Experimental velocity data estimation for imperfect particle images using machine learning

Morimoto, Masaki; Fukami, Kai; Fukagata, Koji

doi:10.48550/arxiv.2005.00756

Cited by 9 publications

(17 citation statements)

References 49 publications

(51 reference statements)

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“…The CNN excels at extracting features from a large amount of data thanks to its filter operator, and it has been utilized in image processing and classification tasks [31]. In the field of fluid dynamics, the use of CNN is also spreading because the filter shearing allows us to handle the high-dimensional fluid data efficiently [32,33,34,35,36].…”

Section: D-3d Convolutional Neural Networkmentioning

confidence: 99%

Supervised convolutional network for three-dimensional fluid data reconstruction from sectional flow fields with adaptive super-resolution assistance

Matsuo¹,

Nakamura²,

Morimoto³

et al. 2021

Preprint

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The recent development of high-performance computing enables us to generate spatiotemporal high-resolution data of nonlinear dynamical systems and to analyze them for deeper understanding of their complex nature. This trend can be found in a wide range of science and engineering communities, which suggests that detailed investigations on efficient data handling in physical science must be required in future. To this end, we introduce the use of convolutional neural networks (CNNs) to achieve an efficient data storage and estimation of scientific big data derived from nonlinear dynamical systems.The CNN is utilized to reconstruct three-dimensional data from a few numbers of two-dimensional sections in a computationally friendly manner. The present model is a combination of two-and three-dimensional CNNs, which allows users to save only some of the two-dimensional sections to reconstruct the volumetric data. As an example of threedimensional data, we consider a fluid flow around a square cylinder at the diameter-based Reynolds number Re D of 300, and show that volumetric fluid flow data can successfully be reconstructed with the present method from as few as five sections. Furthermore, we also propose a combination of the present CNN-based reconstruction with an adaptive sampling-based super-resolution analysis to augment the data compression capability of the present methods. Our report can be a significant bridge toward practical data handling for not only the fluid mechanics field but also a vast range of physical sciences.

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Section: D-3d Convolutional Neural Networkmentioning

confidence: 99%

Supervised convolutional network for three-dimensional fluid data reconstruction from sectional flow fields with adaptive super-resolution assistance

Matsuo¹,

Nakamura²,

Morimoto³

et al. 2021

Preprint

Self Cite

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show abstract

“…The CNN has mainly utilized in image processing and classification tasks. Moreover, the use of CNN has also emerged in fluid dynamics field because of the compatibility of filter sharing idea to high-dimensional fluid data [36][37][38][39][40][41][42] .…”

Section: B Convolutional Neural Network Based Autoencodermentioning

confidence: 99%

Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow

Nakamura,

Fukami,

Hasegawa

et al. 2020

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We investigate the applicability of machine learning based reduced order model (ML-ROM) to threedimensional complex flows. As an example, we consider a turbulent channel flow at the friction Reynolds number of Re τ = 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 reproduced turbulent flow fields 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. The influences on vortical structures over the domain and a time interval for temporal prediction for 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.

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“…Note that the assumption on CNN is that the pixels of image far apart have no strong correlation. Although this concept was started in computer science, this has also been adopted for dealing with various high dimensional problems including fluid dynamics [33][34][35][36][37][38][39][40][41].…”

Section: Convolutional Neural Network Based Hierarchical Autoencodermentioning

confidence: 99%

Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data

Fukami,

Nakamura,

Fukagata

2020

Preprint

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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 ReD = 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 twodimensional y − z cross-sectional velocity field of turbulent channel flow at Reτ = 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.

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Experimental velocity data estimation for imperfect particle images using machine learning

Cited by 9 publications

References 49 publications

Supervised convolutional network for three-dimensional fluid data reconstruction from sectional flow fields with adaptive super-resolution assistance

Supervised convolutional network for three-dimensional fluid data reconstruction from sectional flow fields with adaptive super-resolution assistance

Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow

Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data

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