用深度强化学习解决高超音速进气口失稳问题，实现强鲁棒性实时控制。
Hypersonic Flow Control: Generalized Deep Reinforcement Learning for Hypersonic Intake Unstart Control under Uncertainty
- 基于深度强化学习设计实时控制策略，应对高超音速进气口失稳。
- 在马赫数5、雷诺数5×10⁶条件下，成功稳定多种背压工况。
- 对未知工况具备零样本泛化能力，支持噪声传感器与最小传感配置。
高超音速进气口失稳是马赫数5及以上空吸推进系统可靠运行的重大挑战，由强烈的激波-边界层相互作用和快速压力波动引起。本文展示了一种基于深度强化学习（DRL）的主动流动控制方法，用于控制马赫数5、雷诺数5×10⁶条件下的二维典型高超音速进气口失稳问题。自研的CFD求解器结合自适应网格加密，能够高保真模拟激波运动、边界层动力学和流动分离等关键流场特征，确保学习到的控制策略具有物理一致性，适合实时部署。DRL控制器在多种代表燃烧室变化条件的背压范围内均能稳定进气口工作。其进一步展现出对未见场景的强大零样本泛化能力，包括不同背压、雷诺数及传感器配置，并在存在传感器噪声的情况下仍保持鲁棒性。通过最优选择的最小传感器集即可达到相近性能，支持实际应用。该成果建立了面向真实操作不确定性的数据驱动高超音速流动控制新范式。
原文摘要 · Abstract (English)
The hypersonic unstart phenomenon poses a major challenge to reliable air-breathing propulsion at Mach 5 and above, where strong shock-boundary-layer interactions and rapid pressure fluctuations can destabilize inlet operation. Here, we demonstrate a deep reinforcement learning (DRL)- based active flow control strategy to control unstart in a canonical two-dimensional hypersonic inlet at Mach 5 and Reynolds number $5\times 10^6$. The in-house CFD solver enables high-fidelity simulations with adaptive mesh refinement, resolving key flow features, including shock motion, boundary-layer dynamics, and flow separation, that are essential for learning physically consistent control policies suitable for real-time deployment. The DRL controller robustly stabilizes the inlet over a wide range of back pressures representative of varying combustion chamber conditions. It further generalizes to previously unseen scenarios, including different back-pressure levels, Reynolds numbers, and sensor configurations, while operating with noisy measurements, thereby demonstrating strong zero-shot generalization. Control remains robust in the presence of noisy sensor measurements, and a minimal, optimally selected sensor set achieves comparable performance, enabling practical implementation. These results establish a data-driven approach for real-time hypersonic flow control under realistic operational uncertainties.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。