A divide-and-conquer machine learning approach for modeling turbulent flows
A divide-and-conquer machine learning approach for modeling turbulent flows
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DOI：
10.1063/5.0149750
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发表时间：
2023-05
影响因子：
4.6
通讯作者：
Anthony Man;M. Jadidi;A. Keshmiri;Hujun Yin;Y. Mahmoudi
中科院分区：
文献类型：
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作者：
Anthony Man;M. Jadidi;A. Keshmiri;Hujun Yin;Y. Mahmoudi
提出了一种基于分治技术的区域机器学习方法，用于雷诺平均Navier-Stokes（RANS）湍流模拟。这种方法涉及将流域划分为称为区域的流物理区域，在每个区域中训练一个ML模型，然后在各自的区域上验证和测试它们。该方法被证明与张量基神经网络（TBNN）和另一种神经网络称为湍流动能神经网络（TKENN）。这些被用来预测雷诺应力各向异性和湍流动能，分别在测试情况下的流动在一个固体块，其中包含不同的流动物理，包括分离流的区域。结果表明，纬向TBNN和TKENN的组合预测结果比相应的标准非纬向模式的预测结果要准确得多。最值得注意的是，剪切各向异性分量在测试用例中预测至少20%和55%以上的平均准确度相比，非带状TBNN和RANS，分别由带状TBNN。与分区预测构造的雷诺应力也被发现是至少23%以上的准确度比那些获得的非分区的方法和30%以上的准确度比雷诺应力预测RANS平均。这些改进归因于区域的形状，使得分区模型在预测输出时变得高度局部优化。
In this paper, a novel zonal machine learning (ML) approach for Reynolds-averaged Navier–Stokes (RANS) turbulence modeling based on the divide-and-conquer technique is introduced. This approach involves partitioning the flow domain into regions of flow physics called zones, training one ML model in each zone, then validating and testing them on their respective zones. The approach was demonstrated with the tensor basis neural network (TBNN) and another neural net called the turbulent kinetic energy neural network (TKENN). These were used to predict Reynolds stress anisotropy and turbulent kinetic energy, respectively, in test cases of flow over a solid block, which contain regions of different flow physics including separated flows. The results show that the combined predictions given by the zonal TBNNs and TKENNs were significantly more accurate than their corresponding standard non-zonal models. Most notably, shear anisotropy component in the test cases was predicted at least 20% and 55% more accurately on average by the zonal TBNNs compared to the non-zonal TBNN and RANS, respectively. The Reynolds stress constructed with the zonal predictions was also found to be at least 23% more accurate than those obtained with the non-zonal approach and 30% more accurate than the Reynolds stress predicted by RANS on average. These improvements were attributed to the shape of the zones enabling the zonal models to become highly locally optimized at predicting the output.