1 Modeling & Simulation Interoperability (MSI) Challenge Team INCOSE MBSE Initiative Team Update @

1

Modeling & Simulation Interoperability (MSI)Challenge Team

INCOSE MBSE Initiative http://www.omgwiki.org/MBSE/doku.php?id=mbse:modsim

Team Update @ MBSE Workshop

Speakers: Russell Peak and Dirk Zwemer

30 January 2011

International Workshop28 Jan – 2 Feb 2011

Phoenix, AZ, USA

2

Modeling & Simulation Interoperability Team (MSI) Team Objectives

• Overall Objective: Advance how models interact together throughout the system lifecycle

• Key Sub-Objective: Better interconnect system specification & design models with diverse engineering analysis and simulation models– Ex. Interconnecting SysML-based system models with

traditional models: CAD, CAE, reliability, costing, programmatics, PLM, ...

Excavator Domain Models

MCAD Tools

Generic Math Solvers

Sys Dynamics Solvers

Excavator Sys-Level Models

Reliability Model

Cost Model

Optimization ModelObjectiveFunction

Dymola

Federated Excavator Model

Boom Mfg. Assembly Models

System & Req Tools

RSD/E+

MagicDraw

NX

Mathematica

Optimizers

Excel

ModelCenter

Discrete Event Solvers (Specialized)

eM-Plant / Factory Flow

c0. Context-SpecificSimulation Models

e0. Solver Resourcesa0. Descriptive Resources (Authoring Tools, ...)

d0. Simulation Building Block Libraries

Solid Mechanics

Queuing Concepts

Fluid Mechanics

CostConcepts

OptimizationConcepts

Reliability Concepts

Assembly Process Models

Discrete EventAssy Model

Dig Cycle Model

MM1 Queuing Assy Model

Boom Linkage Models

Stress/Deformation Models

Extensional Linkage Model

Plane Stress Linkage Model FEA Solvers

Ansys

Factory CAD Tools

FactoryCAD

b0. Federated Descriptive Models

Boom

Linkages

Hydraulics Subsystem

Factory Domain Models

Federated Factory Model

Operations

Req. & Objectives

...

Dig Site Dump Trucks

Data Mgt. Tools

Excel

Assembly Lines

Work CellsAGVs

Buffers Machines

Req. & Objectives

Excavator MBOM

2008-02-20

Tool &

native model interface (via X

aiTools, A

PIs, ...)1) T

he pattern names and identifiers used here conform

to HM

X 0.1 —

a method

under development for generalized system

-simulation interoperability (SSI).

2) All m

odels shown are SysM

L models unless otherw

ise noted.3) Infrastructure and m

iddleware tools are also present (but not show

n) --e.g., PLM

, CM

, parametric graph m

anagers (XaiT

ools etc.), repositories, etc.

Com

position relationship (usage)N

ative model relationship (via tool interface, stds., ...)

Parametric or algorithm

ic relationship (XaiT

ools, VIA

TR

A, ...)

Notes

Legend

Object1target location

rt1= 30”(anywhere on this circle)


rt2 = 30”(anywhere on this circle)

Object1start location


60 deg, 14”

45 deg, 12”

ra1 = ?ra2 = ?

Object2end location

Object1end location

3

Modeling & Simulation Interoperability Team (MSI) Team Members

http://www.omgwiki.org/MBSE/doku.php?id=mbse:modsim

4

Modeling & Simulation Interoperability Team (MSI) New Members & New Collaborations in 2010-2011

• Jeffery Banks (Northrop Grumman) – SysML parametrics modeling & simulation for information systems

using Rhapsody/Melody

• Bruce Beihoff (Whirlpool)– SysML applications for physics-based modeling

• Dirk Zwemer (InterCAX)– SysML parametrics applications (smart grid, supply chains, ...)

• Challenge Teams: Space Systems, Smart Grid– SysML interoperability with orbit simulation (AGI/STK)

– SysML parametrics-bsaed smart grid model

• Sandia– SysML interoperability with embedded systems simulation (Orchestra)

• Systems Engineering Research Center (SERC) UARC– RT21 VV&A project, RT24 Integrated M&S/DoDAF project

5

Contents

• SysML parametrics advances 2010-2011– 5-minute primer: fuel tank– Advanced modeling constructs: complex aggregates– Debugging and visualization: DNA signatures– Scalability testing & metrics– Expanding applications

• Smart grid modeling – D Zwemer (InterCAX)

• Information systems modeling – J Banks (NGC), FireSAT, biomedical, VV&A, ...

– SysML-LVC simulation interoperability example: STK – Expanding tool support and deployment

• Embedded systems simulation applications (with Sandia)

• Additional team progress– MBSE & manufacturing – SysML & DEVS – McGinnis et al.– SysML and optimization with ModelCenter – Paredis et al.– SysML-Modelica transformation spec – Paredis et al.– SERC RT21 Verification, Validation, and Accreditation project (VV&A) – Growing education opportunities (short courses, undergrad/grad courses, ...)

6SysML and MBSE: A Quick-Start CourseCopyright © Georgia Tech and InterCAX. All Rights Reserved.

SysML Parametrics Primer: Fuel_Tank block & instances on block definition diagram (bdd), parametrics diagram (par)

10.2 gal

5.5 gal

ft310 gauge

ft330 gauge

SysML parametrics diagramCapturing equation-based knowledge


Fuel_Tank parametrics executionParaMagic interoperating w/ equation solvers such as Mathematica

instance ft330 state 1.0 (before solving)

state 1.0 (before solving)

Given my current_amount, how full is my tank?

instance ft330 state 1.1 (after solving)


Fuel_Tank parametrics executionChanging input/output direction (causality) in the same instance

instance ft330 state 2.0 (after changing causalities, and before solving)

state 2.1 (after solving)

What current_amount will give me a tank that is half full?

instance ft330 state 2.1 (after solving)


Fuel_Tank “DNA signature”Interacting with equation graph structure via Panorama tool

Model DNA Signature of instance ft330

(flattened equation structure auto-generated from SysML)


Exercise 0: Automobile Fuel Capacity & MileageStage 3 Model (p1/3)



Example Instances (after solving)

Model DNA Signature



state 1.1 (after solving)

13

Contents






14

Complex AggregatesComplex AggregatesEnabling advanced scalable modelingEnabling advanced scalable modeling

object-oriented, multi-directional, object-oriented, multi-directional, multi-dimensional do-loops multi-dimensional do-loops

5n

using exact same structure model

n

iimassmass

1

n

iicostcost

1

15

Complex AggregatesComplex AggregatesEnabling advanced scalable modelingEnabling advanced scalable modeling

10n

object-oriented, multi-directional, object-oriented, multi-directional, multi-dimensional do-loops multi-dimensional do-loops

using exact same structure model

n

iimassmass

1

n

iicostcost

1

16

Contents







“DNA Signatures”Autogenerated from SysML parametrics

Model DNA Signature of instance ft330

(flattened equation structure auto-generated from SysML)

Updates 2010-2011- Complex and primitive aggregates- Animation- Hide/show based on SysML structure

18

Model “DNA Signatures” Using SysML ParametricsModel “DNA Signatures” Using SysML ParametricsPanorama Tool by Andy Scott (Undergrad Research Asst.) and Russell Peak (Director, Modeling & Simulation Lab)Panorama Tool by Andy Scott (Undergrad Research Asst.) and Russell Peak (Director, Modeling & Simulation Lab)Examples as of ~9/2009 — Low/Medium ComplexityExamples as of ~9/2009 — Low/Medium Complexity

b. Mini Snowman

a. Snowman

c. Snowflake

d. Mouse

g. Robot

f. ?

e. CactusTest: 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

_____ 3. 3-year company financial model

_____ 4. UAV road scanning system model

_____ 5. Car gas mileage model

_____ 6. Airframe mechanical part model

_____ 7. Design verification model (automated test for two Item 6. designs)

www.msl.gatech.edu

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

__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

__f__ 7. Design verification model (automated test for two Item 6. designs)

19

Recent Models: ~Medium ComplexityRecent Models: ~Medium Complexity2010-10 Model size = O(100s) equations, O(1000+) variables2010-10 Model size = O(100s) equations, O(1000+) variables

supply chain metrics

“Galaxy with Black Hole”

mfg. sustainability: airframe wing

“Tumbleweed”

electronics recycling network

mfg. sustainability: automotive transmissions

“Angler Fish”“Turtle Bird”

“Turtle”

20

Recent ModelsRecent Models: ~: ~Medium Complexity Medium Complexity F-86 Cast Wing Section [adapted from Bras, Romaniw, et al.] – p1/3F-86 Cast Wing Section [adapted from Bras, Romaniw, et al.] – p1/3

cast wing – total assembly(JoinNosesToSpar highlighted)

SysML parametrics stats

=== structural stats23 blocks218 value properties38 part properties0 reference properties0 shared properties12 complex aggregate properties0 primitive properties195 constraint properties - regular0 constraint properties - xfwExternal0 constraint properties - cMathematica

=== instance stats184 block instances1879 value property slots165 part property slots0 reference property slots0 shared property slots53 complex aggregate members0 primitive aggregate members346 constraint property eqns - regular0 constraint property eqns - xfwExternal0 constraint property eqns - cMathematica

21

Recent ModelsRecent Models: ~: ~Medium Complexity Medium Complexity F-86 Cast Wing Assembly [adapted from Bras, Romaniw, et al.] – p2/3F-86 Cast Wing Assembly [adapted from Bras, Romaniw, et al.] – p2/3

cast wing – JoinNosesToSpar(machine highlighted)

22

Requirements Verification in FireSat Requirements Verification in FireSat Sources: INCOSE SSWG and InterCAX LLC; Georgia Tech ASE 6006 NGDMCSources: INCOSE SSWG and InterCAX LLC; Georgia Tech ASE 6006 NGDMC

23

Req. VerificationReq. Verificationin FireSat SysML modelin FireSat SysML model(including operational costs, etc.)(including operational costs, etc.)

“DNA signature” auto-generated from SysML parametrics model

Model source: [email protected]

24

Contents






25

Smart Grid SysML Model

Dirk ZwemerINCOSE Smart Grid Challenge Team and

Modeling & Simulation Interoperability [email protected]

Jan. 30, 2011MBSE Workshop INCOSE

IW2011

26

27

Smart Grid Model Summary

• Objective – simulate effect of “Smart Meters” on electricity consumption

• Tools - MagicDraw SysML, ParaMagic, Mathematica, MS Excel

• Metric - Total Daily Expense for all Users

28

SmartGrid Parametric ModelSmartGridSupplyDemand SmartGrid_Domain_BDD[Package] bdd [ ]

constraintsdsb1 : SmartGridSupplyDemand::Constraints::DemandSupplyBalance

valuesbalanceByPeriod : MW [1..*]

«block»Smart_Grid

valuestotalDailyExpense : USDtotalDemandByPeriod : MW [1..*]

«block»Customer_Domain

valuescostByPeriod : $/kW-hr [1..*]timePeriod : HrtotalSupplyByPeriod : MW [1..*]

«block»Bulk_Generation_Domain

valuescapacity : MWcost_Capacity : $/kWcost_Fixed : $M/yrcost_Ops : $/kW-hr [1..*]cost_Variable : $/kW-hrlifetime : Yrpower : MW [1..*]timePeriod : HrtotalPeriodCost : $M [1..*]

«block»Source

valuesbaseDemand : MW [1..*]dailyExpense : USDeffectiveDemand : MW [1..*]expenseByPeriod : USD [1..*]timePeriod : Hr

«block»Customer

valuespriceByPeriod : $/kW-hr [1..*]profitFactor : Real

«block»Operation_Domain ops_pricing

source 1..* custmr 1..*

gen_cost

gen_domain ops_domain cust_domain

29

Source SubtypesBulk_Generation_Domain_BDDBulk_Generation_Domain[Block] bdd [ ]

«block»Source

«block»Non_Renewable_Non_Variable_Source

«block»Renewable_Non_Variable_Source

«block»Renewable_Variable_Source

«block»Geothermal_Power

«block»Biomass_Power

«block»Nuclear_Power

«block»Pump_Storage

«block»Hydro_Power

«block»Solar_Power

«block»Wind_Power

«block»Tidal_Power

«block»Coal_Power

«block»Gas_Power

30

Customer SubtypesCustomer_Domain Customer_BDD[Block] bdd [ ]

constraintsdc1 : SmartGridSupplyDemand::Constraints::DailyCost

referencesops_pricing : Operation_Domain


«block»Customer

constraintsad1 : AdjustedDemanded1 : EffectiveDemandpc1 : SmartGridSupplyDemand::Constraints::PeriodCost

valuesadjDemand : MW [1..*]basePrice : $/kW-hrelasticity : Real

«block»Customer_SmartMeter

constraintspc2 : SmartGridSupplyDemand::Constraints::PeriodCost

«block»Customer_DumbMeter

31




«block»Smart_Grid






«block»Source


«block»Customer




gen_cost


32




«block»Smart_Grid






«block»Source


«block»Customer




gen_cost


For one to many power plants, output (MW) is defined over 24 hour period.

Cost model for each power plant is based on variable, fixed and capacity costs.

33




«block»Smart_Grid






«block»Source


«block»Customer




gen_cost


At Bulk Generation level, output supply is aggregated and weighted average cost is calculated.

34




«block»Smart_Grid






«block»Source


«block»Customer




gen_cost


At Operations level, cost and supply data are read and pricing signals are generated.

35




«block»Smart_Grid






«block»Source


«block»Customer




gen_cost


Customers with Smart Meters read pricing signals and shift demand pattern during day.

Demand shift obeys elasticity function.

36




«block»Smart_Grid






«block»Source


«block»Customer




gen_cost


Customer demand and daily expense is aggregated. Key metric is total daily expense.

37

Bulk GenerationCosts Power

Capacity Lifetime Variable Fixed Capacity Period 1 Period 2 Period 3 Period 4 Period 5 Period 6 Period 7 Period 8Name MW Years $/kW-hr $M/yr $/KW MW MW MW MW MW MW MW MWGas 50 25 0.02 1 500 5 5 5 5 5 7.5 10 10Nuclear 50 20 0.007 3 5000 25 25 25 25 25 25 25 25Solar 50 10 0.001 0.5 2000 1 1 1 1 1 5.5 10 20

Total 150 7500 31 31 31 31 31 38 45 55

38

Customer DemandBase Power DemandPrice Period 1 Period 2 Period 3 Period 4 Period 5 Period 6 Period 7 Period 8

Name Elasticity $/kW-hr MW MW MW MW MW MW MW MWFactory_1 3 0.035 5 5 5 7.5 10 15 20 30Neighborhood_1 1 0.035 10 10 10 10 10 25 40 30

Total 15 15 15 17.5 20 40 60 60

39

Results: SmartGrid vs DumbGrid

Daily Expense: SmartGrid $60,228 DumbGrid $66,477

40

Contents






41

Snowflake CompositionSnowflake CompositionFive (5) LevelsFive (5) Levels

Snowflake de Spring

42

Snowflakes de PhysicaSnowflakes de Physica

43

Recent Models: ~Medium ComplexityRecent Models: ~Medium Complexity2010-10 Model size = O(100s) equations, O(1000+) variables2010-10 Model size = O(100s) equations, O(1000+) variables

supply chain metrics

“Galaxy with Black Hole”

mfg. sustainability: airframe wing

“Tumbleweed”

electronics recycling network

mfg. sustainability: automotive transmissions

“Angler Fish”“Turtle Bird”

“Turtle”

WIP12K equations100K, 1M, ...

44

Contents






45

System M&S System M&S Examples in STKExamples in STK

Communications Link Simulation between Satellite and Ground Station

(a) Link with ground station at t=t1 (b) Link with ground station at t=t2(several orbits after t1)

(c) Link broken with ground station at t=t3(~10 minutes after t2)

Geo-positioning Model

Missile Launcher Model

Force-on-Force Fighter Simulation

(a) Normal model view

(b) Marker & trajectory history view

Based on original models by AGI.

46

Two-way interoperability SysML-STK (throughout simulation run-time)Two-way interoperability SysML-STK (throughout simulation run-time)- Changeable inputs (SysML to STK): satellite and ground station properties- Changeable inputs (SysML to STK): satellite and ground station properties- Results (STK to SysML ): duration of ea. link session with ea. ground station- Results (STK to SysML ): duration of ea. link session with ea. ground station

47

Initial prototype: Initial prototype: STK & SysML parametrics STK & SysML parametrics

(for req. verification, ...)(for req. verification, ...)

48

Contents







Productionizing/Deploying GIT XaiTools™ Technology for Executing SysML Parametrics

Tool Vendor

SysML AuthoringTools

Prototypes byGIT

Products by InterCAX LLC

Atego

(formerly Artisan)

Studio Yesc.2005

ParaSolver™ 1st release: 2010-3Q

EmbeddedPlus E+ SysML / RSA Yesc.2006

—

No Magic MagicDraw Yesc.2007

ParaMagic®

1st release: 2008-Jul-21

Telelogic/IBM Rhapsody — Melody™ 1st release: 2010-1Q

Sparx Systems Enterprise Architect — EA ParametricsComing 2011

n/a XMI import/export Yesc.2006

<tbd>

Others <tbd> Others <tbd> <tbd> <tbd>

www.InterCAX.com

[1] Full disclosure: InterCAX LLC is a spin-off company originally created to commercialize technology from RS Peak’s GIT group. GIT has licensed technology to InterCAX and has an equity stake in the company. RS Peak is one of several business partners in InterCAX. Commercialization of the SysML/composable object aspects has been fostered by the GIT VentureLab incubator program (www.venturelab.gatech.edu) via an InterCAX VentureLab project initiated October 2007.

http://www.intercax.com/

http://www.venturelab.gatech.edu/

50

Contents





• Embedded systems simulation applications (with Sandia)


51

SysML-Enabled Design & Simulation of Embedded Systems

• Exploring use of SysML as front-end design tool for embedded systems to support system simulation

• Collaborative effort between Sandia and InterCAX (funded by Sandia)

• Tools interoperating in proof-of-concept prototypes:– SysML authoring tool: MagicDraw (No Magic Inc.)– Embedded systems simulation tool: Orchestra (Sandia)– DSL/interface enabler: Maestro (InterCAX)

Orchestra POC Greg WickstromSandia National LaboratoriesPO Box 5800 MS0340Albuquerque, NM [email protected] (505) 844-7708

mailto:[email protected]

mailto:[email protected]

52

Test Case: Hypothetical MachineOriginal Document-based Design Capture (Visio)

53

Test Case: Hypothetical MachineAs captured in SysML — a rich, user-friendly, computer-sensible formulation — view1 (ibds)

54

Test Case: Hypothetical MachineAs captured in SysML — a rich, user-friendly, computer-sensible formulation — view2 (bdds)

55

Sample Resulting XML-based Interface ContentAutomatically transforming SysML-based design intent

into Orchestra simulation inputs

56

Sample Benefits

• Richer, more flexible/modular/reusable design capture vs. traditional Visio-based approach

• Automated transformation to support simulation• Enhanced consistency• Additional design views (at no-extra-charge ):

bdds, requirements, ...

57

Contents






58

MBSE & ManufacturingSysML-discrete event simulation interoperability (McGinnis et al.)

DSL + Model Transformation = 10x reduction in

simulation development time and effort

© 2010 Chris Paredis 59

Exploring System ArchitecturesUsing SysML and ModelCenterOptimization Interoperability (C Paredis et al.)

Given:– Component models– Objectives / preferences

Find:– Best system architecture– Best component parameters– Best controller

Given:– Component models– Objectives / preferences

Find:– Best system architecture– Best component parameters– Best controller

Excavator

pump_vdisp

cylinder

accum

How to connect and size these?

engine

v_3way

EngineAnalysisContext EngineAnalysisContext[Block] par [ ]

engineID omega torque

engine : Engine

feasibility

description : EngineTest

«ModelCenter»analysis : EngineAnalysis

id omega torque feasibility

Requirements[Model] req Data[ ]

Id = "1"Text = "The engine shall produce 15 Nm of torque "

«requirement»EngineRequirement

valuesengineIDomegatorque

«block»Engine

valuesfeasibilityrequiredTorque

«block»EngineTestengine

«satisfy» «verify»

60

An Overview of the SysML-ModelicaTransformation Specification

http://www.omgwiki.org/OMGSysML/doku.php?id=sysml-modelica:sysml_and_modelica_integration

Chris Paredis (Georgia Tech)

Y. Bernard (Airbus), R. Burkhart (Deere & Co),H. de Koning (ESA/ESTEC), S. Friedenthal (Lockheed Martin Corp.),

P. Fritzson (Linköping University), N. Rouquette (JPL),W. Schamai (EADS)

60Presentation for the INCOSE Symposium 2010 Chicago, IL USA

61

What is Modelica?(www.modelica.org)

State-of-the-art Modeling Languagefor System Dynamics– Differential Algebraic Equations (DAE)– Discrete Events

Formal, object-oriented language Standardized by the Modelica Association

– Open language specification – tool independent Multi-domain modeling Ports represent energy flow (undirected) or

signal flow (directed) Acausal, equation-based, declarative (f-m*a=0)

61

62

A Robot Example in Modelica

mot

or to

rque

63

SysML-Modelica Transformation Specification:Context & Objective

Two complementary languages for Systems Engineering:– Descriptive modeling in SysML– Formal equation-based modeling for

analyses and trade studies in Modelica

Objective:– Leverage the strengths of both SysML and Modelica by

integrating them to create a more expressive and formal MBSE language.

– Define a formal Transformation Specification: a SysML4Modelica profile a Modelica abstract syntax metamodel a mapping between Modelica and the profile

Presentation for the INCOSE Symposium 2010 Chicago, IL USA 63

64

SysML-Modelica Robot Example:Use Cases & Requirements

64

65

SysML-Modelica Robot Example:Analysis and Trade Study

65Analysis models depend on descriptive models

66

SysML4Modelica Analytical Model:Relation to Modelica Native Model

66

67

SysML-Modelica Robot Example:Modelica model with simulation results

67

68

Specification Adoption Timeline & Status

SysML– SysML RFP: March 2003– 1.0 Specification: September 2007– Currently: Revision Task Force 1.3

Modelica– 1.0 Specification: September 1997– 3.1 Specification: May 2009

SysML-Modelica– Initial idea: July 2005– INCOSE MBSE Challenge Project: August 2007 – now– OMG Working Group established: December 2008– Approved for public comment (RFC): June 2010– Adoption as OMG specification: 2011 (wip)

68

69

Summary

Objective:– Leverage the strengths of both SysML and Modelica by

integrating them to create a more expressive and formal MBSE language.

Descriptive Modeling in SysML

+

Formal Equation-Based Modeling forAnalyses and Trade Studies in Modelica

69

http://doc.omg.org/syseng/2010-6-8

70

Contents






71

Activity 2a in GIT RT21 Project Activity 2a in GIT RT21 Project Leveraged existing capabilities/examplesLeveraged existing capabilities/examples

status as of 2011-01-20(with completed examples listed)

Excavator Domain Models

MCAD Tools

Generic Math Solvers

Sys Dynamics Solvers

Excavator Sys-Level Models

Reliability Model

Cost Model

Optimization Model

ObjectiveFunction

Dymola

Federated Excavator Model

Boom Mfg. Assembly Models

System & Req Tools

RSD/E+

MagicDraw

NX

Mathematica

Optimizers

Excel

ModelCenter

Discrete Event Solvers (Specialized)

eM-Plant / Factory Flow

c0. Context-SpecificSimulation Models

e0. Solver Resourcesa0. Descriptive Resources

(Authoring Tools, ...)d0. Simulation Building Block

Libraries

Solid Mechanics

Queuing Concepts

Fluid Mechanics

CostConcepts

OptimizationConcepts

Reliability Concepts

Assembly Process Models

Discrete EventAssy Model

Dig Cycle Model

MM1 Queuing Assy Model

Boom Linkage Models

Stress/Deformation Models

Extensional Linkage Model

Plane Stress Linkage Model FEA Solvers

Ansys

Factory CAD Tools

FactoryCAD

b0. Federated Descriptive Models

Boom

Linkages

Hydraulics Subsystem

Factory Domain Models

Federated Factory Model

Operations

Req. & Objectives

...

Dig Site Dump Trucks

Data Mgt. Tools

Excel

Assembly Lines

Work CellsAGVs

Buffers Machines

Req. & Objectives

Excavator MBOM

2008-02-20

Tool & native m

odel interface (via XaiTools, APIs, ...)

1) The pattern names and identifiers used here conform

to HM

X 0.1 — a m

ethod under developm

ent for generalized system-sim

ulation interoperability (SSI).2) A

ll models show

n are SysML m

odels unless otherwise noted.

3) Infrastructure and middlew

are tools are also present (but not shown) --e.g.,

PLM, C

M, param

etric graph managers (XaiTools etc.), repositories, etc.

Com

position relationship (usage)N

ative model relationship (via tool interface, stds., ...)

Parametric or algorithm

ic relationship (XaiTools, VIA

TRA, ...)

Notes

Legend

# VV&A Concept Example(s)

1 automated units consistency MagicDraw SysML detecting units mismatch2 other built-in checking per SysML spec Model integrity (e.g., multiplicity checking);

propagating name updates; instance updates; etc.3 automated equation checking ParaMagic detecting wrong parameter name4 other built-in checking added by SysML tools Model checking suites in MagicDraw and ParaMagic 5 leveraging built-in checking by solvers / external tools

wrapped in a SysML context Mathematica detecting overconstrained system of equations, etc.

6 automated requirements verification FireSat, SimpleSat, etc. (parametrics, margin, ...)7 embedded unit tests LinkageSystems, build block libraries, ...8 automated roll-up of embedded unit tests (basic multi-level test) LinkageSystems, HomeHeatingSystem9 automated roll-up of embedded multi-level tests Combining above, ...

10 “DNA signature” - user interaction with model for intuitive visual inspection to aid model comprehension, V&V, debugging, ...

LinkageSystems, NGDMC, etc. (and above)

Main Test Cases (for Activities 2 and 3)- Excavator test bed with linkage systems - FireSat / NGDMC satellite- Home heating system - Mobile robot- Satellite-to-ground station communication link simulation- Short course tutorials (vehicle fuel system, space satellite, ...)

72

# VV&A Concept Example(s)

11 automated tool/solver verificationa Core math solvers: Mathematica, OpenModelica, Matlab SMT Unit test cases; XaiTools test suite (~150 models)

12 automated verification tests on external simulation/analysis modelsa System dynamics: Matlab/Simulink HomeHeatingSystemb FEA: Ansys LinkageSystems

13 automated verification tests on external design/descriptive modelsa Spreadsheets: Excel Satellite analyzer spreadsheetb MCAD: NX; ECAD: Mentor etc. via AP210 Vehicle; electronics (as recorded demos)c System mission design: STK Satellite orbit/trajectory & ground station sys. design

14 automated verification tests on physical systems: a activity-based test scripts with mobile robot Rover functionality scenarios (sensors, camera, ...)

Other aspects that could be demonstrated using similar capabilities as abovea Auto-generating documents from models (e.g., V&V status, accreditation report)b Managing requirements of models/sims themselvesc Managing data flow and data pedigree (for sim inputs, ...)d Capture of validation criteria used by subject matter experts (SMEs)e etc.

Activity 3a in GIT RT21 Project Activity 3a in GIT RT21 Project Extended capabilities/examples and created new onesExtended capabilities/examples and created new ones


rt1= 30”(anywhere on this circle)


rt2 = 30”(anywhere on this circle)



60 deg, 14”

45 deg, 12”

ra1 = ?ra2 = ?

Object2end location

Object1end location

status as of 2011-01-20(with completed examples listed)


Curriculum History & Formats OfferedStatistics as of Sept 2010 — www.pslm.gatech.edu/courses

Full-semester Georgia Tech academic courses– ISYE / ME 8813 & 4803: Since Fall 2007 (~95 students total)

Industry short courses– Collaborative development & delivery with InterCAX LLC– Multiple [offerings,~students] and formats since Aug 2008

» SysML 101 [14,~260]; SysML 102 (hands-on) [12,~205]– Modes: » Onsite at industry/government locations

» Open enrollment via Georgia Tech (Atlanta, DC, Orlando, Vegas, ...)

» Web-based “live” since Apr 2010– Coming soon: 201/202, 301/302 (int/adv concepts, OCSMP prep, ...)

Georgia Tech Professional Masters academic courses– Professional Masters in Applied Systems Engineering

www.pmase.gatech.edu– ASE 6005 SysML-based MBSE course - Summer 2010– ASE 6006 SE Lab (SysML-based system design project) - Fall 2010

74

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