Unified modeling for digital twin of a knowledge-based system design |
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| Affiliation: | 1. School of Mechanical Science and Engineer, Huazhong University of Science and Technology, Wuhan, PR China;2. Bentonville, Arkansas, USA;1. LS2N (Laboratory of Digital Sciences of Nantes, UMR CNRS 6004), University of Nantes, Nantes, France;2. LS2N (Laboratory of Digital Sciences of Nantes, UMR CNRS 6004), Centrale Nantes, Nantes, France;1. National Engineering Research Center for Technological Innovation Method and Tool/School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, China;2. Tianjin Key Laboratory of Power Transmission and Safety Technology for New Energy Vehicles, Tianjin, 300130, China;3. China State Shipbuilding Corporation Limited 716th Research Institute, Lianyungang, 222061, China;4. Faculty of Engineering Technology, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, Netherlands;5. Department of Mechanical Engineering, University of Manitoba, Winnipeg, R3T 5V6, Canada;1. College of Mechanical Engineering, Donghua University, Shanghai, China;2. Department of Mechanical Engineering, The University of Auckland, New Zealand;1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100083, China;2. School of Mechanical Engineering, Shandong University, Jinan, 250061, China;3. LURPA, ENS Paris-Saclay, Universite Paris-Sud, Universite Paris-Saclay, 94235, Cachan, France;4. School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, 2053, Australia;5. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden;6. Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore;1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China;2. Shenyuan Hornors College, Beihang University, Beijing 100083, China;3. Digital Twin International Research Center, International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China |
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| Abstract: | 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. |
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