Sequential estimation of disturbed aerodynamic flows from sparse measurements via a reduced latent space

Abstract

This work presents a fast, uncertainty-aware sequential data assimilation framework for estimating key aerodynamic states (e.g., instantaneous vorticity fields and aerodynamic loads) during severe gust encounters, where vortex-gust interactions strongly affect the flow dynamics. The framework comprises an ensemble Kalman filter (EnKF) designed to detect and reconstruct nearly impulsive flow disturbances with a wide range of strengths and orientations introduced at arbitrary times. The forecast and measurement update stages of the EnKF are composed of learned operators in a low-dimensional latent space obtained via a physics-augmented autoencoder. The forecast operator propagates undisturbed baseline dynamics but cannot predict random gust-induced deviations. The analysis stage therefore frequently assimilates surface pressure measurements to detect disturbance signals and initiate deviations from the nominal trajectory. The methodology is trained and tested on flowfield snapshots from high-fidelity simulations of two-dimensional airfoil-gust encounters and corresponding sparse pressure data. Because assimilation occurs entirely in the latent space, updates are computationally efficient and aerodynamic states can be continuously estimated from streaming pressure measurements. The latent state remains physically interpretable via decoding to the original high-dimensional flow. Eigenvalue decomposition of state and observation Gramians reveals the dominant correction directions required to capture the disturbance and quantifies how sensors inform state corrections during gust interaction. The framework also accounts for sensor failure: sensor-dropout experiments show that the EnKF adaptively reweights neighboring sensors to compensate for lost information, preserving estimation quality under degraded sensing.