In Search of a Universal Rough Wall Model
In Search of a Universal Rough Wall Model
复制标题
寻找通用的粗糙墙模型
DOI：
10.1115/1.4062820
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Kunz, Robert F.
中科院分区：
文献类型：
--
作者：
Yang, Xiang I.;Zhang, Wen;Yuan, Junlin;Kunz, Robert F.
这项工作比较了各种现有的粗糙壁模型在一个大的粗糙表面收集不同的特点，并研究这些模型在容纳新的数据集的潜力。我们考虑了粗糙度数据库1内外68个粗糙表面的三种经验粗糙度相关性、两种基于物理的模型和一种数据驱动的机器学习模型。结果表明，相关型模型和机器学习模型不会外推到它们校准或训练的数据集之外。相比之下，基于物理的遮蔽模型在外推方面表现良好。针对大数据集重新校准粗糙度相关性证明是徒劳的。然而，重新训练机器学习模型会产生良好的结果。我们不追求进一步的重新训练和重新校准基于物理的模型，因为它需要新的物理见解。总的来说，我们的研究结果表明，一个普遍的粗糙壁模型还没有被发现。外推的能力可能来自于结合物理。另一方面，数据有利于机器学习模型。
This work compares various existing rough-wall models on a large collection of rough surfaces with different characteristics and studies the potential of these models in accommodating new datasets. We consider three empirical roughness correlations, two physics-based models, and one data-driven machine-learning model on 68 rough surfaces inside and outside the Roughness Database 1. Results show that correlation-type models and machine-learning models do not extrapolate outside the dataset against which they are calibrated or trained. In contrast, the physics-based sheltering model performs well in extrapolation. Recalibrating a roughness correlation against a large dataset proves unfruitful. However, retraining a machine learning model yields good results. We do not pursue further retraining and recalibrating of a physics-based model, as it requires new physical insights. Overall, our findings suggest that a universal rough-wall model is yet to be found. The capability of extrapolation will likely come from incorporating physics. Data, on the other hand, benefits machine learning models.