论文标题
基于卷积神经网络的层次层次自动编码器，用于流体场数据的非线性模式分解
Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data
论文作者
论文摘要
我们提出了一种定制的基于卷积神经网络的自动编码器，称为层次自动编码器，该编码器使我们能够提取流场的非线性自动编码器模式，同时保留潜在向量的贡献顺序。作为初步测试，提出的方法首先应用于$ re_d $ = 100及其瞬态过程的气缸唤醒。发现所提出的方法可以将这些层流流场的特征提取为潜在向量，同时保持其能量含量的顺序。目前的层次自动编码器将通过二维$ Y-Z $ Y-Z $ y-Z $ y-Z $re_τ$ = 180的横截面速度场进行评估，以检查其适用于湍流。已经证明，可以通过使用有序自动编码器模式系列的概念利用分层模型来有效地映射湍流场。目前的结果表明，可以扩展提出的概念，以满足流体动力学的各种需求，包括减少顺序建模及其与基于线性理论的方法的结合，通过安排提取的非线性模式的顺序。
We propose a customized convolutional neural network based autoencoder called a hierarchical autoencoder, which allows us to extract nonlinear autoencoder modes of flow fields while preserving the contribution order of the latent vectors. As preliminary tests, the proposed method is first applied to a cylinder wake at $Re_D$ = 100 and its transient process. It is found that the proposed method can extract the features of these laminar flow fields as the latent vectors while keeping the order of their energy content. The present hierarchical autoencoder is further assessed with a two-dimensional $y-z$ cross-sectional velocity field of turbulent channel flow at $Re_τ$ = 180 in order to examine its applicability to turbulent flows. It is demonstrated that the turbulent flow field can be efficiently mapped into the latent space by utilizing the hierarchical model with a concept of ordered autoencoder mode family. The present results suggest that the proposed concept can be extended to meet various demands in fluid dynamics including reduced order modeling and its combination with linear theory-based methods by using its ability to arrange the order of the extracted nonlinear modes.
