@article{ZHU2026, 
author = {Xunhan ZHU and Zhenwei DING and Wei XIE and Zhenbing LUO and Yan ZHOU},
title = {Active jet control of type Ⅳ shock/shock interaction and rapid flow field reconstruction},
year = {2026},
journal = {Acta Aeronautica et Astronautica Sinica},
volume = {47},
number = {16},
keywords = {shock/shock interaction, active flow control, active jet, flow field regulation, flow field prediction},
url = {https://www.sciopen.com/article/10.7527/S1000-6893.2026.33188},
doi = {10.7527/S1000-6893.2026.33188},
abstract = {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.}
}