June 15 2008 INCOSE IS 08 Utrecht ModelBased

June 15, 2008 INCOSE IS 08 Utrecht Model-Based Systems Engineering (MBSE) Challenge Team Status Update Mechatronics / Model Interoperability Presenter Russell Peak - Georgia Tech Team Leaders Russell Peak, Roger Burkhart, Sandy Friedenthal, Chris Paredis, Leon Mc. Ginnis v 3 - 2008 -08 -01: Based on IS 08 MBSE presentations at Utrecht and ESTEC plus updates for July 23 JPL seminar. Note: Hidden slides (not presented during the seminar) are included here for context. Portions are Copyright © 2008 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

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). Page 2

Seminar at JPL July 23, 2008 Pasadena MBSE/MBX Experiences in an Excavator Testbed Enhancing Modeling & Simulation Interoperability Using Sys. ML Russell Peak and Chris Paredis Georgia Tech Based on IS 08 MBSE presentation plus updates (see cover slide). Note: Hidden slides (not presented during the seminar) are included here for context. 3

Collaboration Approach Primary Current Team • 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 (see next slide) • Lockheed Martin – Sandy Friedenthal Page 4

GIT Product & Systems Lifecycle Management Center Leveraging Related Efforts www. pslm. gatech. edu • Sys. ML-related projects: – Deere, Lockheed, Boeing, NASA, NIST, TRW Automotive, . . . • Other efforts based at GIT: – NSF Center for Compact & Efficient Fluid Power – Sys. ML course development • For Professional Masters in SE program, continuing ed. short course, . . . – Other groups & labs – Vendor collaboration (tool licenses, support, . . . ) • Consortia & other GIT involvements: – – INCOSE Model-Based Systems Engineering (MBSE) effort NIST SE Tool Interoperability Plug-Fest OMG (Sys. ML, . . . ) PDES Inc. (APs 210, 233, . . . ) • Commercialization efforts: – www. Venture. Lab. gatech. edu-based spin-off company (Inter. CAX): Productionizing tools for executable Sys. ML parametrics 5

Contents • Problem Description – Characteristics of Mechatronic Systems – Challenge Team Objectives • Technical Approach – Techniques and Testbeds • Deliverables & Outcomes • Collaboration Approach Page 6

Characterizing Mechatronics From Rennselaer Mechatronics Web Site Page 7

Mechatronics Architecture Software Interface • Displays • User Controls • Haptics • Remote Links • . . . • Functions • Operating Modes • State Machines • Control Systems • . . . • Modules, Libraries • Messages • Protocols • Code • . . . Electronic Control Unit (ECU) Actuators Sensors Communications Bus “Mechanical System” • Kinematics & Dynamics • Powertrain • Thermal • Fluids • Electric Power • . . . Electronics Feedback Control Loop Page 8

MBSE Challenge Team Objectives Phase 1: 2007 -2008 Overall Objectives • Define & demonstrate capabilities to achieve 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 Objectives to date primarily based on projects in GIT PSLM Center sponsored by industry and government—see backup slides. Page 10

MBSE Challenge Team Objectives Phase 1: 2007 -2008 Specific Objectives 1. Define modeling & simulation interoperability (MSI) method 2. Define Sys. ML and tool requirements to support MSI 1. Provide feedback to vendors and OMG Sys. ML 1. 1 revision task force 3. Demonstrate MSI method with 3+ engineering analysis and dynamic simulation model types 1. Include representative building block library: fluid power 2. Include hybrid discrete/continuous systems described by differential algebraic equations (DAEs) 4. Develop roadmap beyond Phase 1 Page 11

Method Objectives 12

Contents • Problem Description – Characteristics of Mechatronic Systems – Challenge Team Objectives • Technical Approach – Techniques and Testbeds • Deliverables & Outcomes • Collaboration Approach Page 13

Overall Technical Approach • Technique Development – “Federated system model” framework technology • A. k. a. collective product model – Modeling & simulation interoperability (MSI) method – Graph transformation technology – etc. • Testbed Implementations & Execution • Iteration Page 14

Technical Approach—Subset • Standards-based framework technology – Federated system models – Utilize Sys. ML where appropriate (esp. parametrics) • Modeling & simulation interoperability (MSI) method – Harmonize, generalize, extend new & existing work – COBs, CPM, KCM, MACM, MRA, OOSEM, . . . • Testbeds – – Develop and test techniques iteratively Implement test cases for verification & validation Produce reference examples Produce open resources (e. g. , Sys. ML-based fluid power libraries) Page 15

Technical Approach—Subset • Standards-based framework technology – Federated system models – Utilize Sys. ML where appropriate (esp. parametrics) • Modeling & simulation interoperability (MSI) method – Harmonize, generalize, extend new & existing work – COBs/Sys. ML, CPM, KCM, MACM, MRA, OOSEM, . . . • Testbeds – – Develop and test techniques iteratively Implement test cases for verification & validation Produce reference examples Produce open resources (e. g. , Sys. ML-based fluid power libraries) Page 19

The Four Pillars of Sys. ML 1. Structure 2. Behavior interaction state machine activity/ function definition use 3. Requirements 4. Parametrics Page 20

Sys. ML Technology Status www. omgsysml. org • Spec v 1. 0 - 2007 -09 v 1. 1 - 2008 -06 v 2. x - RFI expected 2008 -12 v 1. 2 - WIP • Vendor support • Learning infrastructure – Books, vendor courses, academic courses, INCOSE/OMG tutorial, public examples, etc. • Emerging production usage – http: //www. pslm. gatech. edu/events/frontiers 2008/ Page 21

“Wiring Together” Diverse Models via Sys. ML Level 1: Intra-Template Diversity CAE model (FEA) Mechanical CAD model Symbolic math models [Peak et. al 2007] 22

“Wiring Together” Diverse Models via Sys. ML Level 2: Inter-Template Diversity (per MIM 0. 1) Naval Systems-of-Systems (So. S) Panorama—An Envisioned Complex Model Interoperability Problem Enabled by Sys. ML/MIM/COBs 24

Technical Approach—Subset • Standards-based framework technology – Federated system models – Utilize Sys. ML where appropriate (esp. parametrics) • Modeling & simulation interoperability (MSI) method – Harmonize, generalize, extend new & existing work – COBs, CPM, KCM, MACM, MRA, OOSEM, . . . • Testbeds – – Develop and test techniques iteratively Implement test cases for verification & validation Produce reference examples Produce open resources (e. g. , Sys. ML-based fluid power libraries) Page 25

Excavator Modeling & Simulation Testbed Tool Categories View 26

Excavator Modeling & Simulation Testbed Interoperability Patterns View (MSI Panorama per MIM 0. 1) 27

Progress to Date: June 2008 (page 1/2) Phase 1 Report Availability: Sept. 2008 • Sys. ML authoring tools selection and operation (Embedded. Plus/Rational, Magic. Draw) • Excavator as testbed problem – Demo scenario: dig capacity trade study • Preliminary modeling & simulation interoperability (MSI) method: MIM 1. 0 – Harmonizing system design & analysis models integration methods • Test suites for [topic] development/demonstration/V&V using Sys. ML – Idealized mass-spring-damper [continuous dynamics] – Mechanical linkage [MSI method - mechanical benchmark] • Technique development – Graph transformation approach • Masters thesis completed [Johnson, 2008] – Interoperability via Sys. ML parametrics • Knowledge patterns, tool wrapping, . . . – Design BOM - mfg BOM interoperability via Sys. ML 28

Progress to Date: June 2008 (page 2/2) Phase 1 Report Availability: Sept. 2008 • Testbed environment – Dig cycle simulation (Modelica/Dymola) – CAD/CAE tools, engineering analysis, solvers (NX, Ansys, Mathematica) – Factory design & simulation (Factory CAD, e. M-Plant) – Spreadsheet interface (MS Excel) – Optimizer (Model. Center) • Overall status – – Sys. ML model developed for core interoperability structure Most individual models developed, plus 50% interconnected Most prototype interfaces operational & unit tested Phase 1 report drafted • Remaining work – Completing models & interconnections to support demo scenario – Completing Phase 1 report & archive of models 29

Demo Scenario • New market-driven targets: – 20% increase in dig rate (dirt volume / time) – 15% increase in mfg. production • Check if existing design is sufficient by re-running Sys. ML-enabled simulations • If not, explore re-design trade space – Changes in bucket size, hydraulics, . . . • Re-do V&V using simulations on new design • Explore manufacturing impact – Factory re-design and simulation 30

Excavator Modeling & Simulation Testbed Tool Categories View [WIP models] 31

Excavator Operational Domain Top-Level Context Diagram 32

Excavator Operational Domain First Level of Detail—bdd (Sys. ML block definition diagram) 33

Excavator Operational Domain First Level of Detail—ibd (Sys. ML internal block diagram) 34

Excavator Operational Domain Top-Level Use Cases 35

Excavator Dig Cycle Activity Diagram 36

Excavator Requirements & Objectives req - Sys. ML Requirements Diagram 37

System Objective Function—Excavator Context: Operational Enterprise Mathematical Form Sys. ML Parametrics Form 38

Excavator Test Case Selected System Breakdowns 39

Excavator Modeling & Simulation Testbed Tool Categories View 40

Hydraulic Circuit Diagram Pressure-Compensated, Load-Sensing Excavator—ISO 1219 notation 43

Sys. ML Schematic (ibd) — Basic View Pressure-Compensated, Load-Sensing Excavator 44

Sys. ML Schematic (ibd) — Detailed View Pressure-Compensated, Load-Sensing Excavator 45

Hydraulics Subsystem Simulation Model bdd 47

Excavator Case Study Native Tool Models: Modelica Hydraulics Model Multi-Body System Dynamics Model (linkages, . . . ) Dig Cycle hydraulics environment y world p_amb = 101325 T_amb = 288. 15 x 48

Simulation in Dymola Simulation Results Modelica Lexical Representation (auto-generated from Sys. ML) [Johnson, 2008 - Masters Thesis] 49

Excavator Modeling & Simulation Testbed Tool Categories View 50

Wrap Dynamic Simulation as Model. Center Model in Sys. ML Fully qualified name points to Model. Center model Stereotypes define input/output causality 51

DOE Model in Sys. ML Latin. Hyper. Cube sampler Reference Property Model 52

Automatic Export to and Execution in Model. Center 53

Application in Case Study: Optimization under uncertainty with kriging model optimizer Latin Hypercube + Kriging response surface • Optimization under uncertainty • Latin. Hyper. Cube sampler used to predict expected value • Kriging model used in conjunction with sampler to generate response surface to reduce computational cost Objectives: • Maximize Efficiency • Minimize Cost Design variables: • bore diameters 54

Sys. ML model Optimization under uncertainty with kriging model 55

Excavator Modeling & Simulation Testbed Tool Categories View 56

Excavator Modeling & Simulation Environment Interoperability Patterns View (MSI Panorama per MIM 0. 1) 57

Factory & Manufacturing Process Modeling & Simulation Using Sys. ML [Mc. Ginnis et al. 2007] Sys. ML State Diagram Sys. ML Sequence XML Parser Diagram Discrete Event Simulation 58

Excavator Modeling & Simulation Testbed Tool Categories View 61

MCAD-Sys. ML Interface Scenarios UGS/Siemens NX RSD/E+ Sys. ML Model Import User Sys. ML Model Manipulation Simulation Execution* Model Changes Propagate to CAD Tool Parametrics Execution Xai. Tools COB Services Georgia Tech Xai. Tools™ Engineering Analysis Models * = work-in-process 62

MCAD Native Model and Tool UIs UGS/Siemens NX 63

MCAD Model (Subset) in Sys. ML RSD/E+ 64

Interfacing Spreadsheets with Sys. ML Parametrics 65

Excavator Modeling & Simulation Testbed Tool Categories View 66

Enabling Executable Sys. ML Parametrics Commercialization by Inter. CAX LLC in Georgia Tech Venture. Lab incubator program Advanced technology for graph management and solver access via web services. Plugins Prototyped by GIT (to Sys. ML vendor tools) 1) Artisan Studio [2/06] 2) Embedded. Plus [3/07] 3) No. Magic [12/07] Next. Generation Spreadsheet Parametrics plugin 2008 -05 Status - Examples working from IS 07 Parts 1 & 2 papers - Multiple new tutorials: UAVs, finances, insurance claims, comm systems, . . . - Commercialization beta releases soon COB API Execution via API messages or exchange files COB Services (constraint graph manager, including COTS solver access via web services) Composable Objects (COBs) . . . Native Tools Models . . . Ansys (FEA Solver) . . . COTS = commercial-off-the-shelf (typically readily available) Mathematica (Math Solver) Xai. Tools Sys. ML Toolkit™ COB Solving & Browsing Xai. Tools Frame. Work™ Sys. ML Authoring Tools Traditional COTS or in-house solvers 74

Productionizing/Deploying GIT Xai. Tools™ Technology for Executing Sys. ML Parametrics www. Inter. CAX. com Vendor Sys. ML Tool Prototype by GIT Product by Inter. CAX LLC Artisan Studio Yes <tbd> Embedded. Plus E+ Sys. ML / RSA Yes <tbd> No Magic. Draw Yes Para. Magic™ (Jul 21, 2008 release) Telelogic/IBM Rhapsody/Tau <tbd> Sparx Systems Enterprise Arch. <tbd> n/a XMI import/export Yes <tbd> 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 is being fostered by the GIT Venture. Lab incubator program ( www. venturelab. gatech. edu) via an Inter. CAX Venture. Lab project initiated October 2007. 75

Various Examples • Road scanning system using unmanned aerial vehicle (UAVs) • . . . • Mechanical part design and analysis (FEA) • . . . • Insurance claims processing and website capacity model • Financial model for small businesses • Banking service levels model • . . . 76

UAV System Design Problem: Little. Eye Network-Centric Warfare Context — Sys. ML/Do. DAF Model Source: No Magic Inc. and Inter. CAX LLC 77

Road Scanner System Problem Little. Eye UAV 80

Little. Eye Sys. ML Model Various Diagram Views 81

Solving Little. Eye Sys. ML Parametrics Para. Magic Browser Views Instance 1 - Before Solving Instance 1 - After Solving 82

Financial Projections System Three Year Corporate Financial Projections • Key questions: – Given projected sales, expenses and financing, what is the financial position of the company at the end of 3 years? – Given the desired financial position at the end of 3 years, what are the required sales, expenses and financing? –… 83

Financial Projections Sys. ML Model Various Diagram Views 84

Solving Financial Projections Sys. ML Parametrics Para. Magic Browser Views Instance 1 - Before Solving Instance 1 - After Solving 85

Using a Spectrum of Modeling Technologies • • • Mental calculations Back-of-envelope hand calculations Spreadsheets. . . Sys. ML (with executable parametrics). . . • Varying characteristics – Quickness, flexibility, structure, modularity, reusability, self-validation/constraints, . . . 86

Excavator Modeling & Simulation Testbed Tool Categories View 87

Recurring Problem: Maintaining Multiple Views • Multiple stakeholders with different views and tools • Models of different system aspects • Different views are not independent System Design Model Aspect A Models Aspect B Models 88

Approach: Graph Transformations • Recent developments in Model-Driven Engineering • Tools for Model and Graph Transformations – Viatra – GME/GRe. AT – Fujaba – MOFLON – Mo. T – Kermeta 89

Capturing Domain Specific Knowledge in Graph Transformations Requirements & Objectives Sys. ML system alternative Topology Generation using Graph Transf System Alternatives MAs. Co. Ms Sys. ML Model Composition using Graph Transf System Behavior Sys. ML Models Model Translation using Graph Transf Executable Simulations behavior model simulation configuration Dymola Simulation Configuration using Graph Transf Design Optimization Model. Center 101

Graph Transformations for Systems Design • Capture complex knowledge – Language mappings – Abstractions and idealizations – Analysis patterns – Synthesis patterns – Workflow • Intuitive graphical formalism • Powerful tools are maturing 102

Contents • Problem Description – Characteristics of Mechatronic Systems – Challenge Team Objectives • Technical Approach – Techniques and Testbeds • Deliverables & Outcomes • Collaboration Approach Page 103

Expected Deliverables & Outcomes Phase 1 (Sept. 2008) • Solution and supporting models – Excavator test case models, test suites, … • MBSE practices used – Modeling & simulation interoperability (MSI) method, … • Model interchange capabilities – Tests between Sys. ML tools, CAD/CAE tools, … • MBSE metrics/value – See “Benefits” slide with candidate metrics • MBSE findings, issues, & recommendations – Issue submissions to OMG and vendors, publications, … • Training material – Examples, tutorials, … • Plan forward (Phase 2 and beyond) Page 104

Primary Reporting Venues • Call for Participation @ IS’ 07 – Jun 26, 2007 in San Diego • Phase 1 Status Update @ IW’ 08 MBSE Workshop #2 – Jan 25, 2008 in Albuquerque • Phase 1 Status Update @ Frontiers Workshop – May 14, 2008 in Atlanta • Phase 1 Status Update @ IS’ 08 – Jun 15 -19, 2008 in Utrecht • Phase 1 Final Report & Archive of Models – Sep 2008 via website • Misc. reports/updates/publications @ various venues – OMG meetings, society & vendor conferences, . . . Page 105

Phase 1 Report • Draft version: June 2008 – ~100+ pages – ~75+ figures • Final version: Sept. 2008 Page 106

MBSE Challenge Team Objectives Phase 2: 2008 -2009 (proposed—pending resources) Overall Objectives • Refine & extend beyond Phase 1 capabilities for modeling & simulation interoperability (MSI) • Phase 2 Scope – Domains: Primary: Mechatronics (expanded excavator testbed) Secondary: Others to demo reusability – Capabilities: Methodologies, tools, requirements, and practical applications – MSI subset: Connecting system specification & design models with multiple engineering analysis – Deployment: Productionizing techniques & tools to enable ubiquitous practice • Advance & demo how Sys. ML facilitates effective MSI Page 107

MBSE Challenge Team Objectives Phase 2: 2008 -2009 Specific Objectives 1. Extend modeling & simulation interoperability method: MIM 2. 0 1. Generalizations: graph transformations, variable topology, reusability, parametrics 2. x, trade study support, inconsistency mgt. , E/MBOM extensions, method workflow, . . . 2. Specializations: software, electronics, closed-loop control, . . . 3. Interfaces to new tools: ECAD, Matlab, Arena, . . . 2. Refine Sys. ML and tool requirements to support MIM 2. 0 1. Provide feedback to vendors and OMG Sys. ML 1. 2/2. x task forces 3. Demonstrate extensions in updated testbed 4. Define deployment plan and initiate execution 5. Refine roadmap beyond Phase 2 Page 108

Potential Excavator Testbed Extension Building block modularity, reusability, adaptation, . . . Potential Space Systems Test Case #1 Phoenix Digs for Clues on Mars - Credit: Phoenix Mission Team, NASA, JPL-Caltech, U. Arizona, Texas A&M University What's a good recipe for preparing Martian soil? Start by filling your robot's scoop a bit less than half way. Next, dump your Martian soil into one of your TEGA ovens, being sure to watch out for clumping. Then, slowly increase the temperature to over 1000 degrees Celsius over several days. Keep checking to see when your soil becomes vaporized. Finally, your Martian soil is not ready for eating, but rather sniffing The above technique is being used by the Phoenix Lander that arrived on Mars three weeks ago. Data from the first batch of baked soil should be available in a few days. Pictured above, a circular array of the Phoenix Lander's solar panels are visible on the left, while a scoop partly filled with Martian soil is visible on the right. The robotic Phoenix Lander will spend much of the next three months digging, scooping, baking, sniffing, zapping, dissolving, and magnifying bits of Mars to help neighboring Earthlings learn more about the hydrologic and biologic possibilities of the sometimes mysterious red planet. [http: //antwrp. gsfc. nasa. gov/apod/ap 080615. html] 109

Potential Space Systems Test Case #2 Transform spreadsheet-based models into Sys. ML. . . (1) Sample 2 -Year Titan Orbital Mission Scenario http: //opfm. jpl. nasa. gov/community/opfminstrumentsworkshoppresentations/ 2008 -06 TSSM Orbiter Science Scenario, Rob Lock • Four (4) 6 -month cycles = eleven campaigns (instrument usage profiles during orbits) • Three (3) science campaign types; maintain each campaign for 16 days (one Titan revolution) (2) Atmosphere & Ionosphere Campaign Data & power timelines for key ~6. 5 -hour segment of 16 -day campaign 110

Modeling & Simulation Interoperability Anticipated Benefits of Sys. ML-based Approach Precision Knowledge for the Model-Based Enterprise 111

Contents • Problem Description – Characteristics of Mechatronic Systems – Challenge Team Objectives • Technical Approach – Techniques and Testbeds • Deliverables & Outcomes • Collaboration Approach Page 112

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] – Roger Burkhart [Burkhart. Roger. M @ John. Deere. com] – Sandy Friedenthal [sanford. friedenthal @ lmco. com] Page 113

Backup Slides Page 114

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. 115

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. . . ) 116

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. • 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). ] 117

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). 118

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/ 119

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/ 120

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/ 121

Mechatronics Definition “The synergistic combination of mechanical, electronic, and software engineering” (Wikipedia) System Modeling Mechanics Electronics Sensors Electromechanics CAD/CAM Control Circuits Mechatronics Digital Control Simulation Software Control Micro-controllers From Tamburini & Deren, PLM World ’ 06 http: //eislab. gatech. edu/pubs/conferences/2006 -plm-world-tamburini/ Page 122

Mechatronics—Open Technology for Modeling & Frameworks Systems Mechanics • Sys. ML • STEP AP 233 • Open Modelica • Domain-specific models • MCAD/CAE • STEP AP 203/214/209. . . • Part & subsystem models . . . Software • UML 2 • Real-time middleware • Communication protocols • Programming languages & libraries • Code generators • IDEs (Eclipse, . . . ). . . Electronics • ECAD/CAE • STEP AP 210 • Component models. . . Not shown: Cross-cutting infrastructure (PLM, CM, . . . ) Page 123

Modelica Multi-Discipline Models Page 124