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

• Published in: International journal of production research Vol. 60; no. 24; pp. 7588 - 7609
• Main Authors: Li, Yuanfu, Chen, Jinwei, Hu, Zhenchao, Zhang, Huisheng, Lu, Jinzhi, Kiritsis, Dimitris
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 17.12.2022; Taylor & Francis LLC
• Subjects: Aerospace engineering; Algorithms; Co-simulation; Cognition; Cognition & reasoning; cognitive twin; Gas turbines; Internet of Things; model based systems engineering; Modelling; Ontology; ontology model; semantic modeling; Simulation; Topology
• ISSN: 0020-7543; 1366-588X; 1366-588X
• DOI: 10.1080/00207543.2021.1971318

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 co-simulation of complex engineered systems is solved by the unified ontology modelling approach and the architecture of CT.