AI 中文总结
该研究针对钝体尾流的潜空间降阶模型，发现其自回归滚出误差主要为累积相位漂移，提出的相位校正方法可有效消除非平稳流动中的滚出误差。
AI 中文摘要
自回归降阶模型存在复合长程滚出误差，这类误差通常被视为非结构化噪声。本文针对雷诺数Re=100至800范围内的钝体尾流，证明该滚出误差具有高度结构性，可揭示模型实际学习到的内容。针对卷积自编码器LSTM模型，95%至98%的误差为纯相位误差，在涡脱频率处达到峰值。该网络几乎精确重现吸引子几何，极限环振幅匹配度达0.15%以内，但循环遍历速率略有偏差。这种定时误差在整个滚出过程中仅累积为几千分之一的循环，即使单步验证误差近乎完美，仍会驱动长程误差。由于相位误差线性漂移，可仅用每个潜坐标一个参数、在短校准窗口上拟合，无需重新训练即可离线校正。该相位拟合的信噪比作为诊断指标，可可靠预测校正成功率（50个网络的相关系数r=0.85）。简单周期基线在平稳极限循环上匹配该性能，但应用于慢变入流驱动的尾流时，误差会超出一个数量级。相反，本文的相位校正仅需相位相干性，在非平稳流动中成功消除约一半的滚出误差。
英文摘要
Autoregressive reduced order models suffer from compounding long horizon rollout errors, typically treated as unstructured noise. We demonstrate that for bluff body wakes across Re=100 to 800, this rollout error is highly structured and reveals what these models actually learn. For a convolutional autoencoder LSTM model, 95 % to 98% of the error is pure phase error, peaking sharply at the vortex shedding frequency. The network reproduces the attractor geometry almost exactly, matching limit cycle amplitudes within 0.15%, but traverses the cycle at slightly the wrong rate. This timing error, accumulating to just a few thousandths of a cycle over the entire rollout, drives the long horizon error even while one step validation errors appear virtually perfect. Because phase error drifts linearly, it can be corrected offline without retraining using just one parameter per latent coordinate, fitted on a short calibration window. The signal to noise ratio of this phase fit serves as a diagnostic that reliably predicts correction success (r=0.85 across 50 networks). While simple periodic baselines match this performance on stationary limit cycles, they fail by over an order of magnitude when applied to wakes driven by slowly varying inflows. Conversely, our phase correction requires only phase coherence, successfully removing roughly half of the rollout error during non stationary flow.
Comments22 Pages, 8 Figures
