高超音速流动控制：面向不确定条件下的通用深度强化学习用于高超音速进气口失速控制
Hypersonic Flow Control: Generalized Deep Reinforcement Learning for Hypersonic Intake Unstart Control under Uncertainty
AI总结：
本文提出一种基于深度强化学习的高超音速进气口失速控制方法，通过高保真模拟和物理一致的控制策略，在不确定条件下实现稳健的实时流动控制。
摘要：
高超音速失速现象对Mach 5及以上的可靠空气呼吸推进系统构成重大挑战，其中强激波-边界层相互作用和快速压力波动可能破坏进气口运行。本文展示了一种基于深度强化学习（DRL）的主动流动控制策略，用于控制Mach 5和雷诺数5×10⁶的典型二维高超音速进气口中的失速现象。自主研发的CFD求解器能够通过自适应网格细化进行高保真模拟，解析关键流动特征，包括激波运动、边界层动力学和流动分离，这对于学习物理一致的控制策略至关重要。DRL控制器在广泛的回压范围内稳定进气口，这些回压代表了不同的燃烧室条件。它进一步泛化到之前未见过的场景，包括不同的回压水平、雷诺数和传感器配置，同时在有噪声的测量条件下运行，从而展示了强大的零样本泛化能力。在存在噪声传感器测量的情况下，控制仍然稳健，且最小的、最优选择的传感器集能够实现可比性能，从而实现实际应用。这些结果建立了一种数据驱动的方法，用于在现实操作不确定性下的实时高超音速流动控制。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
