论文标题
关于机器学习辅助各向异性映射的普遍性用于预测性湍流建模
On the generalizability of machine-learning-assisted anisotropy mappings for predictive turbulence modelling
论文作者
论文摘要
最近已经提出了一些用于增强湍流封闭模型的机器学习框架。但是，增强湍流模型的普遍性仍然是一个悬而未决的问题。我们通过系统地改变几种模型的培训和测试集来调查这个问题。最佳的三项张量基膨胀用于开发模型的数据驱动的湍流闭合近似。然后，对随机森林，神经网络和极端梯度提升（XGBoost）模型进行了高参数优化。我们建议XGBOOST由于其低调成本和良好的性能，用于数据驱动的湍流关闭建模。我们还发现，机器学习模型可以很好地推广到训练数据集中看到的流量的新参数变化，但缺乏对新流量类型的普遍性。这种概括性差距表明机器学习方法最适合为给定流量类型开发专门模型，这是工业应用中经常遇到的问题。
Several machine learning frameworks for augmenting turbulence closure models have been recently proposed. However, the generalizability of an augmented turbulence model remains an open question. We investigate this question by systematically varying the training and test sets of several models. An optimal three-term tensor basis expansion is used to develop a model-agnostic data-driven turbulence closure approximation. Then, hyperparameter optimization was performed for a random forest, a neural network, and an eXtreme Gradient Boosting (XGBoost) model. We recommend XGBoost for data-driven turbulence closure modelling owing to its low-tuning cost and good performance. We also find that machine learning models generalize well to new parametric variations of flows seen in the training dataset, but lack generalizability to new flow types. This generalizability gap suggests that machine learning methods are most suited for developing specialized models for a given flow type, a problem often encountered in industrial applications.
