Photo Credit: Derek Dalle
Modeling & Simulation of CubeSat Mission Model-Based Systems Engineering (MBSE) Behavioral Modeling and Execution Integration of MagicDraw, Cameo Simulation Toolkit, STK, and Matlab using ModelCenter
1 Sara Spangelo Jet Propulsion Laboratory (JPL), California Institute of Technology
Hongman Kim2 Grant Soremekun3 Phoenix Integration, Inc.
May 29/30, 2013
1 [email protected], 2 [email protected], [email protected] System Engineering Challenges
Conventional approaches: • Focus on subset of subsystems Motivation – Over-simplified, low fidelity Overview – Neglect subsystem interactions Modeling • Requirements verification using average/best/worst-cases Simulating – Fail to capture realistic “dynamic” nature of missions Design Trades • Models and simulations are not integrated! Reflections – “Hacked” together for one-off cases Future Work – Not modular, extensible, reusable
Why? Lack of integrated modeling/simulation tools to enable system-level engineering design/analysis.
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1Type of miniature spacecraft (1U = 10cm3, <1 kg)
Image Credit: www.cubesatkit.com System Engineering Challenges
Particularly an issue for CubeSats1 because: • Physical components physically integrated Motivation • Extremely constrained: Overview – Limited ability to collect and store energy (e.g. batteries) Modeling • Operational constraints/ decisions coupled Simulating – When to collect data versus download data? Design Trades • Obits are unknown/ dynamic Reflections – Little/ no control over launch orbit Future Work – Experience variation in eclipse duration, may de-orbit • Operate in inefficient/ stochastic environments
Integrated models and tools are critical to design and plan for these missions!
1 3 Type of miniature spacecraft (1U = 10cm , <1 kg) 3 Image Credit: www.cubesatkit.com Model-Based Systems Engineering (MBSE1)
Why MBSE? 1) Enables system-level model capture Motivation • Formal, accurate, authoritative single source Overview • Contains elements, relationships, interactions Modeling • Multiple compatible views, e.g. physical/ functional Simulating • Requirements verification and traceability Design Trades
Reflections Future Work
1 “ Formal” model to support requirements, design, analysis, verification 4 Image Credit: Ph.D. Thesis, S. Spangelo, 2012 Model-Based Systems Engineering (MBSE)
Why MBSE? 2) Enables integration of models and simulations Motivation • Connect system-level model to analytical tools (STK, Matlab) Overview • Execute dynamic simulation of end-to-end mission Modeling • Identify failure to satisfy requirements, sub-optimal designs Simulating • Accommodates re-evaluation when design changes occur Design Trades • Enables co-simulation: simultaneous vehicle/ mission design Reflections
Future Work
SysML STK, Matlab Models Simulation Tools
5 Image Credit: STK?/Matlab Websites Get little symbols
Motivating Mission Example • Radio Aurora Explorer (RAX) CubeSat mission • Science target: plasma irregularities in ionosphere
Motivation • Experimental zone in Poker Flat, Alaska Overview • Global ground station network Modeling • Vehicle constraints: solar panels, battery, data buffer Simulating Design Trades Reflections Future Work
RAX 3U CubeSat RAX Ground Network footprints
6 RAX Image Credit: RAX CubeSat Website
Get little symbols
Motivating Mission Example
Systems engineering questions: • How do satellite states evolve throughout mission? Motivation • Does the vehicle design/operations meet all mission requirements? Overview • How do changes in spacecraft mission parameters impact Modeling performance and requirements satisfaction? Simulating Design Trades Reflections Future Work
7 Image Generated with STK Project: “Model” Operational CubeSat Mission goals….
Goal #1: Develop fundamental systems model of CubeSat mission
Motivation Capture structure, function, relationships, requirements, traceability. Overview Pretty clear-cut if you know what you’re modeling. Accomplished by SSWG1,2. Modeling Simulating Design Trades Goal #2: Execute realistic behavioral CubeSat scenarios Reflections Capture operational opportunities, state evolution, mission performance. Future Work No clear way to do this in March 2013. Potential tools: MagicDraw? Simulation Tool Kit (STK)? Matlab? Phoenix ModelCenter? Cameo Simulation Toolkit?
[1] S. Spangelo, D. Kaslow, C. Delp, L. Anderson, B. Cole, E. Foyse, L. Cheng, R. Yntema, M. Bajaj, G. Soremekum, and J. Cutler, “Model Based Systems Engineering (MBSE) Applied to Radio Aurora Explorer (RAX) CubeSat Mission Operational Scenarios”, Accepted for IEEE Aerospace Conference, 2013, Big Sky, MT, March 2013. [1] S. Spangelo, D. Kaslow, C. Delp, B. Cole, L. Anderson, E. Fosse, L. Hartman, B. Gilbert, and J. Cutler, “Applying Model Based Systems Engineering (MBSE) to a Standard CubeSat”, IEEE Aerospace Conference, 2012, Big Sky, MT, March 2012. 8 Project: “Model” Operational CubeSat Mission accomplished…
Project Deliverables: Motivation • Systems-level SysML model (in MagicDraw) Overview Modeling – Structure of mission architecture and vehicle Simulating – Requirements definition and traceability Design Trades – Parametric diagrams to capture analytical relationships Reflections • Evaluated using MBSE Analyzer Future Work – Behavioral diagrams to capture dynamic operations • Executed using Cameo Simulation Toolkit and MBSE Analyzer1 • Analytical models for describing behavior – STK, Matlab, Java – ModelCenter enabled integration with SysML and automated execution of dynamic scenarios
1 A prototype capability was developed for this work that allows CST to execute parametric diagrams via MBSE Analyzer 9 Image Generated with STK Get little symbols
Modeling Philosophies
For usability/ extensibility:
Motivation • Modularity: re-usable libraries of parts Overview e.g. constraint block modules are re-used in many parametric diagrams Modeling
Simulating • Patterning: re-use of modeling patterns Design Trades e.g. common pattern in Power and Data Management subsystems Reflections
Future Work • Nomenclature: simple and sufficiently descriptive e.g. subsystem naming codes used for data rate and power values
10 CubeSat System Model Architecture
Motivation Overview Modeling Simulating Design Trades Reflections Future Work
The system model captures requirements, structure, behavior, and parametrics.
11 Structural Diagrams
Motivation Overview Mission Level Modeling Simulating Design Trades Reflections Future Work
Vehicle Level
12 Defines Defines constraint on Mission Requirements constraint on lowest battery minimum level throughout Drive systems design download mission
Motivation Overview Modeling Simulating Design Trades Reflections Future Work
Defines constraint on lowest data storage level throughout mission
13 Parametric Diagram Constraint blocks defines opportunities
Motivation Overview Modeling Simulating Design Trades Reflections Future Work
Pointing to a ModelCenter model with STK and Matlab
14 ModelCenter Model STK and Matlab Plug-Ins
Motivation Overview Modeling Simulating Design Trades Reflections Future Work
• Analysis models (STK, Matlab) wrapped and integrated with ModelCenter • ModelCenter models imported into SysML model constraint blocks with MBSE Analyzer 15 Systems Tool Kit (STK)
Analytic simulation tool used to propagate obit & compute: • Solar state: sun/eclipse, solar panel angles Motivation • Access to experimental zone Overview • Access to ground stations Modeling Simulating Design Trades Sun State/ Eclipse State Reflections Future Work
16 Image Generated with STK Parametric Diagrams Constraint blocks computes total power
Motivation Overview Modeling Simulating Design Trades Reflections Future Work
Pointing to a ModelCenter model with Matlab plug-in
17 Parametric Diagrams Constraint blocks update satellite states
Several constraint blocks re-used throughout model Motivation Overview Modeling Simulating Design Trades Reflections Future Work
• Compute energy level at the next time step • Similar parametric diagrams for experiment data and data download
18 MBSE Analyzer: Parametric Diagram Solver
Motivation Overview Modeling Simulating Design Trades Reflections Future Work
• Solves linked parametric diagrams (all 3) simultaneously
• Automated requirements verification (green: pass, red: fail) 19 Bringing the Model to Life Main State Machine Diagram
• Entry point of Cameo Simulation Toolkit Motivation (CST) behavioral Overview simulation Modeling • Starts “RunOperation” Simulating activity diagram that Design Trades steps through mission Reflections simulation Future Work • Updates solar, experiment, and download states according to signals
20 Main Simulation Loop
Update states Motivation according to opportunities Overview Modeling Simulating Design Trades Reflections Future Work
Iterate through Update time entire scenario duration
21 State Update Activity Diagram
Motivation Overview Modeling Simulating Call MBSE Analyzer to solve Design Trades parametric diagrams Reflections Send signals to update Future Work the state machine
Update property values for the calculation at the next time step
1 A prototype capability was developed for this work that allows CST to execute parametric diagrams via MBSE Analyzer 22 How are Mission Simulations Performed?
Motivation Overview Modeling Simulating Design Trades Reflections Future Work
MagicDraw CST MBSE Analyzer/ModelCenter STK, Matlab, etc. (Behavioral diagrams) (Parametric diagrams) (Analytical models)
23 Matlab Symbol Image Credit: Matlab Websites Mission Simulation Results
Each column contains updated state at time step
Motivation Overview Modeling Simulating Design Trades Reflections Future Work
• During CST simulation, MBSE Analyzer is called at each time step • Data Explorer automatically stores time history of the simulation data 24 Mission Simulation Results
Energy drop in • Combined simulation SysML Motivation eclipse behavioral diagrams to STK, Overview Matlab using MBSE Analyzer Modeling Energy drop • MBSE Analyzer is called at Simulating during each time step during CST download Design Trades simulation Reflections • Time history of energy level, Future Work experiments, and data download is stored
25 Final Step: Requirements Verification Full end-to-end (dynamic) scenario
Motivation Overview Modeling Simulating Design Trades Reflections Future Work
• Post-CST simulation: final state stored in an instance specification • Use MBSE Analyzer to verify requirements with visual tool!
26 Mission and Design Trade-Offs Battery Capacity 120000 115000
110000 Motivation 105000 Requirements defined Overview in SysML model Nominal 100000
Modeling Battery Energy, Joules 95000 Capacity Simulating 90000 1 11 21 31 41 51 61 71 81 91 Design Trades Time, minutes
Reflections Minimum Battery Capacity Maximum Battery Capacity Future Work Battery Level 115500 115000 1/8 Battery 114500 Capacity 114000 113500 113000
112500 Energy, Joules
112000 Infeasibility: Failure to 111500 satisfy requirements 111000 1 11 21 31 41 51 61 71 81 91 Time, minutes 27 Mission and Design Trade-Offs Orbit Altitudes
9 Nominal Orbit High Orbit 8 Motivation Low Orbit Minimum Requirement Overview 7
Modeling 6
Simulating 5
Design Trades 4 Reflections 3 Future Work DownloadedData,MBytes 2
1
0 -1 4 9 14 19 24 -1 Time, hours
Nominal: semi-major axis = 7012km, apogee altitude = 811.69 km, perigee altitude=457.57 km High: semi-major axis = 7500 km, apogee altitude = 1311.22 km, perigee altitude = 932.50 km Low: semi-major axis = 6800 km, apogee altitude =593.55 km, perigee altitude=250.18 km 28
Mission and Design Trade-Offs Ground Station Locations
10 Ann Arbor and Menlo Park (Nominal) Ann Arbor and Fairbanks Fairbanks and Menlo Park Motivation 8 Minimum Requirement
Overview Modeling 6 Simulating 4 Design Trades
Reflections 2 Future Work DownloadedData, MBytes 0 -1 4 9 14 19 24
-2 Time, hours
Location And Description Of Ground Stations In Network Name State Latitude Longitude Altitude Minimum Elevation Efficiency (degrees) (degrees) (km) (degrees) AnnArbor MI 42.271 -83.73 0.256 5 0.8 Fairbanks AK 64.88 -147.5 0 0 1 MenloPark CA 37.457 -122.2 0.022 0 0.95 29 Reflecting on Project Experience
How did MBSE enable us to overcome challenges? • Coupled analytic models with simulation capabilities Motivation • Demonstrated dynamic behavioral modeling Overview • Achieved requirements verification for full end-to-end missions Modeling • Extensible by use of standards, libraries, patterns, etc. Simulating
Design Trades Lessons Learned Reflections • Working with many tools is challenging (license, versions, etc.) Future Work • STK has a lot of flexibility: exploit use vectors/ angles • Best to automate repeated tasks • Working with vendors is necessary/advantageous • Always ask: “Am I using the right modeling/simulation tool?”
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Future Work • Extend the system-level model • Higher fidelity models of the spacecraft subsystems
Motivation • Include communication and experimental link budgets Overview • Extend and refine the behavioral and analysis models Modeling • Add spacecraft scheduling for optimal use of resources Simulating • Improve approach for data extraction at specific time (e.g. from STK) Design Trades • Automate system and mission parameters trade-offs Reflections • Extend MBSE Analyzer to drive simulations by CST Future Work • Enable sensitivity analysis and design optimization • Generalize the model for applicability to a variety of mission concepts
31 Pre-Decisional – For Planning and Discussion Purposes Only Image Credit: CubeSat Mission Team Websites
Acknowledgements
• Mike Bruchanski, Greg Haun, Dave Kaslow from Analytical Graphics, Inc. (AGI)
• Chris Delp, Louise Anderson, Bjorn Cole, James Smith from JPL
• Radio Aurora eXplorer (RAX) Team, Prof. James Cutler
• CubeSat and Amateur Radio Communities
• Dr. Derek Dalle (graphics)
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