基于人工神经网络的湍流大涡模拟方法 1)
ARTIFICIAL NEURAL NETWORK-BASED SUBGRID-SCALE MODELS FOR LARGE-EDDY SIMULATION OF TURBULENCE 1)
基于人工神经网络的湍流大涡模拟方法 1) |
| 谢晨月, 袁泽龙, 王建春, 万敏平, 陈十一 |
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ARTIFICIAL NEURAL NETWORK-BASED SUBGRID-SCALE MODELS FOR LARGE-EDDY SIMULATION OF TURBULENCE 1) |
| Xie Chenyu, Yuan Zelong, Wang Jianchun, Wan Minping, Chen Shiyi |
| 图9 $M_{t} =0.4$和$t/\tau =3.37$ ($\tau =L_{I} /u^{rms}$是大涡翻转时间) 情况下的归一化速度散度$\tilde{{\theta }}/\tilde{{\theta }}_{fDNS}^{rms}$云图, 其中LES网格为64$^{3}(h_{LES} =\varDelta /2)$,滤波宽度为$\varDelta =32\delta x$类似于SANN模型, DANN模型采用两个控制参数主导神经网络输入层的结构: 输入量的空间模板在每个方向上的点数$D(D=2R_{s} +1)$; 滤波宽度$\varDelta $和空间模板的网格尺度$\varDelta_{g} $之比: $R_{g} =\varDelta /\varDelta_{g} $, 则输入层的神经元个数为$M=3\times D^{3}$. DANN($D,R_{g} )$模型共包含输入层${X}_{I} $、4个隐藏层${X}_{h} $和输出层${X}_{O} $, 其中输入和输出参数空间分别为 |
| Fig.9 Contours of the normalized velocity divergence $\tilde{{\theta }}/\tilde{{\theta }}_{fDNS}^{rms} $ on an arbitrarily selected x-y slice, at $M_{t} =0.4$, and $t/\tau =3.37$ (here $\tau =L_{I} /u^{rms}$ is the large-eddy turnover time) for LES at grid resolution of 64$^{3}(h_{LES} =\varDelta /2)$ with the filter width $\varDelta =32\delta x$} |
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