Abstract: The geometric uncertainty issue of compressor blade machining is very prominent. As the input of the uncertainty quantification system, the accurate expression of its statistical distribution is particularly important for the system output. According to the statistical analysis of leading-edge radius errors at the same blade height section of 100 compressor rotor blades, it is found that the distribution is defective to left and in a peak condition, and the non-normality is significant. Then, to supplement the limitations of the normal distribution, the expectation conditional maximization either method is used to obtain the error distribution with skewness and kurtosis characteristics, which fits the error data better than the normal distribution. Finally, the leading-edge radius error fitting the two distributions are used as the uncertainty quantification input respectively, and the total pressure loss coefficient and static pressure ratio of the cascade are used as the response. The comparison of the quantification results shows that the total pressure loss coefficient and static pressure ratio mean variable values corresponding to different statistical distribution are less than 1%, which can be considered to be negligible, while the scatter difference is significant. Besides, the variation of the total pressure loss coefficient is larger than that of the static pressure ratio, about 20% at most. In addition, if the normal distribution is input, the influence of the leading-edge radius error’s uncertainty on the scatter of the cascade aerodynamic performance and the performance variation range are significantly overestimated, while the possibility of performance deterioration is underestimated. The research illustrates the necessity of machining error considering skewness and kurtosis characteristics, and it is expected to provide more accurate reference for the blade fine machining.

Key words: machining error, skewness, kurtosis, statistical distribution, uncertainty analysis