由于气动环境的复杂性，旋翼在气动评估中存在较多不确定性且对旋翼性能影响较大。当前旋翼气动评估方法不能考虑气动数据不确定性，本文将数据融合技术应用于旋翼气动预测以在记及某些不确定因素影响的条件下，获得具有更高可信度的气动力分布及置信区间，为进一步开展旋翼气动数据的不确定度分析及工程应用奠定基础。基于不同来源的气动力数据均为正态分布的独立随机变量这一假设，将分布气动数据和测量值之间的关系作为融合准则，以实现最佳匹配测量值为目标，采用贝叶斯估计求解融合数据的最大后验概率分布，从而构建一种基于贝叶斯框架的气动数据融合方法。以UH-60A旋翼及Caradonna-Tung旋翼为例，采用所提方法进行不同来源气动数据的融合，对融合结果的估计方差与预测误差进行对比分析。结果表明，首先，所提方法对于输入数据的来源无特殊要求，能够给出融合结果的置信区间并降低不同来源数据的估计方差，基于此结果后续可进行不确定度分析与研究；其次，融合所得结果并不局限于不同来源数据之间，扩大了数据覆盖范围；最后，融合所得结果相比于单一数据来源更符合物理规律。

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.