Co-simulation: State of the art and open challengesmsdl.cs.mcgill.ca/people/claudio/pres/2017/KeynoteTrento.pdf · 2018-01-04 · The Context 1990 2000 2010 SIMNET(Military Training)

Co-simulation

Cláudio Gomes*

http://msdl.cs.mcgill.ca/people/claudio

State of the Art and Open Challenges

* with Casper Thule, Peter Larsen, David Broman, and Hans Vangheluwe

The Essence

2

[1] http://overturetool.org/

[2] http://www.blensor.org/

[3] https://nl.mathworks.com/products/simulink.html

A technique to combine simulators.

Body

Controller

Env.Overture[1]

[3]

[2]

[4] Case Study by Kristof Berx , Davy Maes, and Klaas Gadeyne, from Flanders Make

[4]

Co-simulation: how?

Control Simulator

Model

Controller

SolverBody Simulator

SolverModel

Body

Env. Simulator

SolverModel

Env.

3

Body

Controller

Env.Overture[1]

[3]

[2]





[4]

ControlSimulator

Co-simulation: how?

Body Simulator Env. SimulatorFMU.zip|-- .dll or .so|-- .xml

fmi2DoStep(…),fmi2SetReal(…), …

4[5] www.fmi-standard.org

[5]

Body

Controller

Env.Overture[1]

[3]

[2]





[4]

Co-simulation: how?

Body Simulator

Model Solver

Body

Env. Simulator

Model Solver

Env.

Orchestrator

Body Simulator

Sensor&ControlSimulator

Env. Simulator

5

ControlSimulator

Model

Controller

Solver

t := t + H…

…

Co-simulation: how?Orchestrator

Body Simulator

ControlSimulator

Env. Simulator

simulateUntil(t+H,…)

getOutput(…)

setInput(…)


getOutput(…)

setInput(…)


ControlSimulator Body Simulator Env. Simulator

6

Gu, B., & Asada, H. H. (2001). Co-simulation of algebraically coupled dynamic subsystems. In American Control Conference, 2001. Proceedings of the 2001 (Vol. 3, pp. 2273–2278 vol.3). http://doi.org/10.1109/ACC.2001.946089

The ContextHistorical Overview

7

The Context

1980 1990 2000

8

[1] Fujimoto, R. M. (1990). Parallel discrete event simulation. Communications of the ACM, 33(10), 30–53. http://doi.org/10.1145/84537.84545

First discrete event synchronization algorithms [1]

[2]

[2] Kent Peacock, J., Wong, J. ., & Manning, E. G. (1979). Distributed simulation using a network of processors. Computer Networks (1976), 3(1), 44–56. http://doi.org/10.1016/0376-5075(79)90053-9

The Context

1980 1990 [1] http://www.frobnotz.com/cosim/cosim.html2000

Decomposition of sparsely coupled systems.

First discrete event synchronization algorithms

9

[1]

[2]

[2] Newton, A. R., & Sangiovanni-Vincentelli, A. L. (1983). Relaxation-Based Electrical Simulation. SIAM Journal on Scientific and Statistical Computing, 4(3), 485–524. http://doi.org/10.1137/0904036

The Context

1980 1990 2000



Time warp O.S.

10

[1] Jefferson, D., Beckman, B., Wieland, F., Blume, L., & Diloreto, M. (1987). Distributed Simulation and the Time Warp Operating System David. ACM SIGOPS Operating Systems Review, 21(5), 77–93. http://doi.org/10.1145/37499.37508

[1]

The Context

1980 1990 2000



Time warp O.S.

SIMNET(Military Training)

11

[1] Miller, D. C., & Thorpe, J. A. (1995). SIMNET: the advent of simulator networking. Proceedings of the IEEE, 83(8), 1114–1123. http://doi.org/10.1109/5.400452

[1]

The Context

12

1980 1990 2000

The Context

1990 2000 2010

SIMNET(Military Training)SW/HW co-simulation.

“Software developers were often left to develop code for months with severely limited ability to test […]. Painful integration efforts came late in the design cycle, and minor miscommunication became major design flaws.”

-- Anne Powell and Shawn Lin

[1] http://www.frobnotz.com/cosim/cosim.html

13

[1]

The Context

1990 2000 2010

SIMNET(Military Training)SW/HW co-simulation.

“A DSblock may serve as neutral ‘model bus’ to exchange nonlinear models between different modelling and simulation environments.”

DSBlock Standard

14

[1] Otter, M., & Elmqvist, H. (1995). The DSblock model interface for exchanging model components. In Proceedings of the Eurosim’95 Simulation Congress (pp. 505–510).

[1]

The Context

1990 2000 2010

SIMNET(Military Training)SW/HW co-simulation.DSBlock Standard

MPM&S Research agenda

15

[1] Vangheluwe, H. L., Vansteenkiste, G. C., & Kerckhoffs, E. J. H. (1996). Simulation for the Future : Progress of the Esprit Basic Research Working Group 8467. In Proceedings of the 1996 European Simulation Symposium (pp. XXIX--XXXIV). Genoa: Society for Computer Simulation International.

[1]

“A communication protocol which defines interaction between simulators in a tool and vendor independent manner.”

The Context

1990 2000 2010



DIS IEEE Standard

16

[1] IEEE Standard for Distributed Interactive Simulation--Application Protocols. (2012). IEEE Std1278.1-2012 (Revision of IEEE Std 1278.1-1995). http://doi.org/10.1109/IEEESTD.2012.6387564

[1]

The Context

1990 2000 2010



DIS IEEE Standard

Multi-abstraction

17

[1] Hines, K., & Borriello, G. (1997). Selective focus as a means of improving geographically distributed embedded system co-simulation. In 8th IEEE International Workshop on Rapid System Prototyping, 1997 (pp. 58–62). http://doi.org/10.1109/IWRSP.1997.618825

[1]

The Context

1990 2000 2010



DIS IEEE Standard

Multi-abstraction

Automotive, Railway, HVAC reported applications

18[2] Le Marrec, P., Valderrama, C. A., Hessel, F., Jerraya, A. A., Attia, M., & Cayrol, O. (1998). Hardware, software and mechanical cosimulation for automotive applications. In 9th International Workshop on Rapid System Prototyping (pp. 202–206). http://doi.org/10.1109/IWRSP.1998.676692

[1] Arnold, M., & Günther, M. (2001). Preconditioned Dynamic Iteration for Coupled Differential-Algebraic Systems. BIT Numerical Mathematics, 41(1), 1–25. http://doi.org/10.1023/A:1021909032551

[1]

[2]

The Context

19

1990 2000 2010

The Context

2000 2010


HLA IEEE Standard

20

[1] IEEE Standard for Modeling and Simulation (M&S) High Level Architecture (HLA) - Federate Interface Specification. (2010). IEEE Standard 1516-2010. Retrieved from https://standards.ieee.org/findstds/standard/1516-2010.html

[1]

The Context

2000 2010


HLA IEEE Standard

[1] Andert Jr., E. P., & Morgan, D. (1998). Collaborative Virtual Prototyping and Test. Naval Engineers Journal, 110(6), 17–23. http://doi.org/10.1111/j.1559-3584.1998.tb02962.x

DMU Concept

21

[1]

The Context

2000 2010


HLA IEEE Standard

IP Protection, FMI StandardDMU Concept

22

[1] www.fmi-standard.org

[1]

The Context

2000 2010


HLA IEEE Standard


Digital Twin

[1] https://siemensplm.i.lithium.com/t5/image/serverpage/image-id/31800iB94D19235331E799?v=1.0 23

[1]

The Context

2000 2010


HLA IEEE Standard


Digital TwinVirtual Design, Build, Operation, and Maintenance

24

Open Challenges

The VisionVirtual Design, Build, Operation, and Maintenance

25

Virtual Design

(x,y)

Φ

26[2] Case Study by Kristof Berx , Davy Maes, and Klaas Gadeyne, from Flanders Make

[2]

Frequent full system evaluation

(x,y)

Φ

Body

Controller

Sensors Env.

Virtual Design

Body

MotorL. MotorR.

Controller

Sensors

Battery

Env.

…at multiple levels of refinement

27

Virtual Build

28

[1] http://www.partsim.com/

…at any level of refinement

Virtual Build

Body

MotorL. MotorR.

Controller

Sensors

Battery

Env.

…between multiple suppliers

29


Virtual Operation

[1] http://www.power-technology.com/features/featuredigital-twin-the-living-breathing-and-failing-virtual-power-plant-5742648/ 30

Body

MotorL. MotorR.

Controller

Sensors

Battery

Env.

…between multiple suppliers

…with X-in-the-loop

Virtual Operation

31

[1] Dávid, I., Meyers, B., Vanherpen, K., Van Tendeloo, Y., Berx, K., & Vangheluwe, H. (2017). Modeling and Enactment Support for Early Detection of Inconsistencies in Engineering Processes. In 2nd International Workshop on Collaborative Modelling in MDE (p. to appear). Austin, Texas.

[1,2]



…at any level of refinement

[3] https://www.virtalis.com/blogs/2013/09/06/germanys-dlr-turns-to-virtalis-for-cockpit-simulator-upgrade-4/

[3]

[4] Palmieri, M., Bernardeschi, C., & Masci, P. (2017). Co-simulation of semi-autonomous systems : the Line Follower Robot case study. In 1st Workshop on Formal Co-Simulation. Trento, Italy.

[4]

Virtual Maintenance

32[2] https://www.youtube.com/watch?v=2dCz3oL2rTw

[2]

[1] https://www.fool.com/investing/2016/09/18/what-is-the-industrial-internet.aspx

[1]

ChallengesA Roadmap

33

System Interaction

http://usatoday30.usatoday.com/news/nation/2007-02-26-volkswagen-recall_x.htm 34

“ … a faulty brake light could

work in tandem with the shift

interlock to immobilize the

vehicle and require towing”

System Heterogeneity - ABS

35

[1] https://www.cheatsheet.com/wp-content/uploads/2015/12/Brembo-Glowing-Corvette-Brakes.jpg?x84068

[1]

[2]

[2] Gomes, C. (2015). Foundations for Co-simulation -- IWT Proposal. Antwerp.

[3]

[3] El-Garhy, A. M., El-Sheikh, G. A., & El-Saify, M. H. (2013). Fuzzy Life-Extending Control of Anti-Lock Braking System. Ain Shams Engineering Journal, 4(4), 735–751. http://doi.org/10.1016/j.asej.2012.12.003

Challenges in Systems Development

Scale

Heterogeneity

Market Pressure

36

[1]

[2]

[2] www.imes.uni-hannover.de/

[1] Mustafiz, S., Denil, J., Lúcio, L., & Vangheluwe, H. (2012). The FTG+PM framework for multi-paradigm modelling. In Proceedings of the 6th International Workshop on Multi-Paradigm Modeling - MPM ’12(pp. 13–18). New York, New York, USA: ACM Press. http://doi.org/10.1145/2508443.2508446

[3] Nielsen, C. B., Larsen, P. G., Fitzgerald, J., Woodcock, J., & Peleska, J. (2015). Systems of Systems Engineering: Basic Concepts, Model-Based Techniques, and Research Directions. ACM Comput. Surv., 48(2), 18:1--18:41. http://doi.org/10.1145/2794381

[4] Mosterman, P. J., & Zander, J. (2016). Industry 4.0 as a Cyber-Physical System study. Software & Systems Modeling, 15(1), 17–29. http://doi.org/10.1007/s10270-015-0493-x

Tackling Complexity - Tools

Formalism & Tool Specialization

37[2] www.fmi-standard.org

[3] https://www.gtisoft.com/gt-suite-applications/thermal-management/powertraincooling/

Co-simulation[2] [3]

Model Exchange

[1] http://www.dlr.de/irs/en/desktopdefault.aspx/tabid-11079/#gallery/27339

[1]

Design Space Exploration

Full System Simulation

Use Cases

X-in-the-LoopIncremental

Testing/Certification

38[1] http://projects.au.dk/into-cps/dissemination/summerschool/

[1]

Open Challenges

Traceability

Consistency Management

Guarantees

Non-determinismX-in-the-LoopIncrement




Dynamic Structure

Performance Accuracy Tradeoff

Debugging

Validity

39

(Time) Prediction

Validity

Denil, J., Klikovits, S., Mosterman, P. J., Vallecillo, A., & Vangheluwe, H. (2017). The experiment model and validity frame in M&S. In Proceedings of the Symposium on Theory of Modeling & Simulation (Vol. 49).

Validity

40

Vanherpen, K., Denil, J., De Meulenaere, P., & Vangheluwe, H. (2016). Ontological Reasoning as an Enabler of Contract-Based Co-design. In C. Berger, M. R. Mousavi, & R. Wisniewski (Eds.), Cyber Physical Systems. Design, Modeling, and Evaluation: 6th International Workshop, CyPhy 2016, Pittsburgh, PA, USA, October 6, 2016, Revised Selected Papers (pp. 101–115). Cham: Springer International Publishing. http://doi.org/10.1007/978-3-319-51738-4_8

forced

isp

lace

me

nt

Validity

Spiegel, Reynolds, and Brogan

Participants were instructed to ignore any constraints

related to the implementation of the model. In the falling

body simulation, there are multiple implementation choices

of numerical methods and numerical precision. In comput-

ing position as a function of time, what sort of numerical

integration method should be employed? What effect

would it have on correctness of results? We have chosen

not to discuss implementation assumptions in order to fo-

cus attention on the model instead of the simulation. When

joining composable simulations it will be necessary to

validate both the combined model and then to validate the

combined simulation.

After a period of two weeks submissions were tabu-

lated and a master list was created and discussed among

the contestants and others. We defer discussion of the re-

sults to after the presentation of the challenge in the next

section.

3.2 Falling Body Model

The following model is an extended version of the model

presented in Appendix A of the monograph by Davis and

Anderson (2003). A sphere is falling through some me-

dium and experiencing drag as it falls. Let p(t) equal the

position of the sphere at time t and p(0) = p0 be the initial

position. Let v(t) = p′(t) equal the velocity of the sphere at

time t, and let v(0) = v0 be the initial velocity. Calculate

p(t) and v(t) for all t ≥ 0.

The sphere is perfectly smooth, it has diameter d and

mass m. The medium has uniform density ρf and uniform

kinematic viscosity ν. Assume that when the sphere im-

pacts with the earth, p(tearth) = 0, it will remain on the

ground for all t > tearth.

The following forces will act on the sphere (Chow

1979):

• Gravity: The sphere experiences constant accel-

eration, g ≈ 9.8 m/s2.

• Buoyancy: mf = (1/6)πd3ρf against gravity.

• Inertial drag: (1/2) mf v′ (t)

• Viscous drag: (1/2)ρf · v(t) ·│v(t)│· π/4 · d2 ·

cd(v(t))

• Wave drag: Wave drag is negligible at subsonic

speeds.

We apply Newton’s Second Law to determine acceleration,

and then employ numerical methods of integration to cal-

culate velocity and position.

Figure 1: Falling Body Model

The term cd is the drag coefficient and it is defined as a

function of the Reynolds number, which is Re = v(t) · d / ν.

The drag coefficient is determined experimentally as a

function of the Reynolds number. Both the drag coeffi-

cient and the Reynolds number are dimensionless values.

For a perfectly smooth sphere, cd can be approximated

piecewise with the following function,

•••

•

•••

•

•

=

•<•

••<••

••<

•<

•<•

76

654275.0

5

2

10Re10218.0

102Re103(Re)000366.0

103Re4005.0

400Re1

1Re10

646.0(Re)

24

Re24

dc

The reader interested in pursuing the challenge without

bias from the challenge results should pause at this point,

continuing when identification of unstated constraints is

completed. We discuss the results of the challenge next.

3.3 Challenge Results and Constraint Taxonomy

The competition produced a master list of twenty-nine

validation constraints (see Appendix). From them we have

derived a taxonomy of validation constraint types. Every

effort has been made to remove redundant constraints and

to represent identified validation constraints as concisely as

possible. We make no claim to having found all validation

constraints for the falling body model.

It is illuminating to consider that the top three contest-

ants identified only 21, 19, and 16 constraints, respectively,

out of the master list (see Table 1). No single participant

was capable of identifying more than three-quarters of all

currently identified constraints. Like the component de-

signers of (Garlan, Allan, and Ockerbloom 1995) our chal-

lenge participants were neither “lazy, stupid, nor mali-

cious.” Each participant failed to identify several implicit

41

[1] Spiegel, M., Reynolds, P. F., & Brogan, D. C. (2005). A Case Study of Model Context for Simulation Composability and Reusability. In Proceedings of the Winter Simulation Conference, 2005. (Vol. 2005, pp. 437–444). IEEE. http://doi.org/10.1109/WSC.2005.1574279

Validity

Spiegel, Reynolds, and Brogan In an informal contest related to our study, no participant

identified more than 75% of the ultimate set of constraints

identified. Borrowing from Garlan, Allan, and Ocker-

bloom our challenge participants were neither “lazy, stu-

pid, nor malicious.” (1995)

We believe the study reported here can be useful to the

reader beyond the results above. The falling body model

presents a fine example for testing any proposed reusability

process. If the process cannot lead to the efficient extrac-

tion of the constraints listed in the Appendix, then it is of

questionable value.

In the future we anticipate further study of our initial

taxonomy of validation constraints. Will other types of

simulations yield new categories of constraints? The tax-

onomy is useful only if it can serve as a general guidepost

that suggests hidden constraints that have not been identi-

fied. Additionally the taxonomy for the simulation com-

munity may benefit from insights in the larger domain of

software design. Generic software applications contain

properties that are identified as invariant or time-

dependent. Can the lessons from formal software analysis

be applied to our objectives? We will be exploring these

issues.

ACKNOWLEDGEMENTS

We gratefully acknowledge support from the National Sci-

ence Foundation (ITR 0426971), as well as from our col-

leagues in the Modeling and Simulation Technology Re-

search Initiative (MaSTRI) at the University of Virginia.

We would also like to thank all the participants of the fal-

ling body challenge: Robert Bartholet, Joseph Carnahan,

Mark Farrington, Aleks Gershaft, William Kammersell,

Yinping Kuang, Xinyu Liu, Yannick Loitiere, Thomas Lu-

bitz, and Weide Zhang.

APPENDIX: FALLING BODY CONSTRAINTS

1. Invariant Constraints

1.a Sphere Attributes

1. Sphere Property - The body is a sphere and it re-

mains spherical.

2. Smooth Property - The body is smooth and it re-

mains smooth.

3. Impermeable Property - The body is completely

impermeable.

4. Initial Velocity - The body has an initial velocity

of v0 that has no horizontal component of motion.

5. Angular Velocity - The body has no initial angu-

lar velocity.

6. Constant Mass - The mass of the body remains

constant over time. The body does not experience

ablation or accretion.

7. Constant Diameter - The diameter of the body

remains constant over time.

8. Distribution of Mass - The body has a centrally

symmetric mass distribution that remains constant

over time.

9. Uncertainty Principle - The diameter of the body

is much greater than the Plank length.

10. Brownian Motion - The mass and diameter of the

body are large enough such that Brownian motion

of the fluid has negligible impact on the body.

11. General Relativity - The mass of the body is low

enough to ignore the gravitational curvature of

space-time.

1.b Fluid Attributes

12. Fluid Density - The fluid density is constant. The

fluid is incompressible.

13. Fluid Pressure - The fluid pressure is constant.

14. Fluid Temperature - The fluid temperature is con-

stant.

15. Kinematic Viscosity - The kinematic viscosity is

constant. The medium is a Newtonian fluid.

16. Stationary Fluid - The fluid is stationary apart

from being disturbed by the falling body.

17. Infinite Fluid - The volume of the fluid is large

enough to completely envelope the sphere. The

movement of the fluid is not restricted by a con-

tainer such as a pipe or tube.

1.c Earth Attributes

18. Flat Terrain - The ground does not have terrain

and remains flat for all t > 0.

19. Coriolis Effect - The Earth is not rotating. We ig-

nore the Coriolis effect.

2. Dynamic Constraints

20. Mach Speed - The velocity of the body is suffi-

ciently less than the speed of sound for that me-

dium.

21. Special Relativity - The velocity of the body is

sufficiently less than the speed of light for that

medium.

22. Reynolds Number - The Reynolds number re-

mains between 10-2 and 107 for all t > 0. The

Reynolds number is a function of velocity.

3. Inter-Object Constraints

23. Sphere/Fluid Interaction - The body and the fluid

interact only through buoyancy and drag. For ex-

ample, the body cannot dissolve in the fluid, nor

can the body transfer heat to the fluid. Spiegel, Reynolds, and Brogan

In an informal contest related to our study, no participant










questionable value.












issues.

ACKNOWLEDGEMENTS














mains spherical.


mains smooth.


impermeable.




lar velocity.








over time.








space-time.






stant.

















dium.



medium.








can the body transfer heat to the fluid.

Spiegel, Reynolds, and Brogan In an informal contest related to our study, no participant










questionable value.












issues.

ACKNOWLEDGEMENTS














mains spherical.


mains smooth.


impermeable.




lar velocity.








over time.








space-time.






stant.

















dium.



medium.








can the body transfer heat to the fluid. Spiegel, Reynolds, and Brogan

24. Sphere/Earth Interaction - The body and the earth

interact only through the gravitational force.

25. Fluid/Earth Interaction - The fluid and the earth

do not interact.

26. Closed System - The Earth, sphere, and fluid do

not interact with any other objects.

27. Simple Gravity - Gravity is a constant downward

force of 9.8 m/s2.

28. One-Sided Gravity - The mass of the body is

much less than the mass of the Earth. The Earth

is not affected by the gravitational pull of the

body.

29. Inelastic Collision - The collision between the

sphere and the ground is perfectly inelastic.

REFERENCES

Bartholet, R., D.C. Brogan, P.F. Reynolds, and J.C.

Carnahan 2004. In search of the philosopher’s stone:

Simulation composability versus component-based

software design. In Proceedings of the Fall 2004 Simulation Interoperability Workshop, Orlando, FL.

Carnahan, J.C., D.C. Brogan and P.F. Reynolds. Simula-

tion-specific characteristics. Submitted for publica-

tion.

Chow, C. 1979. Introduction to computational fluid me-

chanics. Hoboken, New Jersey: John Wiley & Sons

Inc.

Davis, P.K. and R.H. Anderson 2003. Improving the com-

posability of Department of Defense models and simu-

lations, Rand Corporation Report. <http://www.rand.org/publications/MG

/MG101/>

Garlan, D., R. Allen, and J. Ockerbloom 1995. Architec-

tural mismatch or why it’s hard to build systems out of

existing parts. In Proceedings of the Seventeenth In-ternational Conference on Software Engineering. Se-

attle WA.

Hanks, K.S., K.C. Knight, and E.A. Strunk. 2001. Erro-

neous requirements: a linguistic basis for their occur-

rence and an approach to their reduction. In Proceed-ings of the 26th Annual NASA Goddard Software Engineering Workshop. Greenbelt, MD

Hayhurst, K.J. and C.M. Holloway 2001. Challenges in

software aspects of aviation systems. In Proceedings of the 26th Annual NASA Goddard Software Engineer-ing Workshop. Greenbelt, MD

Kasputis, S. and H.C. Ng 2000. Composable simulations.

In Proceedings of the 2000 Winter Simulation Confer-ence, ed. J.A. Joines, R.R. Barton, K. Kang, and P.A.

Fishwick, 1577-1584.

Lutz, R. R. 1993. Analyzing software requirements errors

in safety-critical, embedded systems. In Proceedings of the IEEE International Symposium on Requirements

Engineering. Piscataway, New Jersey: Institute of

Electrical and Electronics Engineers.

Malak, R. J., and C.J.J. Paradis 2004. Foundations of vali-

dating reusable behavioral models in engineering de-

sign problems. In Proceedings of the 2004 Winter Simulation Conference, ed. R.G. Ingalis, M.D. Ros-

setti, J.S. Smith, and B.A. Peters, 420-428. Piscata-

way, New Jersey: Institute of Electrical and Electron-

ics Engineers.

Page, E.H. and J.M. Opper 1999. Observations on the

complexity of composable simulation. In Proceedings of the 1999 Winter Simulation Conference, ed. P.A.

Farrington, H.B. Nembhard, D.T. Sturrock, and G.W.

Evans, 553-560. Piscataway, New Jersey: Institute of

Electrical and Electronics Engineers.

Petty, M.D. and E.W. Weisel 2003a. A composability

lexicon. In Proceedings of the Spring 2003 Simulation Interoperability Workshop, 181-187. Simulation In-

teroperability Standards Organization.

Petty, M.D. and E.W. Weisel 2003b. A formal basis for a

theory of semantic composability. In Proceedings of the Spring 2003 Simulation Interoperability Work-shop, 416-423. Simulation Interoperability Standards

Organization.

Shaughnessy, E.J., I.M. Katz, and J.P. Schaffer. 2005. In-

troduction to fluid mechanics. New York: Oxford

English Press.

Sullivan, K.J. and J.C. Knight 1996. Experience assessing

an architectural approach to large-scale, systematic re-

use. In Proceedings of the 18th International Confer-ence on Software Engineering (ICSE18), 220-229.

Berlin, Germany, March 25-29.

Yam, P. September 2004. Everyday Einstein. Scientific American, 50-55.

Yilmatz, L. 2004. On the need for contextualized intro-

spective models to improve reuse and composability

of defense simulations. Journal of Defense Modeling and Simulation 1 (3): 141-151.

Zeigler, B.P., H. Praehofer, and T.G. Kim. 2000. Theory

of Modeling and Simulation, 2nd Edition. Burlington,

MA: Academic Press.

AUTHOR BIOGRAPHIES

MICHAEL SPIEGEL is a Ph.D. Candidate in Computer

Science and a member of the Modeling and Simulation

Technology Research Initiative (MaSTRI) at the Univer-

sity of Virginia. Michael earned his B.A. in Computer

Science at Swarthmore College. He has previously held

the position of research associate at StreamSage, Inc.

studying natural language processing for multimedia

search engines. His email addresses is

<[email protected]> and his web ad-

dress is <www.cs.virginia.edu/~ms6ep> .

42

Open Challenges

Traceability


(Time) Prediction

Guarantees





Dynamic Structure


Debugging

Validity

43

Non-Determinism

Non-determinism

Lawrence, D. P. Y., Gomes, C., Denil, J., Vangheluwe, H., & Buchs, D. (2016). Coupling Petri nets with Deterministic Formalisms Using Co-simulation. In Proceedings of the Symposium on Theory of Modeling & Simulation: DEVS Integrative M&S Symposium (p. 6:1--6:8). Pasadena, CA, USA.

Mancini, T., Mari, F., Massini, A., Melatti, I., & Tronci, E. (2014). Anytime System Level Verification via Random Exhaustive Hardware in the Loop Simulation. In Digital System Design (DSD), 2014 17th Euromicro Conference on (pp. 236–245). http://doi.org/10.1109/DSD.2014.91

Clarke, E., Donzé, A., & Legay, A. (2010). On simulation-based probabilistic model checking of mixed-analog circuits. Formal Methods in System Design, 36(2), 97–113. http://doi.org/10.1007/s10703-009-0076-y

44

Denil, J., Salay, R., Paredis, C., & Vangheluwe, H. (2018). Towards Agile Model-Based Systems Engineering. In 3rd Flexible MDE Workshop, MoDELS (p. to appear). Austin, Texas.

Open Challenges

Traceability


(Time) Prediction

Guarantees





Dynamic Structure


Debugging

Validity

45

Guarantees – Safety

Guarantees

46

Automaton Abstraction

Carter, R., & Navarro-López, E. M. (2012). Dynamically-Driven Timed Automaton Abstractions for Proving Liveness of Continuous Systems (pp. 59–74). http://doi.org/10.1007/978-3-642-33365-1_6

Navarro-López, E. M., & O’Toole, M. D. (2017). Automated generation of hybrid automata for multi-rigid-body mechanical systems and its application to the falsification of safety properties. Mathematical and Computer Modelling of Dynamical Systems, 0(0), 1–32. http://doi.org/10.1080/13873954.2017.1369437

Maler, O., & Batt, G. (2008). Approximating Continuous Systems by Timed Automata. In Formal Methods in Systems Biology (pp. 77–89). Berlin, Heidelberg: Springer Berlin Heidelberg. http://doi.org/10.1007/978-3-540-68413-8_6

Guarantees – Numerical Error Bounds

Guarantees

Frehse, G. (2015). An Introduction to Hybrid Automata, Numerical Simulation and Reachability Analysis. In Formal Modeling and Verification of Cyber-Physical Systems (pp. 50–81). Wiesbaden: Springer Fachmedien Wiesbaden. http://doi.org/10.1007/978-3-658-09994-7_3

Bouissou, O., Chapoutot, A., & Djoudi, A. (2013). Enclosing Temporal Evolution of Dynamical Systems Using Numerical Methods. In NASA Formal Methods (pp. 108–123). http://doi.org/10.1007/978-3-642-38088-4_8

Immler, F. (2014). Formally Verified Computation of Enclosures of Solutions of Ordinary Differential Equations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8430 LNCS, pp. 113–127). http://doi.org/10.1007/978-3-642-38088-4_8

47

Guarantees – Correct Orchestration

48

Lee, E. A., & Zheng, H. (2005). Operational semantics of hybrid systems. In M. Morari & L. Thiele (Eds.), Hybrid Systems: Computation and Control (Vol. 3414, pp. 25–53). Springer Berlin Heidelberg. http://doi.org/10.1007/978-3-540-31954-2_2

Broman, D., Greenberg, L., Lee, E. A., Masin, M., Tripakis, S., & Wetter, M. (2015). Requirements for Hybrid Cosimulation Standards. In Proceedings of the 18th International Conference on Hybrid Systems: Computation and Control (pp. 179–188). New York, NY, USA: ACM. http://doi.org/10.1145/2728606.2728629

Iugan, L. G., Boucheneb, H., & Nicolescu, G. (2015). A generic conceptual framework based on formal representation for the design of continuous/discrete co-simulation tools. Design Automation for Embedded Systems, 19(3), 243–275. http://doi.org/10.1007/s10617-014-9156-3

Guarantees

Cavalcanti, A., Woodcock, J., & Amálio, N. (2016). Behavioural Models for FMI Co-simulations. In A. Sampaio & F. Wang (Eds.), (pp. 255–273). Cham: Springer International Publishing. http://doi.org/10.1007/978-3-319-46750-4_15

Open Challenges

Traceability


(Time) Prediction

Guarantees





Dynamic Structure


Debugging

Validity

49

Summary

50

Context

• Past

Vision

• Future

Challenges

• Roadmap

Co-simulation

Thank you!Questions?

51

Documents

Co-simulation: State of the art and open challengesmsdl.cs.mcgill.ca/people/claudio/pres/2017/KeynoteTrento.pdf · 2018-01-04 · The Context 1990 2000 2010 SIMNET(Military Training)