Synthetic turbulent inflow generator using machine learning
Synthetic turbulent inflow generator using machine learning
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DOI：
10.1103/physrevfluids.4.064603
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发表时间：
2018-06
影响因子：
2.7
通讯作者：
Kai Fukami;Yusuke Nabae;K. Kawai;K. Fukagata
中科院分区：
文献类型：
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作者：
Kai Fukami;Yusuke Nabae;K. Kawai;K. Fukagata
我们提出了一种方法，用于生成与时间相关的湍流流入数据的帮助下，机器学习（ML），它有可能取代传统的驱动程序模拟或合成的湍流流入发生器。至于ML模型，我们使用自动编码器类型的卷积神经网络（CNN）和多层感知器（MLP）。对于测试情况下，我们研究了一个充分发展的湍流槽道流动的摩擦雷诺数为${\rmRe}_{\tau} = 180$，以便于评估。使用通过直接数值模拟（DNS）获得的单个横截面中的瞬时速度场的时间序列来训练ML模型，以便输出在指定的未来时刻的横截面速度场。从先验测试中，从训练的ML模型的输出被回收到输入，横截面结构的时空演变被发现是合理地再现所提出的方法。在先验测试中获得的湍流统计数据也是，在一般情况下，在合理的协议与DNS数据，虽然在流率的一些偏差被发现。它还发现，目前的机器学习流入发生器是免费的虚假周期性，不同于传统的驱动器DNS在一个周期性的域。作为一个后验测试，我们执行DNS的流入-流出湍流通道流与训练ML模型用作机器学习的湍流流入发生器（MLTG）在入口处。结果表明，本MLTG可以保持足够长的时间来积累湍流统计的湍流通道流，与相应的驱动程序模拟的计算成本低得多。研究还表明，通过适当地校正流量偏差，可以获得准确的湍流统计数据。
We propose a methodology for generating time-dependent turbulent inflow data with the aid of machine learning (ML), which has a possibility to replace conventional driver simulations or synthetic turbulent inflow generators. As for the ML model, we use an auto-encoder type convolutional neural network (CNN) with a multi-layer perceptron (MLP). For the test case, we study a fully-developed turbulent channel flow at the friction Reynolds number of ${\rm Re}_{\tau} = 180$ for easiness of assessment. The ML models are trained using a time series of instantaneous velocity fields in a single cross-section obtained by direct numerical simulation (DNS) so as to output the cross-sectional velocity field at a specified future time instant. From the a priori test in which the output from the trained ML model are recycled to the input, the spatio-temporal evolution of cross-sectional structure is found to be reasonably well reproduced by the proposed method. The turbulence statistics obtained in the a priori test are also, in general, in reasonable agreement with the DNS data, although some deviation in the flow rate was found. It is also found that the present machine-learned inflow generator is free from the spurious periodicity, unlike the conventional driver DNS in a periodic domain. As an a posteriori test, we perform DNS of inflow-outflow turbulent channel flow with the trained ML model used as a machine-learned turbulent inflow generator (MLTG) at the inlet. It is shown that the present MLTG can maintain the turbulent channel flow for a long time period sufficient to accumulate turbulent statistics, with much lower computational cost than the corresponding driver simulation. It is also demonstrated that we can obtain accurate turbulent statistics by properly correcting the deviation in the flow rate.