A Digital Twin Platform for Smart Aircraft
Z. Sharif Khodaei, F. Aliabadi
Online Health Management (HM) plays a pivotal role in optimizing the lifecycle of aircraft while ensuring safety, reliability, and structural integrity. During service, aircraft structures experience complex cyclic loading, making accurate load analysis essential for effective life monitoring and health management. Capturing realistic load histories throughout operation enables more precise estimation of remaining useful life, supports optimization of design limits for improved material utilization, and enhances the fidelity of simulation models by aligning them with real operating conditions. Continuous load monitoring thus represents a key enabler for transitioning from conventional safe-life design philosophies toward advanced damage-tolerant and condition-based approaches. However, despite significant progress in sensing technologies and data processing methods, large-scale sensor deployment remains constrained by financial and logistical limitations, posing challenges to the realization of fully “smart” aircraft. This study presents recent developments from the H2020 AVATAR project, which aims to establish a digital twin framework for optimizing aircraft lifecycle performance. A novel sensing skin has been developed to enable the integration of a sparse sensor network on composite wing structures. The work explores the use of machine learning (ML) to enhance load monitoring capabilities, demonstrating that ML techniques can maintain high predictive accuracy in reconstructing structural responses, such as forces, strains, and stresses, even with limited sensor data (from strain gauges and accelerometers). The proposed approach is validated on a composite wing under realistic operational loading, highlighting its robustness and potential for real-time structural health monitoring in demanding aerospace environments.