📝 SummarXiv

Abstract

This work presents a fast, scalable, and uncertainty-aware methodology for real-time estimation of key aerodynamic states, including instantaneous vorticity fields and aerodynamic loads, during severe gust encounters where strong flow separation and vortex-gust interactions dominate the dynamics. The methodology, trained and tested on high-fidelity simulations of two-dimensional wing-gust encounters with sparse pressure data, combines physics-based ensemble filtering with data-driven surrogate modeling in a learned low-order space. Within this reduced space, latent dynamics and sensor observation operators are learned using neural networks, enabling efficient modeling of complex aerodynamic responses. These components are embedded within a low-rank Ensemble Kalman Filter (LREnKF), yielding a computationally efficient scheme that combines data-driven expressivity with physical interpretability in the original high-dimensional space. Because assimilation occurs entirely within the latent space, updates are fast enough for real-time use, ensuring that aerodynamic states can be continuously estimated from streaming pressure data. The filter corrects predictions predominantly in directions that are both dynamically significant and observable, improving efficiency and reducing spurious adjustments. An observability analysis shows how sensor informativeness evolves during wing-gust interactions, and sensor dropout experiments demonstrate that the LREnKF adaptively re-weights neighboring sensors to preserve estimation quality under degraded sensing.