飞机结构数字孪生的关键一环：多参数飞行实测
收稿日期: 2025-06-06
修回日期: 2025-06-24
录用日期: 2025-07-21
网络出版日期: 2025-08-11
A key component in digital twin of aircraft structures: Multi-dimensional flight parameter measurements
Received date: 2025-06-06
Revised date: 2025-06-24
Accepted date: 2025-07-21
Online published: 2025-08-11
随着航空装备复杂性的提升与运维模式的转型，结构数字孪生成为实现结构健康管理与预测性维护的关键技术。针对当前孪生建模中普遍存在的建模假设偏差、输入信息不确定与模型响应失配等难题，提出了一种基于多参数飞行实测的残差驱动模型优化机制。建立了数字孪生模型的“实测—校准—残差反馈—模型修正”动态闭环路径，系统证明了残差反馈闭环机制的理论收敛性，提出了误差上界的严格定量分析方法，并建立了多维度、可量化的模型自进化评价指标体系。以某型飞机尾翼为例的工程验证表明，所提方法可有效降低复杂工况下的模型预测误差，提升疲劳寿命预测的准确性和鲁棒性。研究成果为飞机结构健康管理的工程化应用和智能化提供了理论支撑与方法基础。
卓然 , 闫楚良 . 飞机结构数字孪生的关键一环：多参数飞行实测[J]. 航空学报, 2025 , 46(19) : 532375 -532375 . DOI: 10.7527/S1000-6893.2025.32375
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 multi-parameter 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.
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