6 papers
Sequential estimation of disturbed aerodynamic flows from sparse measurements via a reduced latent space
Hanieh Mousavi, Anya Jones, Jeff Eldredge
This work presents a fast, uncertainty-aware sequential data assimilation framework for estimating key aerodynamic states (e.g., instantaneous vorticity fields and aerodynamic load…
Quantification and Classification of Carbon Nanotubes in Electron Micrographs using Vision Foundation Models
Sanjay Pradeep, Chen Wang, Matthew M. Dahm +2
Accurate characterization of carbon nanotube morphologies in electron microscopy images is vital for exposure assessment and toxicological studies, yet current workflows rely on sl…
A practical guide to estimation and uncertainty quantification of aerodynamic flows
Jeff D. Eldredge, Hanieh Mousavi
Many applications in aerodynamics, particularly in closed-loop control, depend on sensors to estimate the evolving state of the flow. This estimation task is inherently accompanied…
Attention on flow control: transformer-based reinforcement learning for lift regulation in highly disturbed flows
Zhecheng Liu, Jeff D. Eldredge
A linear flow control strategy designed for weak disturbances may not remain effective in sequences of strong disturbances due to nonlinear interactions, but it is sensible to leve…
Model-Based Reinforcement Learning for Control of Strongly-Disturbed Unsteady Aerodynamic Flows
Zhecheng Liu, Diederik Beckers, Jeff D. Eldredge
The intrinsic high dimension of fluid dynamics is an inherent challenge to control of aerodynamic flows, and this is further complicated by a flow's nonlinear response to strong di…
Low-Order Flow Reconstruction and Uncertainty Quantification in Disturbed Aerodynamics Using Sparse Pressure Measurements
Hanieh Mousavi, Jeff D. Eldredge
This paper presents a novel machine-learning framework for reconstructing low-order gust-encounter flow field and lift coefficients from sparse, noisy surface pressure measurements…