Active control of supersonic jet based on multiple microjets

Fan Dewei; Xue Yuling; Ning Yinghao; Arun Kumar Perumal

doi:10.7638/kqdlxxb-2024.0163

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Active control of supersonic jet based on multiple microjets

更新时间：2026-05-28

- Active control of supersonic jet based on multiple microjets
- Vol. 44, Issue 1, Pages: 76-84(2026)
- 作者机构：
  
  1. 杭州市北京航空航天大学国际创新研究院（北京航空航天大学国际创新学院）,杭州,311115
  2. 中国烟草总公司郑州烟草研究院,郑州,450001
  3. 印度理工学院（坎普尔）航空航天工程系，坎普尔,208016
- 作者简介：
- 基金信息：
- DOI：10.7638/kqdlxxb-2024.0163
  CLC： V211.3
- Published：2026
- 稿件说明：
移动端阅览
范德威, 薛雨凌, 宁英豪, 等. 基于多支微射流的超声速射流主动控制[J]. 空气动力学学报, 2026, 44(1): 76−84.

Fan Dewei, Xue Yuling, Ning Yinghao, et al. Active control of supersonic jet based on multiple microjets[J]. 2026, 44(1): 76-84.
范德威, 薛雨凌, 宁英豪, 等. 基于多支微射流的超声速射流主动控制[J]. 空气动力学学报, 2026, 44(1): 76−84. DOI： 10.7638/kqdlxxb-2024.0163

Fan Dewei, Xue Yuling, Ning Yinghao, et al. Active control of supersonic jet based on multiple microjets[J]. 2026, 44(1): 76-84. DOI： 10.7638/kqdlxxb-2024.0163.

摘要

在国家大力发展航空航天产业的背景下，研究超声速射流主动控制技术具有重要意义。以增强超声速射流混合为目标，针对经典圆形超声速射流混合开展主动控制实验研究。使用侧向注入微射流作为激励装置，研究主射流出口处压比、微射流与主射流质量流量比和微射流注入支数这3个关键参数对超声速射流核心区长度的影响规律。结果表明：固定质量流量比和微射流支数，核心区长度的控制效果随着微射流出口处的压比增加逐渐减弱；微射流支数和出口处压比不变，核心区长度与微射流及主射流的质量流量比呈较为复杂的非线性关系；固定微射流出口处压比时，增加微射流支数，并未显著提升射流混合效果；射流的控制效果随出口压比的增加而减弱，当出口压比为2.50、3.00、3.67、5.00和7.00时，射流核心区长度可分别减小62%、60%、53%、46%和8%。此外，还基于泰勒展开和反向传播（back propagation

BP）神经网络算法分别建立了两种预测射流核心区长度模型。结果表明：在微射流控制下获得的最优值$ {{L}}_{\text{c

opt}}^{\text{*}} $ = 8.7附近，两者均表现出较好的预测能力；而当远离最优值时，BP神经网络对超声速射流核心区长度的预测结果具有更高的准确性，误差仅为基于泰勒展开方法的1/6。本文将实验结果与BP神经网络相结合，为超声速射流的主动控制与工程优化提供了高精度智能预测方法，显著提升了混合效果并降低了实验成本。

Abstract

This study experimentally investigates the active control of a Mach 1.5 round jet using multiple steady radial microjets to enhance jet mixing. The control parameters include the pressure ratio of the main jet exit

the mass flow ratio of microjet to main jet

and the number of microjets. The effect of these parameters on supersonic jet mixing is revealed through experimental results. Results indicate that as the pressure ratio increases

the control performance of the supersonic jet gradually decreases from under- to over-expanded conditions. There is a complex nonlinear relationship between the mass flow ratio and the core length of the supersonic jet. Besides

increasing the number of microjets does not enhance the supersonic jet mixing. While the control effect decreases with the increase of the pressure ratio of the main jet. For pressure ratios of 2.50

3.00

3.67

5.00

and 7.00

the optimal core length can be reduced by up to 62%

60%

53%

46%

and 8%

respectively. Two prediction models are proposed to link the control parameters with the control targets based on the Taylor expansion and BP neural network algorithm. Both models exhibit better prediction ability near the optimal value. When predictions deviate from the optimal region

the BP neural network yields more accurate estimates of the supersonic jet core length

with a prediction error only 6% of that based on the Taylor expansion method.

关键词

Keywords

references

Ho C M, Huerre P. Perturbed free shear layers[J]. Annual Review of Fluid Mechanics, 1984, 16: 365−422. doi: 10.1146/annurev.fl.16.010184.002053

Gutmark E J, Schadow K C, Yu K H. Mixing enhancement in supersonic free shear flows[J]. Annual Review of Fluid Mechanics, 1995, 27: 375−417. doi: 10.1146/annurev.fl.27.010195.002111

Su H M, Cai J S, Qu K, et al. Numerical simulations of inert and reactive highly underexpanded jets[J]. 2020, 32(3): 036104. doi: 10.1063/1.5144558

Mali A K, Jana T, Kaushik M, et al. A survey on control techniques to augment compressible jet mixing[J]. Flow, Turbulence and Combustion, 2025, 114(1): 1−48. doi: 10.1007/s10494-024-00588-6

Kaushik M. Theoretical and experimental aerodynamics[M]. Singapore: Springer, 2019.

Kaushik M. Fundamentals of gas dynamics[M]. Singapore: Springer, 2022. doi: 10.1007/978-981-16-9085-3

Xue F, Yunsong G, Wang Y, et al. Research on control effectiveness of fluidic thrust vectoring[J]. Science Progress, 2021, 104(1): 36850421998137. doi: 10.1177/0036850421998137

Knowles K, Saddington A J. A review of jet mixing enhancement for aircraft propulsion applications[J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2006, 220(2): 103−127. doi: 10.1243/09544100g01605

Rao A N, Kushari A, Chandra Mandal A. Screech characteristics of under-expanded high aspect ratio elliptic jet[J]. Physics of Fluids, 2020, 32(7): 076106. doi: 10.1063/5.0010186

Rao S M V, Karthick S K, Anand A. Elliptic supersonic jet morphology manipulation using sharp-tipped lobes[J]. 2020, 32(8): 086107. doi: 10.1063/5.0015035

Akram S, Perumal A K, Rathakrishnan E. Effect of tab parameters on the near-field mixing characteristics of a Mach 1.5 elliptic jet[J]. 2021, 33(3): 036114. doi: 10.1063/5.0040489

Thangaraj T, Kaushik M, Deb D, et al. Survey on vortex shedding tabs as supersonic jet control[J]. Frontiers in Physics, 2022, 9: 842. doi: 10.3389/fphy.2021.789742

Cican G, Deaconu M, Crunteanu D E. Impact of using chevrons nozzle on the acoustics and performances of a micro turbojet engine[J]. Applied Sciences, 2021, 11(11): 5158. doi: 10.3390/app11115158

Bradbury L J S, Khadem A H. The distortion of a jet by tabs[J]. Journal of Fluid Mechanics, 1975, 70: 801−813. doi: 10.1017/S0022112075002352

Kumar Perumal A. Performance analysis of fluidic injector for supersonic jet manipulation[J]. AIAA Journal, 2023: 1-11. doi: 10.2514/1.j063295

Luo Zhenbing, Xia Zhixun, Deng Xiong, et al. Research progress of dual synthetic jets and its flow control technology[J]. Acta Aerodynamica Sinica, 2017, 35(2): 252−264(in Chinese). doi: 10.7638/kqdlxxb-2017.0053

Wiltse J M, Glezer A. Manipulation of free shear flows using piezoelectric actuators[J]. Journal of Fluid Mechanics, 1993, 249: 261. doi: 10.1017/s002211209300117x

Crow S C, Champagne F H. Orderly structure in jet turbulence[J]. Journal of Fluid Mechanics, 1971, 48: 547−591. doi: 10.1017/S0022112071001745

Portugal S, Choudhury B, Cardenas D. Advances on aerodynamic actuation induced by surface dielectric barrier discharges[J]. Frontiers in Physics, 2022, 10: 923103. doi: 10.3389/fphy.2022.923103

Pham A V, Inaba K, Saito M, et al. Effect of plasma actuator on velocity and temperature profiles of high aspect ratio rectangular jet[J]. Fluids, 2022, 7(8): 281. doi: 10.3390/fluids7080281

Davis M R. Variable control of jet decay[J]. AIAA Journal, 1982, 20(5): 606−609. doi: 10.2514/3.7934

Yu S C M, Lim K S, Chao W, et al. Mixing enhancement in subsonic jet flow using the air-tab technique[J]. AIAA Journal, 2008, 46(11): 2966−2969. doi: 10.2514/1.37187

Fan D W, Zhou Y, Noack B R. Fast triple-parameter extremum seeking exemplified for jet control[J]. Experiments in Fluids, 2020, 61(7): 152. doi: 10.1007/s00348-020-02953-3

Arakeri V H, Krothapalli A, Siddavaram V, et al. On the use of microjets to suppress turbulence in a Mach 0.9 axisymmetric jet[J]. Journal of Fluid Mechanics, 2003, 490: 75−98. doi: 10.1017/s0022112003005202

New T H, Tay W L. Effects of cross-stream radial injections on a round jet[J]. Journal of Turbulence, 2006, 7: N57. doi: 10.1080/14685240600847466

Morris P J, Mclaughlin D K, Kuo C W. Noise reduction in supersonic jets by nozzle fluidic inserts[J]. Journal of Sound and Vibration, 2013, 332(17): 3992−4003. doi: 10.1016/j.jsv.2012.11.023

Henderson B. Fifty years of fluidic injection for jet noise reduction[J]. International Journal of Aeroacoustics, 2010, 9(1-2): 91−122. doi: 10.1260/1475-472x.9.1-2.91

Cuppoletti D R, Gutmark E. Fluidic injection on a supersonic jet at various Mach numbers[J]. AIAA Journal, 2014, 52(2): 293−306. doi: 10.2514/1.j010000

Perumal A K, Rathakrishnan E. Design of fluidic injector for supersonic jet manipulation[J]. AIAA Journal, 2022, 60(8): 4639−4648. doi: 10.2514/1.j061257

Ponton M K, Seiner J M. The effects of nozzle exit lip thickness on plume resonance[J]. Journal of Sound and Vibration, 1992, 154(3): 531−549. doi: 10.1016/0022-460X(92)90784-U

Fan D W, Zhou Y. Scaling and classification of a minijet-manipulated turbulent jet[J]. Physical Review Fluids, 2022. doi: 10.1103/physrevfluids.7.07460

Zhou Y, Fan D W, Zhang B F, et al. Artificial intelligence control of a turbulent jet[J]. Journal of Fluid Mechanics, 2020, 897: A27. doi: 10.1017/jfm.2020.392

Murakami E, Papamoschou D. Mixing layer characteristics of coaxial supersonic jets[C]// 6 th Aeroacoustics Conference and Exhibit, Lahaina, HI, USA. Virginia: AIAA, 2012. doi: 10.2514/6.2000-2060

Chauvet N, Deck S, Jacquin L. Numerical study of mixing enhancement in a supersonic round jet[J]. AIAA Journal, 2007, 45(7): 1675−1687. doi: 10.2514/1.27497

Fan D W, Zhang B F, Zhou Y, et al. Optimization and sensitivity analysis of active drag reduction of a square-back Ahmed body using machine learning control[J]. Physics of Fluids, 2020, 32(12): 125117. doi: 10.1063/5.0033156

Wang Xiaochuan, Shi Feng, Yu Lei, et al. Analysis of 43 cases of MATLAB neural network[M]. Beijing: Beijing University of Aeronautics Astronautics Press, 2013(in Chinese).

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Aerodynamic and wind energy harvesting integrative design for UAVs

A multi-fidelity surrogate model with sparse neural kernel

Flow field prediction and reconstruction of aircraft based on flexible smart skin

Data-driven rapid prediction of surface heat flux for lifting-body aircraft

Deep neural network-based reduced-order modeling of high-speed airfoil flow field

Related Author

Luo Yang

Yang Wenqing

Song Bifeng

An Weigang

Wang Wei

Jia Jiaxing

Lian Feng

Li Xinran

Related Institution

School of Aeronautics, Northwestern Polytechnical University, Xi'an

National Key Laboratory of Aircraft Configuration Design, Xi'an

School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an

State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology , Wuhan

China Academy of Aerospace Aerodynamics, Beijing

AI问答

⁰