Architectural design of structural health monitoring (SHM) based digital twin for the next-generation landing gear

Weight reduction on landing gear (LG) supported with a robust structural health monitoring (SHM) system is considered one of the promising approaches to achieving sustainable aircraft. In this research, the main requirements for establishing an SHM-based digital twin for the next-generation LG are formulated through the findings from a systematic literature review, as the inputs to design a scalable digital twin (DT) architecture comprising high-fidelity finite element (FE) models, sensor data, connectivity, AI, physics-informed-AI simulation models, etc. Given landing gear complexity, the model-based systems engineering (MBSE) paradigm was adopted using Capella and MATLAB to create, verify and validate the architecture.