Ontological Integration of Semantics and Domain Knowledge in Hardware and Software Co-simulation of the Smart Grid

Since the complex engineered system involves multi-disciplinary, co-simulation is the key technique to the performance analysis. However, the co-simulation is hindered by heterogeneous sub-systems and ununified environments. In this paper, a Cognitive Twin (CT) to support the co-simulation of the complex engineered system is introduced. It is a generic approach that can be applied in many complex engineered systems such as the aerospace field, automotive system, the Internet of Things, manufacturing systems, etc. CT adopts an ontology model to develop cognition capability based on CT architecture. Then, a unified ontology modelling approach based on GOPPRR (graph, object, point, property, role, relationship) is presented to support an accurate semantic description of the topology between digital entities that use FMI 2.0 as the interconnection standard. Besides, four types of information are included in the ontology model to form the knowledge in co-simulation. Finally, the co-simulation is automatically executed using the cognition capability. Furthermore, a master-slave algorithm is deployed to establish a unified co-simulation environment. The flexibility of CT is evaluated using a gas turbine case. The results demonstrate that the complication in the cosimulation of complex engineered systems is solved by the unified ontology modelling approach and the architecture of CT.

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NAVIDRO, a CARES architectural style for configuring drone co-simulation

Salmon,

Pillain,

Guillou

et al. 2024

ACM Trans. Embed. Comput. Syst.

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One primary objective of drone simulation is to evaluate diverse drone configurations and contexts aligned with specific user objectives. The initial challenge for simulator designers involves managing the heterogeneity of drone components, encompassing both software and hardware systems, as well as the drone’s behavior. To facilitate the integration of these diverse models, the Functional Mock-Up Interface (FMI) for Co-Simulation proposes a generic data-oriented interface. However, an additional challenge lies in simplifying the configuration of co-simulation, necessitating an approach to guide the modeling of parametric features and operational conditions such as failures or environment changes. The paper addresses this challenge by introducing CARES, a Model-Driven Engineering (MDE) and component-based approach for designing drone simulators, integrating the Functional Mock-Up Interface (FMI) for Co-Simulation. The proposed models incorporate concepts from Component-Based Software Engineering (CBSE) and FMI. The NAVIDRO architectural style is presented for designing and configuring drone co-simulation. CARES utilizes a code generator to produce structural glue code (Java or C++), facilitating the integration of FMI-based domain-specific code. The approach is evaluated through the development of a simulator for navigation functions in an Autonomous Underwater Vehicle (AUV), demonstrating its effectiveness in assessing various AUV configurations and contexts.

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Ontological Integration of Semantics and Domain Knowledge in Hardware and Software Co-simulation of the Smart Grid

Cited by 3 publications

References 28 publications

Co-simulation of Socio-Technical Energy Systems: An Interdisciplinary Design Process

Co-simulation of Socio-Technical Energy Systems: An Interdisciplinary Design Process

Co-simulation of complex engineered systems enabled by a cognitive twin architecture

NAVIDRO, a CARES architectural style for configuring drone co-simulation

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