Fine-grained decomposition of complex digital twin systems driven by semantic-topological-dynamic associations

Abstract

Complex digital twin (DT) systems offer a robust solution for design, optimization, and operational management in industrial domains. However, in an effort to faithfully replicate the dynamic changes of the physical world with high fidelity, the excessively intricate and highly coupled system components present modeling challenges, making it difficult to accurately capture the system's dynamic characteristics and internal correlations. Particularly in scenarios involving multi-scale and multi-physics coupling, complex systems lack adequate fine-grained decomposition (FGD) methods. This results in cumbersome information exchange and consistency maintenance between models of different granularities. To address these limitations, this paper proposes a method for multi-level decomposition of complex twin models. This method constructs a FGD model for DTs by integrating three key correlation mechanisms between components: semantic association, dynamic association, and topological association. The decomposed model achieves reasonable simplification and abstraction while maintaining the accuracy of the complex system, thereby balancing computational efficiency and simulation precision. The case study validation employed a marine diesel engine piston production line to test the proposed decomposition method, verifying the effectiveness of the approach.