基于贝叶斯框架的旋翼气动力数据融合

doi:10.7527/S1000-6893.2023.28960

Abstract

Abstract:

Due to the complexity of the aerodynamic environment， there are many uncertainties in the aerodynamic evaluation of rotor blades， which have a significant impact on their performance. The existing aerodynamic evaluation methods cannot take into account the uncertainty of aerodynamic data. This study applies the data fusion technology to the prediction of rotor blade aerodynamics. The aerodynamic force distribution and confidence intervals with higher credibility under the conditions of remembering certain uncertain factors are obtained， laying a foundation for further uncertainty analysis and engineering applications of rotor blade aerodynamic data. Based on the hypothesis that the aerodynamic force data from different sources are all normally distributed independent random variables， the relationship between the distributed aerodynamic data and measured values is used as the fusion criterion to achieve the best matching of the measured values. The Bayesian estimation is used to solve the maximum posterior probability distribution of the fused data， thus constructing an aerodynamic data fusion method based on the Bayesian framework. Using the UH-60A and Caradonna-Tung rotor blades as examples， the proposed method is used to fuse the aerodynamic data from different sources， and the estimation variance and prediction error of the fused results are analyzed and compared. The results show that firstly， the proposed method， without special requirements for the source of input data， can provide confidence intervals for fused results， and reduce the estimation variance of data from different sources. On this basis， uncertainty analysis can be conducted. Secondly， the fused results are not limited to data from different sources， and thus broaden the coverage of data. Finally， the fused results are more in line with physical laws than the data from a single source.

Key words: rotor aerodynamic evaluation, data fusion, Bayesian estimation, uncertainty analysis, CFD

CLC Number:

V211.3

Hua YANG, Shusheng CHEN, Zhenghong GAO, Quanfeng JIANG, Wei ZHANG. Rotor aerodynamic data fusion based on Bayesian framework[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(8): 128960.

Figures/Tables 19

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Table 1

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Table 2

Comparison of prediction errors for blade thrust coefficients （UH-60A）

参数	拉力系数 $C T / σ$	相对误差/%
$z C F D$	0.081 46	4.16
$z E X P$	0.083 14	2.19
$z M A P$	0.084 96	0.05
$z r e a l$	0.085

Table 2

Fig.12

Fig.13

Table 3

Comparison of prediction errors for each section airfoil lift coefficient

剖面升力系数 $z = C L$

（相对误差）

r/R=0.400

r/R=0.675

r/R=0.775

r/R=0.865

r/R=0.945

r/R=0.900

z C F D

0.710 86

（8.92%）

0.570 37

（0.90%）

0.463 17

（2.73%）

0.385 79

（10.96%）

0.399 84

（13.36%）

0.328 06

（19.79%）

z E X P

0.759 21

（2.73%）

0.592 35

（4.79%）

0.517 09

（8.59%）

0.437 68

（1.02%）

0.446 09

（3.34%）

0.327 32

（19.52%）

z M A P

0.780 38

（0.01%）

0.565 31

（0.005%）

0.476 15

（0.002%）

0.433 25

（0.002%）

0.461 44

（0.017%）

0.274 12

（0.095%）

z r e a l

0.780 48

0.565 28

0.476 16

0.433 26

0.461 52

0.273 86

Table 3

Fig.14

Fig.15

Table 4

Comparison of prediction errors for blade thrust coefficients （CT rotor）

参数	拉力系数 $C T / σ$	相对误差/%
$z B E M$	0.053 72	24.18
$z P I P C$	0.045 99	6.31
$z M A P$	0.043 31	0.12
$z r e a l$	0.043 26

Table 4

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Rotor aerodynamic data fusion based on Bayesian framework

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