Citation: | HAN R K,LIU Z Y,QIAN W Q,et al. Spatio-temporal reconstruction method of flow field based on deep neural network[J]. Journal of Experiments in Fluid Mechanics, 2022,36(3):118-126. doi: 10.11729/syltlx20210124 |
[1] |
ADRIAN R J. Multi-point optical measurements of simultaneous vectors in unsteady flow—a review[J]. International Journal of Heat and Fluid Flow,1986,7(2):127-145. doi: 10.1016/0142-727X(86)90062-7
|
[2] |
王福君,王洪平,高琪,等. 鱼游动涡结构PIV实验研究[J]. 实验流体力学,2020,34(5):20-28. doi: 10.11729/syltlx20200039
WANG F J,WANG H P,GAO Q,et al. PIV experimental study on fish swimming vortex structure[J]. Journal of Experiments in Fluid Mechanics,2020,34(5):20-28. doi: 10.11729/syltlx20200039
|
[3] |
王昕. 面向非定常流场的实时自适应PIV测量技术研究[D]. 武汉: 华中科技大学, 2017.
WANG X. Research of real-time adaptive PIV measurement technique oriented to unsteady flow field[D]. Wuhan: Huazhong University of Science and Technology, 2017.
|
[4] |
LECUN Y,BENGIO Y,HINTON G. Deep learning[J]. Nature,2015,521(7553):436-444. doi: 10.1038/nature14539
|
[5] |
KRIZHEVSKY A,SUTSKEVER I,HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM,2017,60(6):84-90. doi: 10.1145/3065386
|
[6] |
SAINATH T N, MOHAMED A R, KINGSBURY B, et al. Deep convolutional neural networks for LVCSR[C]//Proc of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. 2013: 8614-8618. doi: 10.1109/ICASSP.2013.6639347
|
[7] |
XIONG H Y,ALIPANAHI B,LEE L J,et al. RNA splicing: The human splicing code reveals new insights into the genetic determinants of disease[J]. Science,2015,347(6218):1254806. doi: 10.1126/science.1254806
|
[8] |
LING J L,KURZAWSKI A,TEMPLETON J. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance[J]. Journal of Fluid Mechanics,2016,807:155-166. doi: 10.1017/jfm.2016.615
|
[9] |
RAISSI M,YAZDANI A,KARNIADAKIS G E. Hidden fluid mechanics: learning velocity and pressure fields from flow visualizations[J]. Science,2020,367(6481):1026-1030. doi: 10.1126/science.aaw4741
|
[10] |
叶舒然,张珍,王一伟,等. 基于卷积神经网络的深度学习流场特征识别及应用进展[J]. 航空学报,2021,42(4):524736.
YE S R,ZHANG Z,WANG Y W,et al. Progress in deep convolutional neural network based flow field recognition and its applications[J]. Acta Aeronautica et Astronautica Sinica,2021,42(4):524736.
|
[11] |
HUI X Y,BAI J Q,WANG H,et al. Fast pressure distribution prediction of airfoils using deep learning[J]. Aerospace Science and Technology,2020,105:105949. doi: 10.1016/j.ast.2020.105949
|
[12] |
王怡星,韩仁坤,刘子扬,等. 流体力学深度学习建模技术研究进展[J]. 航空学报,2021,42(4):225-244.
WANG Y X,HAN R K,LIU Z Y,et al. Progress of deep learning modeling technology for fluid mechanics[J]. Acta Aeronautica et Astronautica Sinica,2021,42(4):225-244.
|
[13] |
SEKAR V,JIANG Q H,SHU C,et al. Fast flow field prediction over airfoils using deep learning approach[J]. Physics of Fluids,2019,31(5):057103. doi: 10.1063/1.5094943
|
[14] |
JIN X W,CHENG P,CHEN W L,et al. Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder[J]. Physics of Fluids,2018,30(4):047105. doi: 10.1063/1.5024595
|
[15] |
HAN R K,WANG Y X,ZHANG Y,et al. A novel spatial-temporal prediction method for unsteady wake flows based on hybrid deep neural network[J]. Physics of Fluids,2019,31(12):127101. doi: 10.1063/1.5127247
|
[16] |
惠心雨,袁泽龙,白俊强,等. 基于深度学习的非定常周期性流动预测方法[J]. 空气动力学学报,2019,37(3):462-469. doi: 10.7638/kqdlxxb-2019.0003
HUI X Y,YUAN Z L,BAI J Q,et al. A method of unsteady periodic flow field prediction based on the deep learning[J]. Acta Aerodynamica Sinica,2019,37(3):462-469. doi: 10.7638/kqdlxxb-2019.0003
|
[17] |
FUKAMI K,FUKAGATA K,TAIRA K. Super-resolution reconstruction of turbulent flows with machine learning[J]. Journal of Fluid Mechanics,2019,870:106-120. doi: 10.1017/jfm.2019.238
|
[18] |
LAGEMANN C,LAGEMANN K,MUKHERJEE S,et al. Deep recurrent optical flow learning for particle image velocimetry data[J]. Nature Machine Intelligence,2021,3(7):641-651. doi: 10.1038/s42256-021-00369-0
|
[19] |
朱浩然,高琪,王洪平,等. 基于机器学习方法的三维粒子重构技术[J]. 实验流体力学,2021,35(3):88-93. doi: 10.11729/syltlx20200141
ZHU H R,GAO Q,WANG H P,et al. Particle reconstruction of volumetric particle image velocimetry with strategy of machine learning[J]. Journal of Experiments in Fluid Mechanics,2021,35(3):88-93. doi: 10.11729/syltlx20200141
|
[20] |
蔡声泽,许超,高琪,等. 基于深度神经网络的粒子图像测速算法[J]. 空气动力学学报,2019,37(3):455-461. doi: 10.7638/kqdlxxb—2019.0042
CAI S Z,XU C,GAO Q,et al. Particle image velocimetry based on a deep neural network[J]. Acta Aerodynamica Sinica,2019,37(3):455-461. doi: 10.7638/kqdlxxb—2019.0042
|
[21] |
GUO C Y,FAN Y W,HAN Y,et al. Deep-learning-based liquid extraction algorithm for particle image velocimetry in two-phase flow experiments of an object entering water[J]. Applied Ocean Research,2021,108:102526. doi: 10.1016/j.apor.2021.102526
|
[22] |
YU C D,FAN Y W,BI X J,et al. Deep particle image velocimetry supervised learning under light conditions[J]. Flow Measurement and Instrumentation,2021,80:102000. doi: 10.1016/j.flowmeasinst.2021.102000
|
[23] |
ABADI M, AGARWAL A, BARHAM P, et al. TensorFlow: Large-scale machine learning on heterogeneous systems[EB/OL]. [2021-11-22]. https://arxiv.org/pdf/1603.04467.pdf/.
|