飞机结构数字孪生的关键一环：多参数飞行实测

doi:10.7527/S1000-6893.2025.32375

Abstract

Abstract:

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.

Key words: digital twin, multi-dimensional flight parameter measurement, model calibration, residual feedback, structural health monitoring

CLC Number:

Ran ZHUO, Chuliang YAN. A key component in digital twin of aircraft structures: Multi-dimensional flight parameter measurements[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 532375.

Figures/Tables 5

Table 1

Table 2

Table 3

Fig.1

Table 4

Comparison of key evaluation metrics for model optimization

评价指标	初始模型	迭代优化后	结果
残差归一化指数	0.072 8	0.019 4	误差降低73%
决定系数 $R 2$	0.759 5	0.985 3	显著提升
参数方差	245.97	167.73	降低31%，收敛更集中

Table 4

References 39

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参数类别	示例参数	典型传感器类型
结构响应类	应变、加速度、位移	电阻应变计、加速度计、激光位移传感器
气动特性类	动压、静压、攻角、侧滑角	五孔探头、压差传感器、迎角/侧滑角传感器
控制输入类	驾驶杆行程、舵面偏角、操纵力矩	电位计、电压/电流传感器、飞控律接口
环境工况类	温度、湿度、大气密度、电磁干扰	温湿度传感器、电磁干扰探头
导航状态类	高度、地速、加速度、马赫数、航向	GPS、INS、空速计、高度表
系统状态类	发动机转速、燃油压力、电气状态	转速计、压力传感器、电压表、发动机诊断接口
结构健康类	裂纹长度、损伤指标（如能量耗散、声发射等）	导波阵列、FBG、声发射传感器、散斑干涉仪等

功能	数学模型及方法	工程意义
参数自适应更新	梯度下降/卡尔曼滤波	持续修正模型静态偏差，保持精度一致性
贝叶斯反演融合	粒子滤波/贝叶斯推断	描述不确定性，支持可信度评估
子模型切换机制	区域残差超阈值+迟滞判据	动态调用高保真模型，应对局部失效
异常抑制与加权	残差加权+虚拟传感	屏蔽异常数据，保障更新稳定性

工况描述	飞行实测载荷均值/kN	风洞预测载荷均值/kN，相对偏差/%	初始模型均值/kN，相对偏差/%	迭代优化后均值/kN，相对偏差/%
起飞大迎角俯仰拉起	68.2	78.4，14.90	78.5，15.10	70.8，3.81
大坡度盘旋	50.5	55.6，10.10	55.5，9.90	51.5，1.98
稳态巡航（1g）	35.4	36.2，2.26	36.0，1.69	35，-1.13
螺旋桨滑流激励下爬升	47.6	46.2，-2.94	46.5，-2.31	47.2，-0.84

A key component in digital twin of aircraft structures: Multi-dimensional flight parameter measurements

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