主动射流调控第Ⅳ类激波干扰与流场快速重构(流动控制与热管理专栏)

  • 朱训涵 ,
  • 周岩 ,
  • 丁振伟 ,
  • 谢玮 ,
  • 罗振兵
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  • 1. 国防科技大学空天科学学院
    2. 国防科技大学

收稿日期: 2025-12-04

  修回日期: 2026-03-17

  网络出版日期: 2026-03-19

基金资助

国家自然科学基金;湖南省科技创新计划资助

Active Jet Control of the Fourth Type Shock Wave Interference and Rapid Flow Field Reconstruction

  • ZHU Xun-Han ,
  • ZHOU Yan ,
  • DING Zhen-Wei ,
  • XIE Wei ,
  • LUO Zhen-Bing
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Received date: 2025-12-04

  Revised date: 2026-03-17

  Online published: 2026-03-19

摘要

激波干扰伴随的极端热载荷,严重影响高超声速飞行器性能指标和结构安全,其中第Ⅳ类激波干扰的影响最大。使用数值模拟的方法,系统地研究了主动射流中的定常/振荡射流对第Ⅳ类激波干扰的调控效果及机理。结果显示,定常射流能显著改善第Ⅳ类激波干扰的流场特性:当射流压比(PR)为5时,与无控流场相比,钝头体壁面阻力与最大热流通量分别降低40.7%、40%。核心调控机理为,激波干扰点前移,激波干扰类型由第Ⅳ类向类似第Ⅲ类转变,流场结构由两个三激波点转变为单个三激波点,消除了原来交替出现的膨胀波和压缩波对壁面的冲击。进一步研究表明,定常射流的减阻降热效果随PR增大而增强,但射流压比增大增加了流场不稳定性。相比之下,相同位置的振荡射流因内部压力衰减及单向振荡效应,同PR下的流场调控效果弱于定常射流。为克服传统CFD计算成本较大的瓶颈以快速寻找来流参数下的最优射流压比,基于M-P神经元构建了深度神经网络(DNN)模型以实现流场快速重构。结果显示,该方法的流场预测速度较CFD提升4个数量级,PR<18时,预测精度超过0.99,为第Ⅳ类激波干扰的主动流动控制参数优化提供了高效方法。

本文引用格式

朱训涵 , 周岩 , 丁振伟 , 谢玮 , 罗振兵 . 主动射流调控第Ⅳ类激波干扰与流场快速重构(流动控制与热管理专栏)[J]. 航空学报, 0 : 1 -0 . DOI: 10.7527/S1000-6893.2026.33188

Abstract

The extreme thermal loads induced by shock/shock interaction seriously impair the performance metrics and structural safety of hypersonic vehicles, among which Type Ⅳ shock/shock interaction imposes the most significant impact. Using numerical simulation methods, the control effects and mechanisms of steady/oscillating jets in active jet systems on Type IV shock/shock interaction were systematically investigated. The results demonstrate that steady jets can remarkably improve the flow field characteristics of Type Ⅳ shock/shock interaction: when the jet pressure ratio (PR) is 5, compared with the uncontrolled flow field, the wall drag and maximum heat flux of the blunt body are reduced by 40.7% and 40%, respectively. The core control mechanism is that the shock interaction point moves forward, the shock interaction type transitions from Type IV to a Type III-like pattern, and the flow field structure changes from two triple-shock points to a single triple-shock point, eliminating the impact of the originally alternating expansion and compression waves on the wall. Further studies indicate that the drag and heat reduction effects of steady jets are strengthened with the increase of PR; however, a higher PR will aggravate the flow field instability. In contrast, oscillating jets arranged at the same position exhibit weaker flow control performance than steady jets under the same PR, which is due to internal pressure attenuation and the unidirectional oscillation effect. To address the bottleneck of high computational cost in traditional Computational Fluid Dynamics (CFD) simulations and efficiently determine the optimal PR under given incoming flow conditions, a Deep Neural Network (DNN) model based on McCulloch-Pitts (M-P) neurons was established for rapid flow field reconstruction. The results show that the flow field prediction speed of this method is increased by four orders of magnitude compared with CFD simulations, and the prediction accuracy exceeds 0.99 when PR < 18. This work provides an efficient approach for the parameter optimization of active flow control targeting Type Ⅳ shock/shock interactions.

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