Resources 63

Modeling & Simulation Interoperability Benefits of Sys. ML-based Approach Precision Knowledge for the Model-Based Modeling & Simulation Interoperability Benefits of Sys. ML-based Approach Precision Knowledge for the Model-Based Enterprise 64

MBSE Challenge Team Mechatronics / Model Interoperability Open “Call for Participation” • Systems engineering MBSE Challenge Team Mechatronics / Model Interoperability Open “Call for Participation” • Systems engineering drivers in commercial settings – Increased system complexity – Cross-disciplinary communication/coordination • Enhancement possibilities based on interest – Other demonstration examples and testbeds – Interoperability testing between Sys. ML tools – Shared models and libraries • Primary contacts – Russell Peak [Russell. Peak @ gatech. edu] – Sandy Friedenthal [sanford. friedenthal @ lmco. com] – Roger Burkhart [Burkhart. Roger. M @ John. Deere. com] Page 65

Contents • Phase 1 Synopsis (8/2007 -7/2008) • Phase 2 Highlights (8/2008 -Present) Addressing Contents • Phase 1 Synopsis (8/2007 -7/2008) • Phase 2 Highlights (8/2008 -Present) Addressing key needs per Phase 1 experiences: – Education – Research & Development – Productionization / Commercialization – Applications • Summary • Elaborations on Selected Topics • Related Resources 66

Phase 2: Research & Development Thrusts • Sys. ML-Modelica mapping • “Model DNA” signatures Phase 2: Research & Development Thrusts • Sys. ML-Modelica mapping • “Model DNA” signatures – parametric graph visualization, debugging, . . . • System-E/MCAD/CAE interoperability • etc. Elaborated in next slides. . . 67

System Design & Analysis Integrating and Executing Diverse Models System Sub-system 1 Sub-system 2 System Design & Analysis Integrating and Executing Diverse Models System Sub-system 1 Sub-system 2 Sub-system n … Comp 11 … Comp 1 m 1 Design Comp 11 – Comp 1 m 1 – Behavior 1 Behavior i 1 Mapping Relationships (Parametrics) m. CAD model in Sys. ML e. CAD model in Sys. ML FEA models in Sys. ML (assembly structure, properties, constraints) (key system-level entities and properties) (analysis conditions & results) System model in Sys. ML External tools and models m. CAD models e. CAD models CAE models Other simulation models (NX, Pro/E, CATIA, …) (Board Station, CR 5000, …) (FEA, CFD, …) (STK, DEVS, …) Copyright Inter. CAX – All rights reserved 68

System model of a Mini Satellite with a electronic comp (BGA) Author: System Engr. System model of a Mini Satellite with a electronic comp (BGA) Author: System Engr. PCA = printed circuit assembly PCB = printed circuit board (bare substrate w/ metal traces. . . ) BGA = ball grid array (a type of electronic component) Copyright Inter. CAX – All rights reserved 69

Mini Satellite must satisfy weight and cost requirements Copyright Inter. CAX – All rights Mini Satellite must satisfy weight and cost requirements Copyright Inter. CAX – All rights reserved 70

System Design & Analysis Integrating and Executing Diverse Models System Sub-system 1 Sub-system 2 System Design & Analysis Integrating and Executing Diverse Models System Sub-system 1 Sub-system 2 Sub-system n … Comp 11 … Comp 1 m 1 Design Comp 11 – Comp 1 m 1 – Behavior 1 Behavior i 1 Mapping Relationships (Parametrics) m. CAD model in Sys. ML e. CAD model in Sys. ML FEA models in Sys. ML (assembly structure, properties, constraints) (key system-level entities and properties) (analysis conditions & results) System model in Sys. ML External tools and models m. CAD models e. CAD models CAE models Other simulation models (NX, Pro/E, CATIA, …) (Board Station, CR 5000, …) (FEA, CFD, …) (STK, DEVS, …) Copyright Inter. CAX – All rights reserved 71

Mechanical CAD Model of BGA (top view – mold, chip, heat sink comps) CAD Mechanical CAD Model of BGA (top view – mold, chip, heat sink comps) CAD (Siemens NX) model of the BGA assembly Author: Mechanical Design Engineer Copyright Inter. CAX – All rights reserved 72

Mechanical CAD Model of BGA (bottom view - ~200 solder balls) CAD (Siemens NX) Mechanical CAD Model of BGA (bottom view - ~200 solder balls) CAD (Siemens NX) model of the BGA assembly Author: Mechanical Design Engineer Copyright Inter. CAX – All rights reserved 73

Challenges u The system engineer needs to propagate component requirements (e. g. weight, height) Challenges u The system engineer needs to propagate component requirements (e. g. weight, height) from the system model (Sys. ML) to mechanical design model (NX) u The CAD engineer needs to propagate component properties (NX) to system model (Sys. ML) to verify the design in system context (repeated as the design progresses) u The system engineer and mechanical engineer need to “map”/connect component properties in NX model and component properties in Sys. ML model. Copyright Inter. CAX – All rights reserved 74

“System Model”- “X Domain Model” Integration Ex. for X = Mechanical CAD Systems Engineering “System Model”- “X Domain Model” Integration Ex. for X = Mechanical CAD Systems Engineering Domain Design Domain Magic. Draw Sys. ML System Model NX MCAD Component Z CAD Model Component Z System Model Property a 1 Property a 2 Step 1 a Step 1 b Step 2 Step 3 Step 4 a 2 = b 1+b 2 Property b 1 Property b 2 Property b 3 Component Z CAD Design Parameter b 1 Parameter b 2 Parameter b 3 Create a system model (e. g. with Magic. Draw Sys. ML) Create a CAD domain model (e. g. with Siemens NX) Import the CAD model into Sys. ML as a CAD Model block Connect (map) the CAD model to the system model using Sys. ML parametrics Control an auto-synch process: updates in CAD model ↔ updates in system model Copyright Inter. CAX – All rights reserved 75

Sys. ML model of the BGA assembly automatically generated from NX model Copyright Inter. Sys. ML model of the BGA assembly automatically generated from NX model Copyright Inter. CAX – All rights reserved 76

The mapping (non-directed connections) between the BGA component in the Mini Satellite system model The mapping (non-directed connections) between the BGA component in the Mini Satellite system model (Sys. ML) and the MCAD NX model (now exposed in Sys. ML) can now be specified by the user. . . (the starting point shown here). . . Copyright Inter. CAX – All rights reserved 77

The resulting system model - MCAD model connections (as specified by the user in The resulting system model - MCAD model connections (as specified by the user in a Sys. ML parametrics diagram) are shown here. This parametric diagram is executable and is an integral aspect of the overall system model. Copyright Inter. CAX – All rights reserved 78

Para. Magic is used to execute the resulting total model. It computes systemlevel cost Para. Magic is used to execute the resulting total model. It computes systemlevel cost & weight from all nested subsystem-level & component-level models (originating from MCAD / ECAD /. . . tools), and it verifies related requirements. Weight requirement satisfied Cost requirement not satisfied Copyright Inter. CAX – All rights reserved 79

System Design & Analysis Integrating and Executing Diverse Models System Sub-system 1 Sub-system 2 System Design & Analysis Integrating and Executing Diverse Models System Sub-system 1 Sub-system 2 Sub-system n … Comp 11 … Comp 1 m 1 Design Comp 11 – Comp 1 m 1 – Behavior 1 Behavior i 1 Mapping Relationships (Parametrics) m. CAD model in Sys. ML e. CAD model in Sys. ML FEA models in Sys. ML (assembly structure, properties, constraints) (key system-level entities and properties) (analysis conditions & results) System model in Sys. ML External tools and models m. CAD models e. CAD models CAE models Other simulation models (NX, Pro/E, CATIA, …) (Board Station, CR 5000, …) (FEA, CFD, …) (STK, DEVS, …) Copyright Inter. CAX – All rights reserved 80

STEP AP 210 (IS 0 10303 -210) Design Standard for Electromechanical Products Design Integrators STEP AP 210 (IS 0 10303 -210) Design Standard for Electromechanical Products Design Integrators (LKSoft - an Inter. CAX partner) ECAD Tools Board Station (Mentor Graphics) Prototyped in SBIR Phase 1 project CR 5000 (Zuken) VISULA (Zuken) Allegro (Cadence) … Enterprise Databases Part libraries Material libraries … Copyright Inter. CAX – All rights reserved STEP AP 210 model (ISO 10303 -210) Sys. ML www. ap 210. org www. wikistep. org www. lksoft. com STEP AP 210 Facts - O(100 man-yrs) in development - 1000+ concepts - Edition 1 released in 2001 - Edition 2 releasing soon (2010) - In-production at Rockwell Collins, Boeing, NASA, … 81

Sys. ML Schema derived from STEP AP 210 (9 high-level SE-related concepts) Copyright Inter. Sys. ML Schema derived from STEP AP 210 (9 high-level SE-related concepts) Copyright Inter. CAX – All rights reserved 82

AP 210 -based ECAD Model (I-501) (PCA with a 9 -stratum PCB and 4 AP 210 -based ECAD Model (I-501) (PCA with a 9 -stratum PCB and 4 comps) IDA-STEP (LKSoft) www. ida-step. net Layout of electrical features on layers Copyright Inter. CAX – All rights reserved 83

AP 210 -based ECAD Model (I-501) (PCB Stackup showing 9 stratums) Stackup of PCB AP 210 -based ECAD Model (I-501) (PCB Stackup showing 9 stratums) Stackup of PCB stratums Copyright Inter. CAX – All rights reserved 84

Sys. ML Instance Model Auto-generated from I-501 AP 210 Model Printed Circuit Assembly Printed Sys. ML Instance Model Auto-generated from I-501 AP 210 Model Printed Circuit Assembly Printed Circuit Board 4 components 9 PCB stratums Copyright Inter. CAX – All rights reserved 85

System Design & Analysis Integrating and Executing Diverse Models System Sub-system 1 Sub-system 2 System Design & Analysis Integrating and Executing Diverse Models System Sub-system 1 Sub-system 2 Sub-system n … Comp 11 … Comp 1 m 1 Design Comp 11 – Comp 1 m 1 – Behavior 1 Behavior i 1 Mapping Relationships (Parametrics) m. CAD model in Sys. ML e. CAD model in Sys. ML FEA models in Sys. ML (assembly structure, properties, constraints) (key system-level entities and properties) (analysis conditions & results) System model in Sys. ML External tools and models m. CAD models e. CAD models CAE models Other simulation models (NX, Pro/E, CATIA, …) (Board Station, CR 5000, …) (FEA, CFD, …) (STK, DEVS, …) Copyright Inter. CAX – All rights reserved 86

Fire. Sat System Model (PCA and PCB components) Copyright Inter. CAX – All rights Fire. Sat System Model (PCA and PCB components) Copyright Inter. CAX – All rights reserved 87

Printed Circuit Assembly – Testbed Model PCA PCB Packaged components Copyright Inter. CAX – Printed Circuit Assembly – Testbed Model PCA PCB Packaged components Copyright Inter. CAX – All rights reserved 88

Requirements, Design/CAD, and Analysis/CAE u Electronic Artifacts - PCA, PCB, Packaged parts u Must Requirements, Design/CAD, and Analysis/CAE u Electronic Artifacts - PCA, PCB, Packaged parts u Must satisfy requirements u Analysis/CAE models defined for verifying requirements Copyright Inter. CAX – All rights reserved 89

PCA CAD Model in NX [~2000 bodies] (top view – 6 BGA assembly components) PCA CAD Model in NX [~2000 bodies] (top view – 6 BGA assembly components) Copyright Inter. CAX – All rights reserved 90

PCA CAD Model in NX [~2000 bodies] (bottom view – 4 BGA assembly components) PCA CAD Model in NX [~2000 bodies] (bottom view – 4 BGA assembly components) Copyright Inter. CAX – All rights reserved 91

PCA Model in Sys. ML (schema) (auto-generated from NX CAD model) Printed Circuit Assembly PCA Model in Sys. ML (schema) (auto-generated from NX CAD model) Printed Circuit Assembly Printed Circuit Board 10 BGA assembly components ~2000 BGA solder ball features Copyright Inter. CAX – All rights reserved 92

PCA Model in Sys. ML (instance) (auto-generated from NX CAD model) Printed Circuit Assembly PCA Model in Sys. ML (instance) (auto-generated from NX CAD model) Printed Circuit Assembly Printed Circuit Board Copyright Inter. CAX – All rights reserved 10 BGA assembly components 93

Printed Circuit Board – Behavior Models Copyright Inter. CAX – All rights reserved 94 Printed Circuit Board – Behavior Models Copyright Inter. CAX – All rights reserved 94

Interfaces to External Tools/Models Similar approaches can be used for other externally defined models, Interfaces to External Tools/Models Similar approaches can be used for other externally defined models, such as STK models and CAE models (e. g. finite element analysis model, CFD model, etc. ) Interfaces prototyped for this SBIR Phase 1 project: - Magic. Draw - NX plugin (mechanical CAD tool) - Magic. Draw - AP 210 plugin (electrical CAD standard) - ABAQUS/ANSYS finite element analysis tool Existing commercial interfaces used: - Matlab/Simulink, Excel, Mathematica (in Para. Magic) - AP 210 interfaces to major ECAD tools (www. lksoft. com) Copyright Inter. CAX – All rights reserved 95

Contents • Phase 1 Synopsis (8/2007 -7/2008) • Phase 2 Highlights (8/2008 -Present) Addressing Contents • Phase 1 Synopsis (8/2007 -7/2008) • Phase 2 Highlights (8/2008 -Present) Addressing key needs per Phase 1 experiences: – Education – Research & Development – Productionization / Commercialization – Applications • Summary • Elaborations on Selected Topics • Related Resources 96

Sys. ML Parametrics—Suggested Starting Points Introductory Papers/Tutorials • Peak RS, Burkhart RM, Friedenthal SA, Sys. ML Parametrics—Suggested Starting Points Introductory Papers/Tutorials • Peak RS, Burkhart RM, Friedenthal SA, Wilson MW, Bajaj M, Kim I (2007) Simulation-Based Design Using Sys. ML—Part 1: A Parametrics Primer. INCOSE Intl. Symposium, San Diego. [Provides tutorial-like introduction to Sys. ML parametrics. ] http: //eislab. gatech. edu/pubs/conferences/2007 -incose-is-1 -peak-primer/ • Peak RS, Burkhart RM, Friedenthal SA, Wilson MW, Bajaj M, Kim I (2007) Simulation-Based Design Using Sys. ML—Part 2: Celebrating Diversity by Example. INCOSE Intl. Symposium, San Diego. [Provides tutorial-like introduction on using Sys. ML for modeling & simulation, including the MRA method for creating parametric simulation templates that are connected to design models. ] http: //eislab. gatech. edu/pubs/conferences/2007 -incose-is-2 -peak-diversity/ Example Applications • Peak RS, Burkhart RM, Friedenthal SA, Paredis CJJ, Mc. Ginnis LF (2008) Integrating Design with Simulation & Analysis Using Sys. ML— Mechatronics/Interoperability Team Status Report. Presentation to INCOSE MBSE Challenge Team, Utrecht, Holland. [Overviews modeling & simulation interoperability (MSI) methodology progress in the context of an excavator testbed. ] http: //eislab. gatech. edu/pubs/seminars-etc/2008 -06 -incose-is-mbse-mechatronics-msi-peak/ • Peak RS (2007) Leveraging Templates & Processes with Sys. ML. Invited Presentation. Developing a Design/Simulation Framework: A Workshop with CPDA's Design and Simulation Council, Atlanta. [Includes applications to automotive steering wheel systems and FEA simulation templates. ] http: //eislab. gatech. edu/pubs/conferences/2007 -cpda-dsfw-peak/ Commercial Tools and Other Examples/Tutorials • Para. Magic™ plugin for Magic. Draw®. Developed by Inter. CAX LLC (a Georgia Tech spin-off) [1]. Available at www. Magic. Draw. com. • Zwemer DA and Bajaj M (2008) Sys. ML Parametrics and Progress Towards Multi-Solvers and Next-Generation Object-Oriented Spreadsheets. Frontiers in Design & Simulation Workshop, Georgia Tech PSLM Center, Atlanta. [Highlights techniques for executing Sys. ML parametrics based on the Para. Magic™ plugin for Magic. Draw®. Includes UAV and financial systems examples. ] http: //www. pslm. gatech. edu/events/frontiers/ See slides below for additional references and resources. [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 is being fostered by the GIT Venture. Lab incubator program ( www. venturelab. gatech. edu) via an Inter. CAX Venture. Lab project initiated October 2007. 97

MBX/Sys. ML-Related Efforts at Georgia Tech • Sys. ML Focus Area web page – MBX/Sys. ML-Related Efforts at Georgia Tech • Sys. ML Focus Area web page – http: //www. pslm. gatech. edu/topics/sysml/ – Includes links to publications, applications, projects, examples, courses, commercialization, etc. – Frontiers 2008 workshop on MBSE/MBX, Sys. ML, . . . • Selected projects – – – Deere: System dynamics (fluid power, . . . ) Lockheed: System design & analysis integration NASA: Enabling technology (Sys. ML, . . . ) NIST: Design-analysis interoperability (DAI) TRW Automotive: DAI/FEA (steering wheel systems. . . ) 98

Selected GIT MBX/Sys. ML-Related Publications Some references are available online at http: //www. pslm. Selected GIT MBX/Sys. ML-Related Publications Some references are available online at http: //www. pslm. gatech. edu/topics/sysml/. See additional slides for selected abstracts. http: //www. pslm. gatech. edu/topics/sysml/. • Peak RS, Burkhart RM, Friedenthal SA, Paredis CJJ, Mc. Ginnis LF (2008) Integrating Design with Simulation & Analysis Using Sys. ML—Mechatronics/Interoperability • • Team Status Report. Presentation to INCOSE MBSE Challenge Team, Utrecht, Holland. [Overviews modeling & simulation interoperability (MSI) methodology progress in the context of an excavator testbed. ] http: //eislab. gatech. edu/pubs/seminars-etc/2008 -06 -incose-is-mbse-mechatronics-msi-peak/ Mc. Ginnis, Leon F. , "IC Factory Design: The Next Generation, " e-Manufacturing Symposium, Taipei, Taiwan, June 13, 2007. [Presents the concept of model-based fab design, and how Sys. ML can enable integrated simulation. ] Kwon, Ky Sang, and Leon F. Mc. Ginnis, "Sys. ML-based Simulation Framework for Semiconductor Manufacturing, " IEEE CASE Conference, Scottsdale, AZ, September 22 -25, 2007. [Presents some technical details on the use of Sys. ML to create formal generic models (user libraries) of fab structure, and how these formal models can be combined with currently available data sources to automatically generate simulation models. ] Huang, Edward, Ramamurthy, Randeep, and Leon F. Mc. Ginnis, "System and Simulation Modeling Using Sys. ML, " 2007 Winter Simulation Conference, Washington, DC. [Presents some technical details on the use of Sys. ML to create formal generic models (user libraries) of fab structure, and how these formal models can be combined with currently available data sources to automatically generate simulation models. ] Mc. Ginnis, Leon F. , Edward Huang, Ky Sang Kwon, Randeep Ramamurthy, Kan Wu, "Real CAD for Facilities, " 2007 IERC, Nashville, TN. [Presents concept of using Factory. CAD as a layout authoring tool and integrating it, via Sys. ML with e. M-Plant for automated fab simulation model generation. ] • T. A. Johnson, J. M. Jobe, C. J. J. Paredis, and R. Burkhart "Modeling Continuous System Dynamics in Sys. ML, " in Proceedings of the 2007 ASME International Mechanical Engineering Congress and Exposition, paper no. IMECE 2007 -42754, Seattle, WA, November 11 -15, 2007. [Describes how continuous dynamics models can be represented in Sys. ML. The approach is based on the continuous dynamics language Modelica. ] • T. A. Johnson, C. J. J. Paredis, and R. Burkhart "Integrating Models and Simulations of Continuous Dynamics into Sys. ML, " in Proceedings of the 6 th International Modelica Conference, March 3 -4, 2008. [Describes how continuous dynamics models and simulations can be used in the context of engineering systems design within Sys. ML. The design of a car suspension modeled as a mass-spring-damper system is used as an illustration. ] • C. J. J. Paredis "Research in Systems Design: Designing the Design Process, " IDETC/CIE 2007, Computers and Information in Engineering Conference -- Workshop on Model-Based Systems Development, Las Vegas, NV, September 4, 2007. [Presents relationship between Sys. ML and the multi-aspect component model method. ] • Peak RS, Burkhart RM, Friedenthal SA, Wilson MW, Bajaj M, Kim I (2007) Simulation-Based Design Using Sys. ML—Part 1: A Parametrics Primer. INCOSE Intl. Symposium, San Diego. [Provides tutorial-like introduction to Sys. ML parametrics. ] • Peak RS, Burkhart RM, Friedenthal SA, Wilson MW, Bajaj M, Kim I (2007) Simulation-Based Design Using Sys. ML—Part 2: Celebrating Diversity by Example. INCOSE Intl. Symposium, San Diego. [Provides tutorial-like introduction on using Sys. ML for modeling & simulation, including the MRA method for creating parametric simulation templates that are connected to design models. ] • Peak RS (2007) Leveraging Templates & Processes with Sys. ML. Invited Presentation. Developing a Design/Simulation Framework: A Workshop with CPDA's Design and Simulation Council, Atlanta. [Includes applications to automotive steering wheel systems and FEA simulation templates. ] http: //eislab. gatech. edu/pubs/conferences/2007 -cpda-dsfw-peak/ • Bajaj M, Peak RS, Paredis CJJ (2007) Knowledge Composition for Efficient Analysis Problem Formulation, Part 1: Motivation and Requirements. DETC 2007 -35049, Proc ASME CIE Intl Conf, Las Vegas. [Introduces the knowledge composition method (KCM), which addresses design-simulation integration for variable topology problems. ] • Bajaj M, Peak RS, Paredis CJJ (2007) Knowledge Composition for Efficient Analysis Problem Formulation, Part 2: Approach and Analysis Meta-Model. DETC 200735050, Proc ASME CIE Intl Conf, Las Vegas. [Elaborates on the KCM approach, including work towards next-generation analysis/simulation building blocks (ABBs/SBBs). ] 99

Publications (cont. ) • Shah AA, Schaefer D, Paredis CJJ (2009) Enabling Multi-View Modeling Publications (cont. ) • Shah AA, Schaefer D, Paredis CJJ (2009) Enabling Multi-View Modeling with Sys. ML Profiles and Model Transformations. International Conference on Product Lifecycle Management, Bath, UK. • Kerzhner AA, Paredis CJJ (2009) Using Domain Specific Languages to Capture Design Synthesis Knowledge for Model-Based Systems Engineering. Proceedings of the ASME 2009 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, San Diego, CA, DETC 2009 -87286. • J. M. Jobe, T. A. Johnson and C. J. J. Paredis, “Multi-Aspect Component Models: A Framework for Model Reuse in Sys. ML, ” in Proceedings of IDETC/CIE 2008, paper no. DETC 2008– 49339, Brooklyn, NY, 2008. • W. Schamai, P. Fritzson, C. Paredis and A. Pop, "Towards Unified System Modeling and Simulation with Modelica. ML: Modeling of Executable Behavior Using Graphical Notations, " Proceedings of the 7 th International Modelica Conference, pp. 612 -621, Como, Italy, 20 -22 September, 2009. 100

Integrating Design with Simulation & Analysis Using Sys. ML— Mechatronics/Interoperability Team Status Report Abstract Integrating Design with Simulation & Analysis Using Sys. ML— Mechatronics/Interoperability Team Status Report Abstract This presentation overviews work-in-progress experiences and lessons learned from an excavator testbed that interconnects simulation models with associated diverse system models, design models, and manufacturing models. The goal is to enable advanced model-based systems engineering (MBSE) in particular and model-based X 1 (MBX) in general. Our method employs Sys. ML as the primary technology to achieve multi-level multi-fidelity interoperability, while at the same time leveraging conventional modeling & simulation tools including mechanical CAD, factory CAD, spreadsheets, math solvers, finite element analysis (FEA), discrete event solvers, and optimization tools. This work is currently sponsored by several organizations (including Deere and Lockheed) and is part of the Mechatronics & Interoperability Team in the INCOSE MBSE Challenge. Citation Peak RS, Burkhart RM, Friedenthal SA, Paredis CJJ, Mc. Ginnis LF (2008) Integrating Design with Simulation & Analysis Using Sys. ML—Mechatronics/Interoperability Team Status Report. Presentation to INCOSE MBSE Challenge Team, Utrecht, Holland. http: //eislab. gatech. edu/pubs/seminars-etc/2008 -06 -incose-is-mbse-mechatronics-msi-peak/ [1] The X in MBX includes engineering (MBE), manufacturing (MBM), and potentially other scopes and contexts such as model-based enterprises (MBE). 101

Simulation-Based Design Using Sys. ML Part 1: A Parametrics Primer Part 2: Celebrating Diversity Simulation-Based Design Using Sys. ML Part 1: A Parametrics Primer Part 2: Celebrating Diversity by Example OMG Sys. ML™ is a modeling language for specifying, analyzing, designing, and verifying complex systems. It is a general-purpose graphical modeling language with computer-sensible semantics. This Part 1 paper and its Part 2 companion show Sys. ML supports simulation-based design (SBD) via tutorial-like examples. Our target audience is end users wanting to learn about Sys. ML parametrics in general and its applications to engineering design and analysis in particular. We include background on the development of Sys. ML parametrics that may also be useful for other stakeholders (e. g, vendors and researchers). In Part 1 we walk through models of simple objects that progressively introduce Sys. ML parametrics concepts. To enhance understanding by comparison and contrast, we present corresponding models based on composable objects (COBs). The COB knowledge representation has provided a conceptual foundation for Sys. ML parametrics, including executability and validation. We end with sample analysis building blocks (ABBs) from mechanics of materials showing how Sys. ML captures engineering knowledge in a reusable form. Part 2 employs these ABBs in a high diversity mechanical example that integrates computer-aided design and engineering analysis (CAD/CAE). The object and constraint graph concepts embodied in Sys. ML parametrics and COBs provide modular analysis capabilities based on multi -directional constraints. These concepts and capabilities provide a semantically rich way to organize and reuse the complex relations and properties that characterize SBD models. Representing relations as noncausal constraints, which generally accept any valid combination of inputs and outputs, enhances modeling flexibility and expressiveness. We envision Sys. ML becoming a unifying representation of domain-specific engineering analysis models that include fine-grain associativity with other domain- and system-level models, ultimately providing fundamental capabilities for next-generation systems lifecycle management. These two companion papers present foundational principles of parametrics in OMG Sys. ML™ and their application to simulation-based design. Parametrics capabilities have been included in Sys. ML to support integrating engineering analysis with system requirements, behavior, and structure models. This Part 2 paper walks through Sys. ML models for a benchmark tutorial on analysis templates utilizing an airframe system component called a flap linkage. This example highlights how engineering analysis models, such as stress models, are captured in Sys. ML, and then executed by external tools including math solvers and finite element analysis solvers. We summarize the multi-representation architecture (MRA) method and how its simulation knowledge patterns support computing environments having a diversity of analysis fidelities, physical behaviors, solution methods, and CAD/CAE tools. Sys. ML and composable object (COB) techniques described in Part 1 together provide the MRA with graphical modeling languages, executable parametrics, and reusable, modular, multidirectional capabilities. We also demonstrate additional Sys. ML modeling concepts, including packages, building block libraries, and requirements-verification-simulation interrelationships. Results indicate that Sys. ML offers significant promise as a unifying language for a variety of models-from top-level system models to discipline-specific leaf-level models. Citation Peak RS, Burkhart RM, Friedenthal SA, Wilson MW, Bajaj M, Kim I (2007) Simulation-Based Design Using Sys. ML. INCOSE Intl. Symposium, San Diego. Part 1: A Parametrics Primer http: //eislab. gatech. edu/pubs/conferences/2007 -incose-is-1 -peak-primer/ Part 2: Celebrating Diversity by Example http: //eislab. gatech. edu/pubs/conferences/2007 -incose-is-2 -peak-diversity/ 102

Composable Objects (COB) Requirements & Objectives Abstract This document formulates a vision for advanced Composable Objects (COB) Requirements & Objectives Abstract This document formulates a vision for advanced collaborative engineering environments (CEEs) to aid in the design, simulation and configuration management of complex engineering systems. Based on inputs from experienced Systems Engineers and technologists from various industries and government agencies, it identifies the current major challenges and pain points of Collaborative Engineering. Each of these challenges and pain points are mapped into desired capabilities of an envisioned CEE System that will address them. Next, we present a CEE methodology that embodies these capabilities. We overview work done to date by GIT on the composable object (COB) knowledge representation as a basis for next-generation CEE systems. This methodology leverages the multi-representation architecture (MRA) for simulation templates, the user-oriented Sys. ML standard for system modeling, and standards like STEP AP 233 (ISO 10303 -233) for enhanced interoperability. Finally, we present COB representation requirements in the context of this CEE methodology. In this current project and subsequent phases we are striving to fulfill these requirements as we develop next-generation COB capabilities. Citation DR Tamburini, RS Peak, CJ Paredis, et al. (2005) Composable Objects (COB) Requirements & Objectives v 1. 0. Technical Report, Georgia Tech, Atlanta. http: //eislab. gatech. edu/projects/nasa-ngcobs/ Associated Project The Composable Object (COB) Knowledge Representation: Enabling Advanced Collaborative Engineering Environments (CEEs). http: //eislab. gatech. edu/projects/nasa-ngcobs/ 103

Leveraging Simulation Templates & Processes with Sys. ML Applications to CAD-FEA Interoperability Abstract Sys. Leveraging Simulation Templates & Processes with Sys. ML Applications to CAD-FEA Interoperability Abstract Sys. ML holds the promise of leveraging generic templates and processes across design and simulation. Russell Peak joins us to give an update on the latest efforts at Georgia Tech to apply this approach in various domains, including specific examples with a top-tier automotive supplier. Learn how you too may join this project and implement a similar effort within your own company to enhance modularity and reusability through a unified method that links diverse models. Russell will also highlight Sys. ML’s parametrics capabilities and usage for physics-based analysis, including integrated CAD-CAE and simulation-based requirements verification. Go to www. omgsysml. org for background on Sys. ML—a graphical modeling language based on UML 2 for specifying, designing, analyzing, and verifying complex systems. Speaker Biosketch Russell S. Peak focuses on knowledge representations that enable complex system interoperability and simulation automation. He originated composable objects (COBs), the multi-representation architecture (MRA) for CAD-CAE interoperability, and context-based analysis models (CBAMs)—a simulation template knowledge pattern that explicitly captures design-analysis associativity. This work has provided the conceptual foundation for Sys. ML parametrics and its validation. He teaches this and related material, and is principal investigator on numerous research projects with sponsors including Boeing, Do. D, IBM, NASA, NIST, Rockwell Collins, Shinko Electric, and TRW Automotive. Dr. Peak joined the GIT research faculty in 1996 to create and lead a design-analysis interoperability thrust area. Prior experience includes business phone design at Bell Laboratories and design-analysis integration exploration as a Visiting Researcher at Hitachi in Japan. Citation RS Peak (2007) Leveraging Simulation Templates & Processes with Sys. ML: Applications to CAD-FEA Interoperability. Developing a Design/Simulation Framework, CPDA Workshop, Atlanta. http: //eislab. gatech. edu/pubs/conferences/2007 -cpda-dsfw-peak/ 104

Managing “Model DNA” Using Sys. ML Parametrics Panorama Tool by Andy Scott (Undergrad Research Managing “Model DNA” Using Sys. ML Parametrics Panorama Tool by Andy Scott (Undergrad Research Asst. ) and Russell Peak (Director, Modeling & Simulation Lab) 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 __a__ 2. 2 -spring physics model b. Mini Snowman f. ? __e__ 3. 3 -year company financial model __c__ 4. UAV road scanning system model __b__ 5. Car gas mileage model __d__ 6. Airframe mechanical part model c. Snowflake g. Robot __f __ 7. Design verification model (automated test for two Item 6. designs) [answers shown above] d. Mouse www. msl. gatech. edu 105