arXiv 2301.090132023-01-21physics.flu-dynphysics.comp-ph

使用机器学习的空间相关turbulence模型聚合

Space-dependent turbulence model aggregation using machine learning

Maximilien de Zordo-Banliat, Grégory Dergham, Xavier Merle, Paola Cinnella

摘要：

在这篇文章中，我们提出了一个数据驱动的方法，结合一组竞争的湍流模型的解决方案。各个模型的预测线性组合，以提供一个合奏的解决方案，伴随着由于湍流模型的选择预测的不确定性的估计。首先，对于一组训练流配置，我们通过引入高保真数据和单个模型预测之间的距离度量，在组件模型表现最好的区域为组件模型分配高权重，反之亦然。然后将模型权重映射到代表局部流物理的特征空间中，并通过随机森林（RF）算法进行回归。RF回归最后被用来推断空间分布的模型权重看不见的配置。新案例的预测被构造为基础模型解的凸线性组合，而模型间方差提供了关于高模型不确定性区域的信息。本文以NACA 65 V103压气机叶栅中Re~ 3e 5的一类流动为例，验证了本文方法的有效性。结果表明，聚合解决方案优于用于通知RF回归量的单个模型的准确性，并且对于与前一个相关性良好的其他量表现良好。估计的不确定性区间通常与目标高保真数据一致。本方法代表了一种可行的方法，用于更客观地选择和组合工程实践中感兴趣的配置中的备选湍流模型

英文摘要：

In this article, we propose a data-driven methodology for combining the solutions of a set of competing turbulence models. The individual model predictions are linearly combined for providing an ensemble solution accompanied by estimates of predictive uncertainty due to the turbulence model choice. First, for a set of training flow configurations we assign to component models high weights in the regions where they best perform, and vice versa, by introducing a measure of distance between high-fidelity data and individual model predictions. The model weights are then mapped into a space of features, representative of local flow physics, and regressed by a Random Forests (RF) algorithm. The RF regressor is finally employed to infer spatial distributions of the model weights for unseen configurations. Predictions of new cases are constructed as a convex linear combination of the underlying models solutions, while the between model variance provides information about regions of high model uncertainty. The method is demonstrated for a class of flows through the compressor cascade NACA65 V103 at Re~3e5. The results show that the aggregated solution outperforms the accuracy of individual models for the quantity used to inform the RF regressor, and performs well for other quantities well-correlated to the preceding one. The estimated uncertainty intervals are generally consistent with the target high-fidelity data. The present approach then represents a viable methodology for a more objective selection and combination of alternative turbulence models in configurations of interest for engineering practice

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摘要：

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