融合数据自适应BPNN的倾转旋翼机回转颤振边界预测

doi:10.7527/S1000-6893.2025.32159

Abstract

Abstract:

To address the aeroelastic instability issues of tiltrotor aircraft， a prediction method for whirl flutter boundary of tiltrotor aircraft based on Back-Propagation Neural Network （BPNN） was proposed. Firstly， a multi-modal coupled aeroelastic stability analysis model for tiltrotor aircraft was established based on Hamilton’s principle and multi-body dynamics methods. Secondly， minimal modal damping ratio data under strongly correlated parameters characterizing system stability were generated， and an artificial neural network prediction model was constructed. Finally， an adaptive data refinement method was proposed to enhance the prediction accuracy of the neural network model. The results show that within the training range， the maximum error between the calculated and predicted values is 3.24%， with an average relative error of 0.031%； outside the training range， the maximum error is 6.51%， and the average relative error is 0.089%. The trained BPNN model exhibits good generalization and high fitting accuracy， enabling efficient and high-precision predictions with fewer sample data， both within and outside the training range. The refinement method effectively improves prediction accuracy， particularly excelling in the prediction of the critical point of whirl flutter， significantly mitigating the peak-to-peak fluctuation effects. Moreover， BPNN provides new tools and methods for handling large-scale complex data， offering new perspectives for the research and application of aeroelastic dynamics in tiltrotor aircraft.

Key words: BP neural network, tiltrotor aircraft, whirl flutter, adaptive data, boundary prediction

CLC Number:

V215.34

Lixiong ZHENG, Zhe CHEN, Xin WANG, Qijun ZHAO. Prediction of whirl flutter boundary for tiltrotor aircraft based on BPNN with adaptive data[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(S1): 732159.

Figures/Tables 19

Fig.1

Fig.2

Fig.3

Table 1

Basic parameters of MTR model

参数	数值
桨叶片数N_b	3
旋翼半径R_a/m	0.723 9
挥舞变距调节系数 $K p$	-0.268
升力线斜率 $a α$	5.936
巡航转速 $Ω$ /（rad∙s^-1）	109.96
桨叶挥舞惯量 $I b$ /（kg∙m²）	0.055 2
机翼展长 $Y t w$ /m	0.870
机翼弦长 $C w$ /m	0.392
桅杆高度 $h$ /m	0.243
短舱重心偏置 $e p$ /m	-0.032 7
机翼前掠角 $w 3$ /（°）	0
机翼垂弯刚度 $K q 1$ /（N∙m²）	2.62×10⁴
机翼弦弯刚度 $K q 2$ /（N∙m²）	1.159×10⁵
机翼扭转刚度 $K ϕ$ /（N∙m²）	1.52×10⁴
机翼垂向阻尼 $C q 1$ /（kg∙m²∙s^-1）	7.733
机翼弦向阻尼 $C q 2$ /（kg∙m²∙s^-1）	17.709
机翼扭转阻尼 $C p$ /（kg∙m²∙s^-1）	0.685

Table 1

Fig.4

Fig.5

Table 2

Variation range of input parameters for tiltrotor aircraft

参数	范围
桅杆高度h	$0.230 85,0.255 15$
机翼垂弯刚度 $K q 1$	$24 890,27 510$
机翼弦弯刚度 $K q 2$	$110 105,121 695$
机翼扭转刚度 $K ϕ$	$14 440,15 960$
挥舞变距调节系数 $K p$	$- 0.281 4, - 0.254 6$

Table 2

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Fig.13

Table 3

Fig.14

Fig.15

Table 4

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状态	临界点速度
状态	理论值/kn	预测值/kn	误差/%
训练范围内	362.466 95	362.462 71	-0.001 17
训练范围外	349.730 83	349.725 43	-0.001 54

峰值次数	加密前最大误差/%	加密后最大误差/%	降幅/%
1	0.069 52	0.034 44	50.46
2	0.313 40	0.234 46	25.19
3	0.578 93	0.461 82	20.23
4	3.241 71	0.222 79	93.13

Prediction of whirl flutter boundary for tiltrotor aircraft based on BPNN with adaptive data

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