Towards Modular Language Design Using Language Fragments: The Hybrid Systems Case Study

Mustafiz, Sadaf; Barroca, Bruno; Gomes, Cláudio; Vangheluwe, Hans

doi:10.1007/978-3-319-32467-8_68

Cited by 1 publication

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“…While a generic wrapper based on the underlying model of computation of the SU can be used, as done in [218] [73], the realization of any of the approaches Hybrid DE or Hybrid CT depends on the concrete co-simulation scenario and the features of the SUs [46] [192], as shown with the thermostat example. There is simply no best choice of wrappers for all scenarios.…”

Section: Semantic Adaptation and Model Compositionmentioning

confidence: 99%

Co-Simulation

et al. 2018

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Modeling and simulation techniques are today extensively used both in industry and science. Parts of larger systems are, however, typically modeled and simulated by different techniques, tools, and algorithms. In addition, experts from different disciplines use various modeling and simulation techniques. Both these facts make it difficult to study coupled heterogeneous systems. Co-simulation is an emerging enabling technique, where global simulation of a coupled system can be achieved by composing the simulations of its parts. Due to its potential and interdisciplinary nature, co-simulation is being studied in different disciplines but with limited sharing of findings. In this survey, we study and survey the state-of-the-art techniques for co-simulation, with the goal of enhancing future research and highlighting the main challenges. To study this broad topic, we start by focusing on discrete-event-based co-simulation, followed by continuous-time-based co-simulation. Finally, we explore the interactions between these two paradigms, in hybrid co-simulation. To survey the current techniques, tools, and research challenges, we systematically classify recently published research literature on co-simulation, and summarize it into a taxonomy. As a result, we identify the need for finding generic approaches for modular, stable, and accurate coupling of simulation units, as well as expressing the adaptations required to ensure that the coupling is correct.

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Section: Semantic Adaptation and Model Compositionmentioning

confidence: 99%