scholarly journals Flow field reconstruction and prediction of the supersonic cascade channel based on a symmetry neural network under complex and variable conditions

AIP Advances ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 065116 ◽  
Author(s):  
Yunfei Li ◽  
Juntao Chang ◽  
Chen Kong ◽  
Ziao Wang
Keyword(s):  
Neural Network ◽  
Flow Field ◽  
Field Reconstruction ◽  
Flow Field Reconstruction

Flow field reconstruction method based on array neural network

2020 ◽  
Vol 125 (1283) ◽  
pp. 223-243
Author(s):  
W. Yuqi ◽  
Y. Wu ◽  
L. Shan ◽  
Z. Jian ◽  
R. Huiying ◽  
...  
Keyword(s):  
Neural Network ◽  
Flow Field ◽  
Traditional Method ◽  
Field Data ◽  
Reconstruction Method ◽  
Target Problem ◽  
Field Reconstruction ◽  
Flow Field Characteristics ◽  
Database Technology ◽  
Flow Field Reconstruction

ABSTRACTMulti-dimensional aerodynamic database technology is widely used, but its model often has the curse of dimensionality. In order to solve this problem, we need projection to reduce the dimension. In addition, due to the lack of traditional method, we have improved the traditional flow field reconstruction method based on artificial neural networks, and we proposed an array neural network method.In this paper, a set of flow field data for the target problem of the fixed Mach number is obtained by the existing CFD method. Then we arrange all the sampled flow field data into a matrix and use proper orthogonal decomposition (POD) to reduce the dimension, whose size is determined by the first few modals of energy. Therefore, significantly reduced data are obtained. Then we use an arrayed neural network to map the flow field data of simplified target problem and the flow field characteristics. Finally, the unknown flow field data can be effectively predicted through the flow field characteristic and the trained array neural network.At the end of this paper, the effectiveness of the method is verified by airfoil flow fields. The calculation results show that the array neural network can reconstruct the flow field of the target problem more accurately than the traditional method, and its convergence speed is significantly faster. In addition, for the case of high angle flow field, the array neural network also performs well. There are no obvious jumps, and huge errors are found in results. In general, the proposed method is better than the traditional method.

Optimization of 3-D Divergence-Free Flow Field Reconstruction Using 2-D Ultrasound Vector Flow Imaging

2019 ◽  
Vol 45 (11) ◽  
pp. 3042-3055 ◽  
Author(s):  
Xinhuan Zhou ◽  
Peter Vincent ◽  
Xiaowei Zhou ◽  
Chee Hau Leow ◽  
Meng-Xing Tang
Keyword(s):  
Flow Field ◽  
Free Flow ◽  
Flow Imaging ◽  
Field Reconstruction ◽  
Divergence Free ◽  
Flow Field Reconstruction

Application of full flow field reconstruction to a viscoelastic liquid in a 2D cross-slot channel

2013 ◽  
Vol 192 ◽  
pp. 10-19 ◽  
Author(s):  
Monirosadat Sadati ◽  
Clarisse Luap ◽  
Beat Lüthi ◽  
Martin Kröger ◽  
Hans Christian Öttinger
Keyword(s):  
Flow Field ◽  
Viscoelastic Liquid ◽  
Slot Channel ◽  
Field Reconstruction ◽  
Flow Field Reconstruction

scholarly journals How Euglena gracilis swims: Flow field reconstruction and analysis

2021 ◽  
Vol 103 (2) ◽  
Author(s):  
Nicola Giuliani ◽  
Massimiliano Rossi ◽  
Giovanni Noselli ◽  
Antonio DeSimone
Keyword(s):  
Flow Field ◽  
Euglena Gracilis ◽  
Field Reconstruction ◽  
Flow Field Reconstruction
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