AI-based real-time noise reduction of flow field pressure signals under plasma electromagnetic interference

CHEN Jie; ZONG Haohua; SONG Huimin; LIANG Hua; LIU Shimin; FANG Ziqi

doi:10.11729/syltlx20230030

Volume 37 Issue 4

Aug. 2023

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Article Navigation > Journal of Experiments in Fluid Mechanics > 2023 > 37(4): 59-65

CHEN J, ZONG H H, SONG H M, et al. AI-based real-time noise reduction of flow field pressure signals under plasma electromagnetic interference[J]. Journal of Experiments in Fluid Mechanics, 2023, 37(4): 59-65 doi: 10.11729/syltlx20230030

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AI-based real-time noise reduction of flow field pressure signals under plasma electromagnetic interference

doi: 10.11729/syltlx20230030

CHEN Jie^1
,,
ZONG Haohua^{2
,
,},
SONG Huimin^{1
,
,},
LIANG Hua¹,
LIU Shimin¹,
FANG Ziqi²

1.
National Key Lab of Aerospace Power System and Plasma Technology, Air Force Engineering University, Xi’an　710038, China
2.
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an　710049, China

Received Date: 2023-03-13
Accepted Date: 2023-04-27
Rev Recd Date: 2023-04-25
Publish Date: 2023-08-30

Abstract

Abstract

For the reliable sensing requirements of closed-loop active flow control, a real-time noise reduction method based on the artificial neural network was proposed for solving the plasma actuation electromagnetic interference on flow field signals. Taking the dynamic pressure sensor installed on the cylinder surface as the experimental subject, the “dense peak” type noise signals of alternating current dielectric barrier discharge (AC–DBD) and the “sparse spike” type noise signals of nanosecond pulsed dielectric barrier discharge (NS–DBD) were collected respectively. Artificial synthetic noise signals were used for supervised learning, and the generalization of the artificial neural network model was tested and verified. The results show that this method can effectively suppress the influence of electromagnetic interference caused by plasma actuation and restore the real pressure signal. It has better denoising performance on the AC–DBD “dense peak” type noise signal. The denoised signal is smoother and better fitted with the real one. This model is also applied to the real flow field pressure measurement, and the accuracy of the denoising network prediction is further verified by comparing the mean value of the denoised signal and the real signal.
- plasma actuation,
- surface dielectric barrier discharge,
- artificial neural network,
- denoising,
- pressure signal,
- flow around circular cylinder

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References

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