Predictive analysis of flow control in high-speed complex flow field based on machine learning

YU Baiyang; LYU Hongqiang; ZHOU Yan; LUO Zhenbing; LIU Xuejun

doi:10.11729/syltlx20210168

Volume 36 Issue 3

Jul. 2022

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Article Navigation > Journal of Experiments in Fluid Mechanics > 2022 > 36(3): 44-54

YU B Y，LYU H Q，ZHOU Y，et al. Predictive analysis of flow control in high-speed complex flow field based on machine learning[J]. Journal of Experiments in Fluid Mechanics, 2022，36（3）：44-54. doi: 10.11729/syltlx20210168

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Predictive analysis of flow control in high-speed complex flow field based on machine learning

doi: 10.11729/syltlx20210168

YU Baiyang^{1, 2, 3, 4
,},
LYU Hongqiang⁵,
ZHOU Yan⁶,
LUO Zhenbing⁶,
LIU Xuejun^{1, 4
,
,}

1.
Key Laboratory of Pattern Analysis and Machine Intelligence, Ministry of Industry and Information Technology, College of Computer Science and Technology / College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing　211106, China
2.
State Key Laboratory of Aerodynamics, Mianyang　621000 , China
3.
Laboratory of Aerodynamic Noise Control, Mianyang　621000, China
4.
Collaborative Innovation Centre of Novel Software Technology and Industrialization, Nanjing　210023, China
5.
College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing　210016, China
6.
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha　410073, China

Received Date: 2021-10-29
Accepted Date: 2022-02-11
Rev Recd Date: 2022-02-09

Available Online: 2022-07-04

Publish Date: 2022-07-04

Abstract

Abstract

The flow control actuator is the core of the active flow control technology. The design level and performance of actuator directly determine the application direction and effect of active flow control. In order to obtain the action law of the flow control actuator, a large number of experiments are needed to study the influence of excitation parameters on control effect parameters, and the experimental cost is large. In this paper, the experimental data of jet shock control in reverse plasma synthesis are used, and the Gaussian process regression model in machine learning is used to obtain the mapping law from the actuator parameters （head cone diameter, cavity volume, discharge capacitance and outlet diameter） to the control effect parameters （maximum out of body distance）. We compare the prediction effects of Gaussian process regression under various kernel functions, and analyze the influence of actuator parameters on control effect parameters by using the characteristic importance analysis method. The results show that for this small sample problem, Gaussian process regression with the quadratic polynomial kernel function Poly2 obtains the highest accuracy; in characteristic importance analysis, the head cone diameter has the greatest influence on the maximum separation distance, followed by discharge capacitance and cavity volume. The influence of these two parameters is similar, and the influence of the outlet diameter is the least. The work of this paper can provide some guidance for the setting of various parameters of the actuator in the flow control experiment of the high-speed complex flow field.
- active flow control,
- exciter,
- machine learning,
- Gaussian process,
- feature importance analysis

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References(32)

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