基于深度注意力网络的压气机流场重构方法

doi:10.7527/S1000-6893.2024.30580

Abstract

Abstract:

The rapid and accurate prediction of compressor performance and key flow field characteristics is critical for digital design， digital twin modeling， and virtual-real interaction of compressors. This study focuses on the first-stage stator of a transonic three-stage compressor. A flow field database was established using high-precision numerical simulation results for 205 different operating conditions at various speeds. We employed a symmetric convolutional neural network with attention mechanisms to reconstruct flow field parameters such as static temperature， static pressure， and Mach number at different radii of the stator blade. This network comprises a visual self-attention model and symmetric convolutional neural network. The former extracts geometric features of blade passages at different radii under various operating conditions， while the latter conducts deeper feature extraction of these features and other inputs （including inlet and outlet boundary conditions， flow field coordinates， and distance fields） through transposed convolution and convolution operations； consequently， this network predicts the flow field at different radial positions. Our findings demonstrate that the symmetric convolutional neural network based on deep attention can rapidly and effectively predict the internal flow field of compressors. The model accurately captures changes in physical parameters near the blade wall， blade wakes， and flow separation. Compared with high-precision numerical simulation， the model achieves rapid and accurate reconstruction of the flow field of compressor stators with an average relative error not exceeding 1%.

Key words: compressor stator, deep learning, visual self-attention mechanism, symmetric convolution, flow field reconstruction

CLC Number:

V231.3

Yueteng WU, Dun BA, Juan DU, Yunfei LI, Juntao CHANG. Compressor flow field reconstruction method based on deep attention networks[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(24): 630580.

Figures/Tables 24

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相对换算转速	转速/（r·min^-1）	S1安装角/（°）
1.00	9 687.0	18.87
0.99	9 590.1	17.11
0.90	8 718.3	3.63
0.80	7 749.6	-5.42

模块	层名称	输出尺寸
嵌入层	重塑层	196×768
嵌入层	全连接层	196×512
Transformer 编码器×3	归一化层	197×512
	多头注意力	197×512
	残差连接	197×512
	归一化层	197×512
	多层感知机	197×512
	残差连接	197×512
特征输出	层归一化	512
特征输出	全连接层	8

模块	层名称	卷积核/步长/填充	输出尺寸
线性模块	全连接层1		5 632
线性模块	全连接层2		1 536
塑形	塑形层		256×2×3
转置卷积×7	转置卷积1	2×2/2/0	128×4×6
转置卷积×7	ELU		128×4×6
转置卷积	转置卷积8	2×2/2/0	1×512×768
转置卷积	ELU		1×512×768
卷积×7	卷积1	2×2/2/0	2×256×384
卷积×7	ELU		2×256×384
卷积	卷积8	2×2/2/0	256×2×3
	ELU		256×2×3
	平均池化层	2×2/2/0×1	256×1×2
塑形	扁平化层		512
线性模块×5	全连接层3		1 024/512/128/32/1
线性模块×5	ELU		1 024/512/128/32/1

名称	相对换算转速	半径/m
训练集	0.8、0.83、0.85、0.87、0.89、0.91、0.93、0.95、0.97、0.99、1.015	0.18、0.21、0.24、0.27、0.30、0.33、0.36
验证集	0.86、0.90、0.94、0.98以及训练集转速上其他半径	0.18、0.21、0.24、0.27、0.30、0.33、0.36（部分工况增加了其他半径，如0.195、0.285等）
测试集	0.88、0.92、0.96、1.00以及训练集转速上其他半径	0.18、0.21、0.24、0.27、0.30、0.33、0.36（部分工况增加了其他半径，如0.255、0.345等）

名称	版本	时间/h
平台	Windows
CPU	Intel（R） Xeon（R） Platinum 8375C CPU @ 2.90 GHz
GPU	NVIDIA GeForce RTX4090	44
Cuda	11.8
Pytorch	2.0.0+cu118

Compressor flow field reconstruction method based on deep attention networks

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