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

Abstract

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.

Co-simulation platform for geometric design, trajectory control and guidance of racing drones

“…The body torque false( τ b o d y false) is usually a formulation linked with the differences between the speeds of the rotors. 46,47,27 In addition, they are mapped as input false( I s false) to the system for the control design. The difference between the thrust forces false( T n x false) generated by the motor-propeller assembly and the mass distribution false( I n x false) along each axis will be associated with the Torque to establish communication between NX and Matlab. Subsequently, they were tracked as an input false( I s false) to the system for control design in Simulink (3).…”

“…[20][21][22][23] However, linking the data with modelbased design software needs further support to enrich design methods. [24][25][26][27] The work aims to offer a single-based simulation platform. It is for geometric design, trajectory control and guidance of racing drones.…”

Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

“…Currently, MASON ontology has been used for establishing semantics, towards zero-defect manufacturing [ 13 , 14 ]. The use of ontology-assisted co-simulation and cognitive digital twin is a main driver for the future of manufacturing utilizing real-time data for status monitoring, fault diagnosis, and performance prediction [ 15 ]. The need for standardization and semantics has been highlighted in the materials characterization field, and data exchange procedures have also been major topics of workshops and forums of experts [ 3 ].…”

Exploring the concept of Cognitive Digital Twin from model-based systems engineering perspective

“…Particularly, when using upper-level ontologies such as Basic Formal Ontology (BFO) [22] and Industrial Ontologies Foundry (IOF) domain ontologies, different ontology models can be integrated using a unified format to promote data interoperability. For example, the IOF-MBSE domain ontology is used in [23] to describe co-simulation models based on a standardized artifact representation. Then, using the same ontology, semantic models are used to represent the model structure of verification models in order to realize DT integration [24].…”