Article | Proceedings of the 9th International MODELICA Conference; September 3-5; 2012; Munich; Germany | Functional Mockup Interface 2.0: The Standard for Tool independent Exchange of Simulation Models Linköping University Electronic Press Conference Proceedings
Göm menyn

Title:
Functional Mockup Interface 2.0: The Standard for Tool independent Exchange of Simulation Models
Author:
Torsten Blochwitz: ITI GmbH, Dresden, Germnay Martin Otter: DLR Oberpfaffenhofen, Germany Johan Akesson: Modelon, Lund, Sweden Martin Arnold: University of Halle, Germany Christoph Clauß: Fraunhofer IIS EAS, Dresden, Germany Hilding Elmqvist: Dassault Systèmes, Lund, Sweden Markus Friedrich: SIMPACK, Gilching, Germany Andreas Junghanns: Qtronic, Berlin, Germany Jakob Mauß: Qtronic, Berlin, Germany Dietmar Neumerkel: Daimler AG, Stuttgart, Germany Hans Olsson: Dassault Systèmes, Lund, Sweden Antoine Viel: LMS Imagine, Roanne, France
DOI:
10.3384/ecp12076173
Download:
Full text (pdf)
Year:
2012
Conference:
Proceedings of the 9th International MODELICA Conference; September 3-5; 2012; Munich; Germany
Issue:
076
Article no.:
017
Pages:
173-184
No. of pages:
12
Publication type:
Abstract and Fulltext
Published:
2012-11-19
ISBN:
978-91-7519-826-2
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press; Linköpings universitet


Export in BibTex, RIS or text

The Functional Mockup Interface (FMI) is a tool independent standard for the exchange of dynamic models and for co simulation. The first version; FMI 1.0; was published in 2010. Already more than 30 tools support FMI 1.0. In this paper an overview about the recently published version 2.0 of FMI is given that combines the formerly separated interfaces for Model Exchange and Co-Simulation in one standard. Based on the experience on using FMI 1.0; many small details have been improved and new features ease the usability and increase the performance especially for larger models. Additionally; a free FMI compliance checker will become soon available and FMI models from different tools are made available on the web to simplify testing.

Keywords: Simulation; Co-Simulation; Model Exchange; Functional Mockup Interface (FMI); Functional Mockup Unit (FMU)

Proceedings of the 9th International MODELICA Conference; September 3-5; 2012; Munich; Germany

Author:
Torsten Blochwitz, Martin Otter, Johan Akesson, Martin Arnold, Christoph Clauß, Hilding Elmqvist, Markus Friedrich, Andreas Junghanns, Jakob Mauß, Dietmar Neumerkel, Hans Olsson, Antoine Viel
Title:
Functional Mockup Interface 2.0: The Standard for Tool independent Exchange of Simulation Models
DOI:
http://dx.doi.org/10.3384/ecp12076173
References:
[1] T. Blochwitz; M. Otter; M. Arnold; C. Bausch; C. Clauß; H.Elmqvist; A. Junghanns; J. Mauss; M. Monteiro; T. Neidhold; D. Neumerkel; H. Olsson; J.-V. Peetz; S. Wolf: The Functional Mockup Interface for Tool independent Exchange of Simulation Models. 8th International Modelica Conference. Dresden 2011. Download: http://www.ep.liu.se/ecp/063/013/ecp11063013.pdf

[2] Modelica Association: Modelica – A Unified Object-Oriented Language for Systems Modeling. Language Specification; Version 3.3. May 9; 2012.

[3] E. Chrisofakis; A. Junghanns; C. Kehrer; A. Rink: Simulation-based development of automotive control software with Modelica. 8th International Modelica Conference. Dresden 2011. Download: http://www.ep.liu.se/ecp/063/001/ecp11063001.pdf

[4] A. Abel; T. Blochwitz; A. Eichberger; P. Hamann; U. Rein: Functional Mock-up Interface in Mechatronic Gearshift Simulation for Commercial Vehicles. 9th International Modelica Conference. Mu-nich; 2012.

[5] Abir Ben Khaled; Mongi Ben Gaid; D. Simon; G. Font: Multicore simulation of powertrains using weakly synchronized model partitioning. Accepted for 2012 IFAC Workshop on Engine and Powertrain Control; Simulation and Modeling. Rueil-Malmaison; 2012

[6] S. Gedda; C. Andersson; J. Åkesson; S. Diehl: Derivative-free Parameter Optimization of Functional Mock-up Units. 9th International Modelica Conference. Munich; 2012.

[7] T. Schierz; M. Arnold; C. Clauss: Co-simulation with Communication Step Size Control in an FMI Compatible Master Algorithm. 9th International Modelica Conference. Munich; 2012.

[8] S. Burhenne; M. Pazold; F. Antretter; F. Ohr; S. Herkel; J. Radon: WUFI Plus Therm: Co-Simulation unter Verwendung von Modelica Modellen. Presentation at the Symposium „Integrale Planung und Simulation in Bauphysik und Gebäudetechnik.“ Dresden; March 2012.

Proceedings of the 9th International MODELICA Conference; September 3-5; 2012; Munich; Germany

Author:
Torsten Blochwitz, Martin Otter, Johan Akesson, Martin Arnold, Christoph Clauß, Hilding Elmqvist, Markus Friedrich, Andreas Junghanns, Jakob Mauß, Dietmar Neumerkel, Hans Olsson, Antoine Viel
Title:
Functional Mockup Interface 2.0: The Standard for Tool independent Exchange of Simulation Models
DOI:
https://doi.org10.3384/ecp12076173
Note: the following are taken directly from CrossRef
Citations:
  • Mathias Uslar, Sebastian Rohjans, Christian Neureiter, Filip Pröstl Andrén, Jorge Velasquez, Cornelius Steinbrink, Venizelos Efthymiou, Gianluigi Migliavacca, Seppo Horsmanheimo, Helfried Brunne & Thomas Strasser (2019). Applying the Smart Grid Architecture Model for Designing and Validating System-of-Systems in the Power and Energy Domain: A European Perspective. Energies, 12(2): 258. DOI: 10.3390/en12020258
  • Lars Ivar Hatledal, Arne Styve, Geir Hovlan & Houxiang Zhang (2019). A Language and Platform Independent Co-Simulation Framework Based on the Functional Mock-Up Interface. IEEE Access, 7: 109328. DOI: 10.1109/ACCESS.2019.2933275
  • Jan Šilar, David Polák, Arnošt Mládek, Filip Ježek, Theodore W Kurtz, Stephen E DiCarlo, Jan Živn & Jiri Kofranek (2019). Development of In-Browser Simulators for Medical Education: Introduction of a Novel Software Toolchain. Journal of Medical Internet Research, 21(7): e14160. DOI: 10.2196/14160
  • Alessandro Vittorio Papadopoulo & Alberto Leva (2014). Automating efficiency-targeted approximations in modelling and simulation tools: dynamic decoupling and mixed-mode integration. SIMULATION, 90(10): 1158. DOI: 10.1177/0037549714547296
  • Cláudio Gomes, Casper Thule, David Broman, Peter Gorm Larse & Hans Vangheluwe (2018). Co-Simulation. ACM Computing Surveys, 51(3): 1. DOI: 10.1145/3179993
  • Thomas Schreiber, Sören Eschweiler, Marc Baransk & Dirk Müller (2020). Application of two promising Reinforcement Learning algorithms for load shifting in a cooling supply system. Energy and Buildings, 229: 110490. DOI: 10.1016/j.enbuild.2020.110490
  • Imad M. Khan, Makrand Datar, Wulong Sun, Georg Festag, T Bin Juan & Natalie Remisoski (2017). Multibody Dynamics Cosimulation for Vehicle NVH Response Predictions. SAE International Journal of Vehicle Dynamics, Stability, and NVH, 1(2): 131. DOI: 10.4271/2017-01-1054
  • Cinzia Bernardeschi, Andrea Domenic & Maurizio Palmieri (2020). Formalization and co-simulation of attacks on cyber-physical systems. Journal of Computer Virology and Hacking Techniques, 16(1): 63. DOI: 10.1007/s11416-019-00344-9
  • Christian Scheifele, Alexander Ver & Oliver Riedel (2019). Real-time co-simulation for the virtual commissioning of production systems. Procedia CIRP, 79: 397. DOI: 10.1016/j.procir.2019.02.104
  • Cinzia Bernardeschi, Pierpaolo Dini, Andrea Domenici, Maurizio Palmier & Sergio Saponara (2020). Formal Verification and Co-Simulation in the Design of a Synchronous Motor Control Algorithm. Energies, 13(16): 4057. DOI: 10.3390/en13164057
  • Severin Sadjina, Lars T. Kyllingstad, Stian Skjon & Eilif Pedersen (2017). Energy conservation and power bonds in co-simulations: non-iterative adaptive step size control and error estimation. Engineering with Computers, 33(3): 607. DOI: 10.1007/s00366-016-0492-8
  • Constantin-Bălă Zamfiresc & Mihai Neghină (2019). Collaborative development of a CPS-based production system. Procedia Computer Science, 162: 579. DOI: 10.1016/j.procs.2019.12.026
  • Xi Zheng, Christine Julien, Hongxu Chen, Rodion Podorozhn & Franck Cassez (2017). Real-Time Simulation Support for Runtime Verification of Cyber-Physical Systems. ACM Transactions on Embedded Computing Systems, 16(4): 1. DOI: 10.1145/3063382
  • Jiří Fürs & Zdeněk Žák (2019). Numerical simulation of unsteady flows through a radial turbine. Advances in Computational Mathematics, 45(4): 1939. DOI: 10.1007/s10444-019-09670-4
  • A. Rousset, R. Bavier & V. Vuillerme (2018). Development and application of a multi-domain dynamic model for direct steam generation solar power plant. IFAC-PapersOnLine, 51(2): 777. DOI: 10.1016/j.ifacol.2018.04.008
  • Gregorio Lopez, Javier Matanza, David De La Vega, Marta Castro, Amaia Arrinda, Jose Ignacio Moren & Alberto Sendin (2019). The Role of Power Line Communications in the Smart Grid Revisited: Applications, Challenges, and Research Initiatives. IEEE Access, 7: 117346. DOI: 10.1109/ACCESS.2019.2928391
  • Severin Sadjina, Lars Tandle Kyllingstad, Martin Rindarøy, Stian Skjong, Vilmar Æsø & Eilif Pedersen (2019). Distributed Co-simulation of Maritime Systems and Operations. Journal of Offshore Mechanics and Arctic Engineering, 141(1): . DOI: 10.1115/1.4040473
  • Dag Fritzson, Robert Brau & Jan Hartford (2018). Composite modelling in 3-D mechanics utilizing Transmission Line Modelling (TLM) and Functional Mock-up Interface (FMI). Modeling, Identification and Control: A Norwegian Research Bulletin, 39(3): 179. DOI: 10.4173/mic.2018.3.4
  • Håkan Andersson, Peter Nordin, Thomas Borrvall, Kjell Simonsson, Daniel Hilding, Mikael Schill, Petter Kru & Daniel Leidermark (2017). A co-simulation method for system-level simulation of fluid–structure couplings in hydraulic percussion units. Engineering with Computers, 33(2): 317. DOI: 10.1007/s00366-016-0476-8
  • D.H. Blum, K. Arendt, L. Rivalin, M.A. Piette, M. Wette & C.T. Veje (2019). Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems. Applied Energy, 236: 410. DOI: 10.1016/j.apenergy.2018.11.093
  • Nicolai Pedersen, Jan Madse & Morten Vejlgaard-Laursen (2015). Co-Simulation of Distributed Engine Control System and Network Model using FMI & SCNSL. IFAC-PapersOnLine, 48(16): 261. DOI: 10.1016/j.ifacol.2015.10.290
  • Maurizio Palmieri, Cinzia Bernardesch & Paolo Masci (2020). A framework for FMI-based co-simulation of human–machine interfaces. Software and Systems Modeling, 19(3): 601. DOI: 10.1007/s10270-019-00754-9
  • Awad Mukbil, Umut Dura & Sven Hartmann (2019). Conformance testing of FMI calling sequence for simulation environments. International Journal of Modeling, Simulation, and Scientific Computing, 10(02): 1950008. DOI: 10.1142/S1793962319500089
  • Matthias Mitterhofer, Georg Ferdinand Schneider, Sebastian Stratbücke & Simone Steiger (2019). Semantics for assembling modular components in a scalable building performance simulation. Journal of Building Performance Simulation, 12(2): 145. DOI: 10.1080/19401493.2018.1492020
  • Pierluigi Nuzzo, Alberto L. Sangiovanni-Vincentelli, Davide Bresolin, Luca Gerett & Tiziano Villa (2015). A Platform-Based Design Methodology With Contracts and Related Tools for the Design of Cyber-Physical Systems. Proceedings of the IEEE, 103(11): 2104. DOI: 10.1109/JPROC.2015.2453253
  • Demetrios Joanno & Roy Kalawsky (2018). A Novel “Resilience Viewpoint” to aid in Engineering Resilience in Systems of Systems (SoS). INCOSE International Symposium, 28(1): 835. DOI: 10.1002/j.2334-5837.2018.00519.x
  • Kunpeng Wang, Peer-Olaf Sieber & Darren Robinson (2017). Towards Generalized Co-simulation of Urban Energy Systems. Procedia Engineering, 198: 366. DOI: 10.1016/j.proeng.2017.07.092
  • Felix Gaisbauer, Eva Lampen, Philipp Agethe & Enrico Rukzio (2020). Combining heterogeneous digital human simulations: presenting a novel co-simulation approach for incorporating different character animation technologies. The Visual Computer, : . DOI: 10.1007/s00371-020-01792-x
  • Tobias Jung, Nasser Jazdi, Stefan Krauß, Christian Köllne & Michael Weyrich (2020). Hardware-in-the-Loop Simulation for a Dynamic Co-Simulation of Internet-of-Things-Components. Procedia CIRP, 93: 1334. DOI: 10.1016/j.procir.2020.03.073
  • Stian Skjong, Martin Rindarøy, Lars T. Kyllingstad, Vilmar Æsø & Eilif Pedersen (2018). Virtual prototyping of maritime systems and operations: applications of distributed co-simulations. Journal of Marine Science and Technology, 23(4): 835. DOI: 10.1007/s00773-017-0514-2
  • Ruoyang Yuan, Tom Fletcher, Ahmed Ahmedov, Nikolaos Kalantzis, Antonios Pezouvanis, Nilabza Dutta, Andrew Watso & Kambiz Ebrahimi (2020). Modelling and Co-simulation of hybrid vehicles: A thermal management perspective. Applied Thermal Engineering, 180: 115883. DOI: 10.1016/j.applthermaleng.2020.115883
  • Yingguang Chu, Lars Ivar Hatledal, Vilmar Æsøy, Sören Ehler & Houxiang Zhang (2018). An Object-Oriented Modeling Approach to Virtual Prototyping of Marine Operation Systems Based on Functional Mock-Up Interface Co-Simulation. Journal of Offshore Mechanics and Arctic Engineering, 140(2): . DOI: 10.1115/1.4038346
  • Eva Lampen, Jonas Teuber, Felix Gaisbauer, Thomas Bär, Thies Pfeiffe & Sven Wachsmuth (2019). Combining Simulation and Augmented Reality Methods for Enhanced Worker Assistance in Manual Assembly. Procedia CIRP, 81: 588. DOI: 10.1016/j.procir.2019.03.160
  • Cornelius Steinbrink, Marita Blank-Babazadeh, André El-Ama, Stefanie Holly, Bengt Lüers, Marvin Nebel-Wenner, Rebeca Ramírez Acosta, Thomas Raub, Jan Schwarz, Sanja Stark, Astrid Nieß & Sebastian Lehnhoff (2019). CPES Testing with mosaik: Co-Simulation Planning, Execution and Analysis. Applied Sciences, 9(5): 923. DOI: 10.3390/app9050923
  • Jannis Sinnemann, Matthias Bartelt, Anton Strahilo & Bernd Kuhlenkötter (2020). Architecture for Simulation and Optimization of Energy Consumption of Automated Production Systems. Procedia CIRP, 93: 1241. DOI: 10.1016/j.procir.2020.04.003
  • Walid Taha, Yingfu Zeng, Adam Duracz, Xu Fei, Kevin Atkinson, Paul Brauner, Robert Cartwrigh & Roland Philippsen (2017). Developing a first course on cyber-physical systems. ACM SIGBED Review, 14(1): 44. DOI: 10.1145/3036686.3036692
  • Dehui Du, Tong Gu & Yao Wang (2020). DSML4CS. International Journal of Web Services Research, 17(2): 59. DOI: 10.4018/IJWSR.2020040104
  • Atiyah Elsheikh (2015). An equation-based algorithmic differentiation technique for differential algebraic equations. Journal of Computational and Applied Mathematics, 281: 135. DOI: 10.1016/j.cam.2014.12.026
  • Tao Ma, Shaukat Al & Tao Yue (2019). Modeling foundations for executable model-based testing of self-healing cyber-physical systems. Software & Systems Modeling, 18(5): 2843. DOI: 10.1007/s10270-018-00703-y
  • Mihai Neghina, Constantin-Bala Zamfiresc & Ken Pierce (2020). Early-stage analysis of cyber-physical production systems through collaborative modelling. Software and Systems Modeling, 19(3): 581. DOI: 10.1007/s10270-019-00753-w
  • Gregor Jochmann, Florian Blümel, Oliver Ster & Jürgen Roßmann (2014). The Virtual Space Robotics Testbed: Comprehensive Means for the Development and Evaluation of Components for Robotic Exploration Missions. KI - Künstliche Intelligenz, 28(2): 85. DOI: 10.1007/s13218-014-0293-4
  • Gregor Thiele, Knut Grabowsk & Jörg Krüger (2019). Energieeffizienz in der intelligenten Fabrik. ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, 114(9): 569. DOI: 10.3139/104.112139


  • Responsible for this page: Peter Berkesand
    Last updated: 2019-11-06