Co-Simulation – an Empirical Survey: Applications, Recent Developments and Future Challenges

Schweiger, Gerald; Engel, Georg P.; Schoeggl, Josef; Hafner, Irene; Gomes, Cláudio; Nouidui, Thierry Stephane

doi:10.11128/arep.55.a55286

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…To facilitate the possibility of consensus between different research groups and appliers of co-simulation, colleagues fromn ational and international research groups and myself have collaboratively developed and conducted at wo-stage Delphi study withm ore than 50 experts.T he aim of this study wast oh elp identifying current research needs,c hallenges and promising standards in co-simulation. Results of this study (published in (Schweiger et al 2019a;Schweiger et al 2018Schweiger et al , 2019b) are summarized in the following.…”

Section: Chapter 4 Empirical Surveyonco-simulationmentioning

confidence: 97%

Cooperative and Multirate Simulation: Analysis, Classification and New Hierarchical Approaches

Hafner¹

2022

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In dieser Dissertation werden hierarchische Co-Simulationsmethoden vorgestellt und hinsichtlich Genauigkeit, Fehlerakkumulation und Stabilität untersucht. Co-Simulation bezeichnet die Kopplung vonz weio der mehr Simulationen, die sich in dem verwendeten Simulationstool, Lösungsalgorithmus oder zumindest der Solverschrittweite unterscheiden. Methoden dieser Arthaben sich mittlerweile als unentbehrliches Instrument zur gesamtheitlichen Abbildung komplexerSysteme aus unterschiedlichen Anwendungsgebieten etabliert. Der vielseitige Ursprung und das breite Spektrum an wissenschaftlichen Lösungsansätzen für Problemstellungen dieser Arthaben dazu geführt, dass einige Begriffe unterschiedlich aufgefasst oder äquivalente Methoden verschieden bezeichnet werden. Dieser Umstand gab den Anstoß für die Zusammenführung, Klärung und Vereinheitlichung des Vokabulars in diesem Forschungsbereich zu Beginn dieser Arbeit. Anschließend folgt ein Überblicküber gängige Multirate-und Co-Simulationsmethoden, begonnen bei Verfahren für gewöhnliche Differentialgleichungen über gekoppelte differentialalgebraische Gleichungssysteme bis hin zur Zusammenführung starkunterschiedlicher Ansätze, wie etwad iskret und kontinuierlich dargestellten Te ilsystemen. Zudem wurde eine empirische Studie mit über fünfzig Te ilnehmenden in Zusammenarbeit mit Kollegen nationaler und internationaler Forschungsgruppen ausgearbeitet und durchgeführt,deren Ergebnisse gängige Standards,H erausforderungen und Forschungsbedarf im Bereich der Co-Simulation aufzeigen. Ob ihrer Vielfalt können Multirate-und Co-Simulationsmethoden anhand unterschiedlicher Gesichtspunkte strukturiertwerden. Diese werden gleichzeitig mit der Klassifikation ausgewählter Literatur ebenfalls in dieser Arbeit präsentiert. Ein Nebenprodukt letzterer stellt unter anderem das Netzwerka nA utorInnen der betrachteten Publikationen dar,i nd em deutlich wird, dass einige voni hnen für internationale Zusammenarbeit offen sind,w ährend andere ihre Forschungstätigkeit bevorzugt innerhalb der eigenen Institution vornehmen. Das Hauptaugenmerkdieser Dissertation liegt auf der Entwicklung und Untersuchung hierarchischer Co-Simulationsmethoden. Diese bezeichnen Co-Simulationen, die unter sich weitere Co-Simulationen, gegebenenfalls auf mehreren Ebenen, koordinieren. Fehlerschätzungen zeigen, dass durch die Einführung weiterer Levels keine zusätzlichen Fehler hinzukommen und Nullstabilität, gesonderta uf jeder Ebene,a nalog zu herkömmlicherC o-iii Simulation untersuchtw erden kann. Benchmark-Tests anhand gekoppelter Dahlquist-Gleichungen zeigen, dass numerische Stabilität sogar erhöht werden kann, sofernSysteme,die untereinander in höherem Ausmaß vonW erten der jeweilig anderen abhängen, die Möglichkeit haben, gesonderta nw eiteren Kommunikationszeitpunkten Daten auszutauschen. Dadurch kann die Genauigkeit erhöht und qualitatives Verhalten erhalten werden, ohne die gesamte Co-Simulation zu verlangsamen. Insgesamt stellt hierarchische Co-Simulation einen innovativen Ansatz dar,der vielversprechende Ergebnisse h...

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Section: Chapter 4 Empirical Surveyonco-simulationmentioning

confidence: 97%

Cooperative and Multirate Simulation: Analysis, Classification and New Hierarchical Approaches

Hafner¹

2022

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“…Finally, it discusses some of the advanced techniques. We refer the reader to [19,27] for other motivations of co-simulation. For indepth discussions about the different kinds of orchestration algorithms and advanced techniques, please see [17,Section 4] and references thereof.…”

Section: Summary and Further Readingmentioning

confidence: 99%

Co-Simulation of Continuous Systems: A Hands-on Approach

Gomes

Vangheluwe

2019

2019 Winter Simulation Conference (WSC)

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exploited to more efficiently simulate them, making it difficult to find a technique that fits all kinds of models.Co-simulation is a generalized form of simulation, where a coupled system is simulated through the composition of simulation units [17,19,25]. Each unit is broadly defined as a black box capable of exhibiting behaviour, consuming inputs and producing outputs, over simulated time.Many of the problems occurring in co-simulations are due to the ill composition of simulation units that represent continuous systems [16]. As such, we argue that having a basic knowledge of numerical (co-)simulation can help practitioners debug, and even improve the performance of, existing co-simulations.In this tutorial, we aim to provide the reader with a basic understanding of numerical algorithms, and we show how attempting to simulate an heterogeneous system naturally leads to co-simulation. Upon completion, the reader should know the many different possible co-simulation approaches, the main concepts involved, and what their tradeoffs are. Furthermore, the reader will be equipped to understand the more advanced concepts in the co-simulation literature.The next section provides a top-down overview of all the concepts that will be discussed here. This concept map will be revisited in all other sections. In the sections after, each concept will be discussed, in a bottom up manner, so as to increase the complexity gradually. The large unfilled dots represent input values, and the smaller unfilled dots represent their extrapolations, as computed by the simulation units. One can see that there is a difference between the values calculated by the extrapolation functions opposed to the actual input, due to the gap between the larger and smaller unfilled dots at t + H. The black dots represents outputs. As illustrated, S 1 and S 2 perform small steps of respectively h 1 and h 2 internally, until the time t + H is reached.The communication step size can either be fixed (defined before the co-simulation starts and constant throughout its execution), or adaptive (the orchestrator determines the best value to be used whenever it asks the simulation units to compute).The communication approach encodes the order in which the simulation units are given inputs and instructed to compute the next interval. Figure 3 summarizes the multiple types of orchestration algorithms using time diagrams.

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“…Among other aspects, computational systems and communication networks need to be combined with physical systems (E. A. Lee and Seshia 2017); further, co-simulation approaches are needed to couple different tools and modeling approaches (e.g., physical and machine learning) (Schweiger, Engel, et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Decarbonization of Industrial Energy Systems: A Case Study of Printed Circuit Board manufacturing

Tugores¹,

Birngruber²,

Fluch³

et al. 2021

Linköping Electronic Conference Proceedings

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Decarbonization of industry is a key challenge to achieve the Paris climate goals. Digitalization of the industry is a cornerstone of this journey. In this paper we present our modelling work towards the creation of a Digital Energy Twin of the energy supply system of a printed circuit board manufacturing by means of a classical use case, system design optimization. The simulation approach allowed us to fairly compare the improvements done in the energy supply system by evaluating those under the same operating conditions. Integration of chiller's waste heat can cover most of the low temperature grid heat demand while the additional generation of chilled water reduces the amount of water pump from and back to the river.

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Co-Simulation – an Empirical Survey: Applications, Recent Developments and Future Challenges

Cited by 7 publications

References 3 publications

Cooperative and Multirate Simulation: Analysis, Classification and New Hierarchical Approaches

Cooperative and Multirate Simulation: Analysis, Classification and New Hierarchical Approaches

Co-Simulation of Continuous Systems: A Hands-on Approach

Decarbonization of Industrial Energy Systems: A Case Study of Printed Circuit Board manufacturing

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