On the improvement of the extrapolation capability of an iterative machine-learning based RANS Framework
On the improvement of the extrapolation capability of an iterative machine-learning based RANS Framework
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DOI：
10.1016/j.compfluid.2023.105864
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Weishuo Liu;Jian Fang;S. Rolfo;C. Moulinec;David R. Emerson
中科院分区：
文献类型：
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作者：
Weishuo Liu;Jian Fang;S. Rolfo;C. Moulinec;David R. Emerson
提出了一种有界归一化方法来提高最近开发的基于迭代ML的湍流建模框架的外推能力（Liu等人，2021），当雷诺数超出训练范围时，其性能下降。有界归一化方法将输入特征约束在有限的范围内，同时利用传统湍流模型的涡粘性对学习目标进行重新归一化。因此，当雷诺数超出训练范围时，可以保持一定程度的物理相似性。在通道流中的测试表明，相对于传统的归一化方法的性能有所改善。改进后的模型在不同雷诺数的空间发展边界层中进行了进一步的测试，结果表明，即使在不同的流态下训练，该模型也能以良好的精度预测表面摩擦和速度分布。
A bounded normalization method is proposed to improve the extrapolation capability of the recently developed iterative ML based turbulence modeling framework (Liu et al., 2021), whose performance degrades when the Reynolds number is beyond the training range. The bounded normalization method constrains the input feature into a finite range, and the learning target is also re-normalized with the eddy viscosity from a traditional turbulence model. Consequently, a certain level of physical similarity can be maintained when the Reynolds number goes beyond the training range. Tests in channel flows demonstrates an improved performance against the conventional normalization method. The improved model is further tested in a spatially developing boundary layer with a varying local Reynolds number, and the result shows that the model can also predict the skin-friction and velocity profiles with a favorable accuracy, even if the model is trained under a different flow configuration.