Unified modeling for digital twin of a knowledge-based system design

Abstract

• Integrating digital twin (DT) with model-based systems engineering (MBSE) for the system design complexity analysis and prediction. • A system design digital twin in 5 dimensions (SDDT 5D ) framework is introduced, which is able to map the actual system design process into the corresponding virtual space . • The core concepts and relationships of SDDT 5D are customized by a SysML profile that keeps every view of the system digital model unified. • The system model reduces ambiguity through unifying system requirements, functions, and structures and behaviors. While Model-Based Systems Engineering (MBSE) improves the ambiguity problem of the conventional document-based way, it brings management complexity. Faced with the complexity, one of the core issues that companies care about is how to effectively evaluate, predict, and manage it in the early system design stage. The inaccuracy of contemporary complexity measurement approaches still exits due to the inconsistency between the actual design process in physical space and the theoretical simulation in virtual space. Digital Twin (DT) provides a promising way to alleviate the problem by bridging the physical space and virtual space. Aiming to integrate DT with MBSE for the system design complexity analysis and prediction, based on previous work, an integration framework named System Design Digital Twin in 5 Dimensions was introduced from a knowledge perspective. The framework provides services for design complexity measurement, effort estimation, and change propagation prediction. Then, to represent the system design digital twin in a unified way, a modeling profile is constructed through SysML stereotypes. The modeling profile includes System design digital model in virtual space profile, system services profile, relationships profile and digital twin data profile. Finally, the system design of a cube-satellite space mission demonstrates the proposed unfiled modeling approach.