Flow prediction using dynamic mode decomposition with time-delay embedding based on local measurement-Reference-Cited by-同舟云学术

Flow prediction using dynamic mode decomposition with time-delay embedding based on local measurement

Published:2021-09 Issue:9 Volume:33 Page:095109
ISSN:1070-6631
Container-title:Physics of Fluids
language:en
Short-container-title:Physics of Fluids

Author:

Yuan Yuan¹²^ORCID,Zhou Kaiwen²³^ORCID,Zhou Wenwu²³^ORCID,Wen Xin²³^ORCID,Liu Yingzheng²³

Affiliation:

1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China

2. Key Lab of Education Ministry for Power Machinery and Engineering School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China

3. Gas Turbine Research Institute, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China

Funder

National Natural Science Foundation of China

Science and Technology Commission of Shanghai Municipality

Oceanic Interdisciplinary Program of Shanghai Jiao Tong University

Publisher

AIP Publishing

Subject

Condensed Matter Physics,Fluid Flow and Transfer Processes,Mechanics of Materials,Computational Mechanics,Mechanical Engineering

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0064867

Reference40 articles.

1. Machine learning approach to construct global phase-averaged flow field based on local flow features

2. Phase-consistent dynamic mode decomposition from multiple overlapping spatial domains

3. Use of proper orthogonal decomposition for time interpolation from PIV data

4. Spatial resolution enhancement/smoothing of stereo–particle-image-velocimetry data using proper-orthogonal-decomposition–based and Kriging interpolation methods

Cited by 25 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Hybrid Approach Combining Phase Space Reconstruction with Random Forest, KNN, and LSTM for Reducing Prediction Lag in Lake Water Level Forecasting;2024-08-22

2. Dynamic mode decomposition with optimal amplitude and time-delay embedding for reconstruction and prediction of local measurements;Nonlinear Dynamics;2024-05-23

3. Physics-informed dynamic mode decomposition for short-term and long-term prediction of gas-solid flows;Chemical Engineering Science;2024-05

4. Extracting vector magnitudes of dominant structures in a cyclic engine flow with dimensionality reduction;Physics of Fluids;2024-02-01

5. Fluid classification with dynamic graph convolution network by local linear embedding well logging data;Physics of Fluids;2024-02-01