论文标题
基于机器学习的不确定性量化机翼的湍流模型
Machine learning based uncertainty quantification of turbulence model for airfoils
论文作者
论文摘要
基于雷诺的Navier-Stokes（RANS）的过渡模型广泛用于航空航天应用中，但由于Boussinesq湍流粘度假设而遭受了不准确的损失。特征空间扰动方法可以通过向其预测的雷诺应力来估计rans模型的准确性。但是，缺少可靠的方法来选择注射扰动的强度，而现有的机器学习模型通常很复杂且渴望数据。我们检查了两个轻加权的机器学习模型，以帮助选择注射的扰动的强度，以估算在Selig-Donovan 7003机翼上过渡到湍流的流量的不确定性。一方面，我们检查了多项式回归，以构建具有特征值扰动增强的标记函数，以估计预测皮肤摩擦系数结合的不确定性。另一方面，我们训练了卷积神经网络（CNN），以预测高保真湍流动能。训练有素的CNN充当标记函数，可以集成到本征空间扰动方法中以量化不确定性。我们的发现表明，轻加权机器学习模型可以有效构建适当的标记功能，该功能有望丰富现有的特征空间扰动方法，以更量化不确定性。
Reynolds-averaged Navier-Stokes (RANS)-based transition modeling is widely used in aerospace applications but suffers inaccuracies due to the Boussinesq turbulent viscosity hypothesis. The eigenspace perturbation method can estimate the accuracy of a RANS model by injecting perturbations to its predicted Reynolds stresses. However, there lacks a reliable method for choosing the strength of the injected perturbation, while existing machine learning models are often complex and data craving. We examined two light-weighted machine learning models to help select the strength of the injected perturbation for estimating the RANS uncertainty of flows undergoing the transition to turbulence over a Selig-Donovan 7003 airfoil. On the one hand, we examined polynomial regression to construct a marker function augmented with eigenvalue perturbations to estimate the uncertainty bound for the predicted skin friction coefficient. On the other hand, we trained a convolutional neural network (CNN) to predict high-fidelity turbulence kinetic energy. The trained CNN acts as a marker function that can be integrated into the eigenspace perturbation method to quantify the RANS uncertainty. Our findings suggest that the light-weighted machine learning models are effective in constructing an appropriate marker function that is promising to enrich the existing eigenspace perturbation method to quantify the RANS uncertainty more precisely.
