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Published in: The Journal of Supercomputing 2/2023

05-08-2022

Simulating hybrid SysML models: a model transformation approach under the DEVS framework

Authors: Wu Xinquan, Yan Xuefeng, Li Xingchan, Wang Yongzhen

Published in: The Journal of Supercomputing | Issue 2/2023

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Abstract

As the complexity of the cyber-physical systems (CPSs) increase, system modeling and simulation tend to be performed on different platforms where collaborative modeling activities are performed on distributed clients, while the simulations of systems are carried out in specific simulation environments, such as high-performance computing (HPC). However, there is a great gap between system models usually designed in system modeling language (SysML) and simulation code, and the existing model transformation-based simulation methods and tools mainly focus on either discrete or continuous models, ignoring the fact that the simulation of hybrid models is quite important in designing complex systems. To this end, a model transformation approach is proposed to simulate hybrid SysML models under a discrete event system specification (DEVS) framework. In this approach, to depict hybrid models, simulation-related meta-models with discrete and continuous features are extracted from SysML views without additional extension. Following the meta object facility (MOF), DEVS meta-models are constructed based on the formal definition of DEVS models, including discrete, hybrid and coupled models. Moreover, a series of concrete mapping rules is defined to transform the discrete and continuous behaviors based on the existing state machine mechanism and constraints of SysML, separately. Such an approach may facilitate a SysML system engineer to use a DEVS-based simulator to validate system models without the necessity of understanding DEVS theory. Finally, the effectiveness of the proposed method is verified by a defense system case.

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Metadata
Title
Simulating hybrid SysML models: a model transformation approach under the DEVS framework
Authors
Wu Xinquan
Yan Xuefeng
Li Xingchan
Wang Yongzhen
Publication date
05-08-2022
Publisher
Springer US
Published in
The Journal of Supercomputing / Issue 2/2023
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-022-04654-6

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