涡轮叶片来流角扰动不确定性量化分析

为了高效精确量化来流条件变化对涡轮叶片性能的影响,发展了一种基于自适应抽样技术的非嵌入式多项式混沌(NIPC)模型,并通过函数试验对自适应NIPC模型进行了验证。在不同尺度来流角变化下,用该模型量化涡轮叶栅能量损失系数的不确定性变化,同时与直接蒙特卡洛模拟和灵敏度分析进行对比。通过流场统计分析揭示来流角变化对气动不确定性的影响机理。结果表明:自适应NIPC在不同尺度来流角扰动下均能快速准确量化能量损失系数变化;能量损失系数不确定性变化对来流角扰动是非线性依赖关系;叶片通道内的激波对来流角变化最为敏感,是引起能量损失系数不确定性变化的主要因素。

Uncertainty quantification of inlet incidence angle variation for turbine blade

A non-intrusive polynomial chaos (NIPC) model was proposed to efficiently and accurately quantify turbine blade performance change caused by flow variations, and verified by function model. It was used to quantify the change of energy loss coefficient for transonic turbine cascade when the inlet incidence angle varied at different scales. Meanwhile, it was compared with the direct Monte Carlo simulation (MCS) and sensitivity-based evaluation method. The flow solutions were statistically analyzed to reveal the impact mechanism of inlet incidence angle variation. The results showed that the adaptive NIPC quantified the uncertainty of energy loss coefficient with high performance at different scales perturbation of inlet incidence angle. Besides, the energy loss coefficient variation was nonlinearly dependent on the inlet incidence perturbation. Moreover, the shock wave in blade passage was most sensitive to the inlet incidence angle variation, becoming the most contribution to energy loss coefficient uncertainty for the transonic turbine cascade.