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arXiv 2006.10282physics.flu-dyn
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用已知的稳态雷诺应力求平均场

Obtaining the mean fields with known Reynolds stresses at steady state

Xianwen Guo, Zhenhua Xia, Heng Xiao, Jinlong Wu, Shiyi Chen

摘要:

随着现代数据科学的兴起,利用机器学习算法进行数据驱动的湍流建模成为一个新的研究领域。许多方法能够实现更好的雷诺应力预测,具有更低的建模误差($ε_M$),比传统的RANS模型,但他们仍然遭受的数值误差和稳定性问题时,估计平均速度场使用RANS方程与预测的雷诺应力,说明求解RANS方程($ε_P$)的误差也是非常重要的。在本工作中,误差$ε_P$分别通过使用直接数值模拟得到的雷诺应力进行了研究,我们推导出了$ε_P$的来源。对于已知雷诺应力的情况,我们建议进行伴随RANS模拟,对$ν_t^*$和$S_{ij}^0$进行初步猜测。大约10次迭代,误差可以减少约一个数量级的流量周期性的山丘。本文的工作不仅提供了一个鲁棒的方法来最小化$ε_P$,这可能是非常有用的数据驱动的湍流模型,但也显示了雷诺应力的非线性部分的重要性,流动分离的流动问题。

英文摘要:

With the rising of modern data science, data--driven turbulence modeling with the aid of machine learning algorithms is becoming a new promising field. Many approaches are able to achieve better Reynolds stress prediction, with much lower modeling error ($ε_M$), than traditional RANS models but they still suffer from numerical error and stability issues when the mean velocity fields are estimated using RANS equations with the predicted Reynolds stresses, illustrating that the error of solving the RANS equations ($ε_P$) is also very important. In the present work, the error $ε_P$ is studied separately by using the Reynolds stresses obtained from direct numerical simulation and we derive the sources of $ε_P$. For the implementations with known Reynolds stresses solely, we suggest to run an adjoint RANS simulation to make first guess on $ν_t^*$ and $S_{ij}^0$. With around 10 iterations, the error could be reduced by about one-order of magnitude in flow over periodic hills. The present work not only provides one robust approach to minimize $ε_P$, which may be very useful for the data-driven turbulence models, but also shows the importance of the nonlinear part of the Reynolds stresses in flow problems with flow separations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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