Uncertainty technologies in aircraft digital strength twins

The rapid development of the aviation industry has introduced triple challenges-timeliness, precision, and intelligence-for next-generation aircraft's full lifecycle management (design, manufacturing, and operation/maintenance). As an enabling technology of the Fourth Industrial Revolution, digital twin has emerged as a core solution for aircraft structural health monitoring and performance prediction, leveraging its real-time interactivity, multi-source heterogeneous data fusion, and high-fidelity modeling capabilities. However, multi-source uncertainties-including material property dispersion during aircraft development, manufacturing tolerances, and in-service structural accidental damage or complex load disturbances-pose significant challenges to the credibility of aircraft digital strength twins. This paper addresses multidimensional uncertainty issues across an aircraft's full lifecycle (design verification, production, operation, and maintenance) while incorporating key technical requirements such as high-precision load identification and high-confidence structural damage diagnosis. It proposes a conceptual framework and technical architecture for aircraft digital strength twins. To enable the engineering implementation of digital strength twin systems, we systematically organize critical technologies including distributed sensor network construction, high-performance computing platform integration, multi-source data fusion, and dynamic model updating, which provide robust hardware/software foundations for engineering applications and efficient uncertainty resolution. Guided by core requirements for interactive real-time performance, model fidelity, and analytical refinement, this study takes uncertainty-driven digital strength twin entities as its research focus. It conducts in-depth analyses of uncertainty propagation mechanisms, key technical pathways, and future development directions across three core processes: load twin, structural damage state twin, and mechanical behavior/performance twin.