论文标题
使用机器学习扩展湍流模型不确定性定量
Extending turbulence model uncertainty quantification using machine learning
论文作者
论文摘要
为了实现航空业喷气发动机的更虚拟设计和认证过程，必须知道计算流体动力学的不确定性范围。这项工作显示了机器学习方法的应用来量化湍流模型的认知不确定性。为了估计不确定性界限的基础方法是基于雷诺应激张量与随机森林的特征空间扰动。
In order to achieve a more virtual design and certification process of jet engines in aviation industry, the uncertainty bounds for computational fluid dynamics have to be known. This work shows the application of a machine learning methodology to quantify the epistemic uncertainties of turbulence models. The underlying method in order to estimate the uncertainty bounds is based on an eigenspace perturbation of the Reynolds stress tensor in combination with random forests.
