Resilient digital twin modeling: A transferable approach

Abstract

High-fidelity digital twin models include not only concrete geometric models but also intrinsic models such as mechanism models and data models, which require high information dimensionality. Consequently, modeling, validation, and implementation cycles become challenging and time-consuming. The reuse of existing digital twin models to achieve rapid transference and utilization of models is becoming one of the hot topics in digital twin modeling. To this effect, aiming at how to reuse the multi-dimensional information of existing models to improve modeling efficiency and resilience, this paper proposes a rapid transferable modeling approach for digital twin models. The approach proposes a unified mapping and digital representation of physical entities based on information metamodels, which can rapidly instantiate digital twin assets. Then, the transferable approaches from the geometry dimension, behavior dimension, and algorithm dimension are given respectively, which include the information model reuse, action feature query and rule generation, as well as fine-tuning of data-driven mechanisms and domain adaptive of digital twins. Finally, through a practical case study, the result shows that the efficiency of modeling improves by 60.39%, and the effectiveness of the model after domain adaptation improves by 6.94%.