Research on the influence of different data distributions on the fitting results of subsonic compressible flow turbulence level and its uncertainty

Abstract

Abstract On the basis of results of subsonic compressible flow turbulence level in high speed wind tunnels by hyperbolic fitting, the uncertainty of turbulence level is quantitatively studied by using Monte Carlo simulation method. In order to generate stochastic sample data for Monte Carlo simulation, two distribution types, uniform distribution and normal distribution, are used to generate the data points to be fitted. The effect of changing characteristic parameters of two distribution types on the turbulence fitting results and its uncertainty is studied. The results show that: (1) for uniform distribution, the larger the data distribution bandwidth is, the larger turbulence level and its uncertainty is; (2) for normal distribution, when the mean of the distribution is fixed and the standard deviation is changed, turbulence level results are basically the same, and the uncertainty and the standard deviation have the same trend; (3) comparing the two distribution types, turbulence level and its uncertainty can both be accurately solved, as long as the characteristic parameters are appropriate.