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4 $M_{t} =0.4$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, 同时LES网格为128$^{3}(h_{LES} =\varDelta /2)$, 滤波宽度为$\varDelta=16\delta x$ |
| Fig.4 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 128$^{3}(h_{LES} =\varDelta /2)$ with the filter width $\varDelta =16\delta x$} |
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