发表机构
Spanish National Institute for Aerospace Technology (INTA); Universidad Carlos III de Madrid; University of Washington（西班牙国家航空技术研究所; 马德里卡洛斯三世大学; 华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究受控尾流中数据驱动潜在空间降阶模型，比较POD、CAEs等压缩方法及基于长短期记忆网络的预测器，发现CAEs压缩效率高但长期预测差，POD潜在轨迹更平滑，揭示紧凑性与预测精度权衡，为实时流动控制设计预测ROMs提供指导。
AI 中文摘要
基于模型的主动流动控制需要准确、稳定且足够快的预测模型以进行实时优化。在受控尾流中，常通过降阶模型（ROMs）实现，先将高维速度快照压缩到潜在空间，再学习潜在空间动力学的时间步预测器。本文研究空间编码器的选择如何影响受控输入下尾流的潜在坐标预测性。使用两种二维尾流配置，比较了用于压缩的本征正交分解（POD）与非线性卷积自动编码器（CAEs）及两种变分自动编码器，并评估了基于长短期记忆网络的时间预测器。结果表明CAEs压缩效率更高但长期预测较差，POD的潜在轨迹更平滑，预测更可靠。揭示了紧凑性与预测精度间的权衡，表明潜在动力学预测的稳定性可能比最大压缩更重要，为实时流动控制设计预测ROMs提供了实用指导。
英文摘要
Model-based active flow control requires predictive models that are accurate, stable, and fast enough for real-time optimisation. In controlled wake flows, this is often achieved through Reduced-Order Models (ROMs) that first compress high-dimensional velocity snapshots into a latent space and then learn a time- stepping predictor for the dynamics in the latent space. Here, we study how the choice of the spatial encoder affects the predictability of the resulting latent coordinates for wake flows under control inputs. Using two actuated 2D wake configurations, a simplified truck wake and the fluidic pinball, we compare Proper Orthogonal Decomposition (POD) against nonlinear Convolutional Autoencoders (CAEs) and two types of variational autoencoders for compression, and evaluate several temporal predictors based on Long Short-Term Memory networks. CAEs achieve higher compression efficiency and sharper short-term reconstructions, but they produce latent dynamics that are more irregular and with broadband spectral content. As a consequence, long-horizon forecasts degrade faster and show a higher probability of catastrophic divergence than POD-based models. POD yields smoother latent trajectories that are easier to learn and extrapolate, leading to more reliable predictions beyond the short- term regime. These results reveal a clear trade-off between compactness and forecast accuracy, and suggest that the stability of the latent dynamics prediction can outweigh maximal compression. This is particularly relevant for control strategies rooted in forecasts of the dynamics, such as model predictive control and reinforcement learning. The findings provide practical guidance for designing actuation-aware, hardware-feasible predictive ROMs for real-time flow control.
Comments32 pages, 20 figures
