Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow
Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow
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DOI：
10.1063/5.0039845
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发表时间：
2020-10
影响因子：
4.6
通讯作者：
Taichi Nakamura;Kai Fukami;K. Hasegawa;Yusuke Nabae;K. Fukagata
中科院分区：
文献类型：
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作者：
Taichi Nakamura;Kai Fukami;K. Hasegawa;Yusuke Nabae;K. Fukagata
研究了基于机器学习的降阶模型(ML-ROM)对三维复杂流动的适用性。作为一个例子，我们考虑了摩擦雷诺数为$Re_\tau=110$的最小区域内的湍流槽道流动，它可以保持湍流的相干结构。训练数据集是通过直接数值模拟(DNS)来准备的。本发明的ML-ROM是由三维卷积神经网络自动编码器(CNN-AE)和长短期记忆(LSTM)相结合构成的。CNN-AE致力于将高维的流场映射到低维的潜在空间。然后利用LSTM来预测由CNN-AE获得的潜在向量的时间演变。CNN-AE和LSTM的结合只需整合低维隐含动力学的时间演化，就可以表示流场的时空高维动力学。ML-ROM机模拟的湍流流场在时间系综意义上与参考的DNS资料在统计上是一致的，这也可以通过基于轨道的分析来发现。文中还研究了区域中包含的涡旋结构群和用于时间预测的时间间隔对ML-ROM性能的影响。最后，我们讨论了目前的ML-ROM在湍流分析中的潜力和局限性。
We investigate the applicability of machine learning based reduced order model (ML-ROM) to three-dimensional complex flows. As an example, we consider a turbulent channel flow at the friction Reynolds number of $Re_\tau=110$ in a minimum domain which can maintain coherent structures of turbulence. Training data set are prepared by direct numerical simulation (DNS). The present ML-ROM is constructed by combining a three-dimensional convolutional neural network autoencoder (CNN-AE) and a long short-term memory (LSTM). The CNN-AE works to map high-dimensional flow fields into a low-dimensional latent space. The LSTM is then utilized to predict a temporal evolution of the latent vectors obtained by the CNN-AE. The combination of CNN-AE and LSTM can represent the spatio-temporal high-dimensional dynamics of flow fields by only integrating the temporal evolution of the low-dimensional latent dynamics. The turbulent flow fields reproduced by the present ML-ROM show statistical agreement with the reference DNS data in time-ensemble sense, which can also be found through an orbit-based analysis. Influences of the population of vortical structures contained in the domain and the time interval used for temporal prediction on the ML- ROM performance are also investigated. The potential and limitation of the present ML-ROM for turbulence analysis are discussed at the end of our presentation.