Uncertainty due to operating conditions in gas turbines can have a significant impact on film cooling performance, or even the life of hot-section components. In this study, uncertainty quantification technique is applied to investigate the influences of inlet flow parameters on film cooling of fan-shaped holes on a stator vane under realistic engine conditions. The input parameters of uncertainty models include mainstream pressure, mainstream temperature, coolant pressure and coolant temperature, and it is assumed that these parameters conform to normal distributions. Surrogate model for film cooling is established by radial basis function neural network, and the statistical characteristics of outputs are determined by Monte Carlo simulation. The quantitative analysis results show that, on pressure surface, a maximum value of 61.6% uncertainty degree of laterally averaged adiabatic cooling effectiveness (ηad,lat) locates at about 4.0 diameters of hole downstream of the coolant exit; however, the maximum uncertainty degree of ηad,lat is only 4.5% on suction surface. Furthermore, the probability density function of area-averaged cooling effectiveness is of highly left-skewed distribution on pressure surface. By sensitivity analysis, the variation of mainstream pressure has the most pronounced effect on film cooling, while the effect of mainstream temperature is unobvious.

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