提出了基于遗传算法加速策略的高效燃气涡轮叶片气热性能不确定性分析系统，此外还发展了一种叶顶气热特性不确定性量数据分析算法。所提出的系统和算法被用于考虑工况波动和几何参数偏差的含气膜冷却结构凹槽状叶顶的气热性能不确定性量化研究。结果表明冷气的注入将显著增强第一级动叶的下游流场的非稳定倾向。在燃气轮机的实际运行中，设计阶段预测能够被冷气充分覆盖的区域的气膜冷却有效度将大大低于确定性计算所得到的数值。在不确定性输入的影响下，凹槽状叶顶的气膜冷却有效度满足高斯分布，其统计平均值相比设计值减少了20.06%，偏离设计值10%的概率高达81.84%。敏感分析的结果表明影响下游总压损失系数波动的主导参数是叶顶间隙偏差，方差指数高达87.21%，而进口总温波动是影响气膜冷却有效度不确定性的核心参数，方差指数为84.01%。

This study proposes an efficient gas turbine blade aerothermal performance uncertainty system based on the genetic algorithm acceleration strategy， and develops a method for uncertainty data analysis of the blade tip aerothermal properties. The proposed system and method are utilized for the quantitative study of the uncertainty in the aerothermal performance of a squealer tip with film cooling structure， taking into account the operating condition fluctuation and geometrical parameter deviation. The results show that coolant jet injection will enhance the unsteady tendency of the downstream flow field of the first-stage rotor blades. In the actual operation of the gas turbine， the film cooling effectiveness in the region predicted at the design stage to be adequately covered by the coolant jet will be much lower than the value obtained from the deterministic calculations. Under the impact of the uncertainty inputs， the film cooling effectiveness of the squealer tip meets the Normal distribution. The mean value is reduced by 20.06% compared with the design value， and the probability of 10% deviation from the design value is as high as 81.84%. The result of sensitivity analysis shows that the main parameter affecting the fluctuation of the total downstream pressure loss coefficient is the tip clearance deviation， with the variance index as high as 87.21%. The total inlet temperature fluctuation is the key parameter affecting the uncertainty of the film cooling effectiveness， with the variance index of 84.01%.