Accurate estimation of aerodynamic state variables such as freestream velocity and angle of attack (AoA) is important for aerodynamic load prediction, flight control, and model validation. This work presents a non-intrusive method for estimating vehicle velocity and AoA from structural vibration measurements rather than direct flow instrumentation such as pitot tubes. A dense array of piezoelectric sensors mounted on the interior skin of an aeroshell capture vibrations induced by turbulent boundary layer pressure fluctuations, and a convolutional neural network (CNN) is trained to invert these structural responses to recover velocity and AoA. Proof-of-concept is demonstrated through controlled experiments in Sandia's hypersonic wind tunnel spanning zero and nonzero AoA configurations, Mach~5 and Mach~8 conditions, and both constant and continuously varying tunnel operations. The CNN is trained and evaluated using data from 16 wind tunnel runs, with a temporally centered held-out interval within each run used to form training, validation, and test datasets and assess intra-run temporal generalization. Raw CNN predictions exhibit increased variance during continuously varying conditions; a short-window moving-median post-processing step suppresses this variance and improves robustness. After post-processing, the method achieves a mean velocity error relative to the low-pass filtered reference velocity below 2.27~m/s (0.21\%) and a mean AoA error of $0.44^{\circ} (8.25\%)$ on held-out test data from the same experimental campaign, demonstrating feasibility of vibration-based velocity and AoA estimation in a controlled laboratory environment.

翻译：准确估计自由流速度和攻角等气动状态变量对于气动载荷预测、飞行控制和模型验证至关重要。本研究提出一种非侵入式方法，通过结构振动测量而非皮托管等直接流场仪器来估计飞行器速度和攻角。在气壳内表面安装的密集压电传感器阵列捕捉湍流边界层压力波动引起的振动，并训练卷积神经网络（CNN）反向解析这些结构响应以复原速度和攻角。通过在桑迪亚高超声速风洞中进行受控实验验证概念可行性，实验涵盖零攻角和非零攻角配置、马赫数5和马赫数8条件，以及恒定和连续变化的隧道运行状态。利用16次风洞运行的数据训练和评估CNN，每次运行中采用时间居中的保留区间构建训练集、验证集和测试集，并评估运行内的时间泛化能力。原始CNN预测在连续变化条件下方差增大；通过短窗口移动中值后处理步骤抑制该方差并提升鲁棒性。经过后处理，该方法相对于低通滤波参考速度的平均速度误差低于2.27米/秒（0.21%），对同一实验活动中保留测试数据的平均攻角误差为0.44度（8.25%），验证了在受控实验室环境中基于振动的速度和攻角估计的可行性。