arXiv subjects

Anya Jones

Publications and source records attributed to Anya Jones.

3 recordsLinked to original sources

arXiv2026

A learning-based joint flow and kinematic state estimation for bodies in highly disturbed flows

Accurate estimation of unsteady aerodynamic flows from sparse measurements remains a fundamental challenge, particularly in the presence of strong disturbances, unknown body kinematics, and incomplete observations. This study presents a data-driven sequential estimation framework for joint reconstruction of unsteady flow fields, aerodynamic loads, and airfoil kinematics from sparse measurements. The approach combines a kinematics-aware nonlinear flow autoencoder with online filtering of streaming measurements. In the resulting reduced-order representation, the forecast and observation operators are learned directly from data, with each forecast-assimilation cycle requiring only a few milliseconds. The framework is evaluated on two-dimensional incompressible flow over an airfoil subjected to random vortical gusts while undergoing arbitrary pitch-up motions. Results demonstrate accurate reconstruction from surface pressure sensors, vertical lines of vorticity sensors, and synthetic velocimetry data with representative shadow regions. The transient informativeness of pressure sensors is quantified through their time-varying contributions to dominant observation modes. While lift is reconstructed from the leading modes, drag, pitch angle, and angular velocity require higher modes. Incorporating limited off-body measurements alongside surface pressure improves observability and reduces estimation error and uncertainty.

physics.flu-dyn↗

arXiv2026

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

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.

physics.flu-dyn↗

arXiv2023

A cyclic perspective on transient gust encounters through the lens of persistent homology

Large amplitude gust encounters exhibit a range of separated flow phenomena, making them difficult to characterize using the traditional tools of aerodynamics. In this work, we propose a dynamical systems approach to gust encounters, viewing the flow as a cycle (or a closed trajectory) in state space. We posit that the topology of this cycle, or its shape and structure, provides a compact description of the flow, and can be used to identify coordinates in which the dynamics evolve in a simple, intuitive way. To demonstrate this idea, we consider flowfield measurements of a transverse gust encounter. For each case in the dataset, we characterize the full-state dynamics of the flow using persistent homology, a tool that identifies holes in point cloud data, and transform the dynamics to a reduced-order space using a nonlinear autoencoder. Critically, we constrain the autoencoder such that it preserves topologically relevant features of the original dynamics, or those features identified by persistent homology. Using this approach, we are able to transform six separate gust encounters to a three-dimensional latent space, in which each gust encounter reduces to a simple circle, and from which the original flow can be reconstructed. This result shows that topology can guide the creation of low-dimensional state representations for strong transverse gust encounters, a crucial step toward the modeling and control of airfoil-gust interactions.

physics.flu-dyn↗