飞机结构数字孪生的关键一环:多参数飞行实测

With the increasing complexity of aviation equipment and the transformation of maintenance modes, structural digital twin technology has become a key enabler for structural health management and predictive maintenance. Addressing common challenges in digital twin modeling-such as modeling assumption deviations, input uncertainty, and model response mismatch-this study proposes a residual-driven model optimization mechanism based on multiparameter flight measurements. A dynamic closed-loop framework of“ measurement – calibration – residual feedback – model correction” is established, with a rigorous theoretical proof of the residual feedback mechanism’s convergence and a quantitative analysis of error upper bounds. Furthermore, a multi-dimensional, quantifiable evaluation index system for model self-evolution is developed. Engineering verification, using the tail of a certain aircraft as an example, demonstrates that the proposed method effectively reduces model prediction errors under complex operating conditions and improves the accuracy and robustness of fatigue life prediction. The research outcomes provide theoretical support and methodological foundations for the engineering application and intelligent development of structural health management in aircraft.