Skip to Content

Search: {{$root.lsaSearchQuery.q}}, Page {{$}}

Applied Interdisciplinary Mathematics (AIM) Seminar

Data assimilation for aerodynamic flow estimation
Friday, January 21, 2022
3:00 PM-12:00 AM
Off Campus Location
Small flight vehicles are more agile but also more strongly affected by unexpected disturbances ('gusts') than larger vehicles. The non-linear aerodynamics of these gust encounters remains a principal challenge in controlling the vehicle's flight. In particular, it is likely that an effective flight control strategy will depend on an estimate of the disturbed flow around the vehicle, rather than just the vehicle's own state. In this talk, I will discuss our work on dynamic estimation of vortical flows from limited sensor data. I will first discuss the characteristics of the Ensemble Kalman Filter, which enables the practical assimilation of sensor data into an ensemble of large-dimensional, non-linear physics-based models. The assimilation of these data can correct for the physics that are unrepresented in the models. In the examples I will show, we use a vortex model to predict the fluid dynamics of the separated flow, and rely on the surface pressure measurements to inform the model of disturbances to that flow. I will discuss various improvements we have made, including a novel rank-reduction technique that greatly reduces the spurious correlations that arise in finite ensembles between measurements and states. The overall estimation algorithm is applied to several disturbed aerodynamic flows in which measurement data are obtained from a high-fidelity Navier-Stokes simulation. I will show that the data-assimilating vortex model efficiently and accurately predicts the evolving flow as well as the normal force in the presence of strong disturbances, without any knowledge of the disturbance characteristics. Speaker(s): Jeff Eldredge (UCLA)
Building: Off Campus Location
Location: Virtual
Event Type: Workshop / Seminar
Tags: Mathematics
Source: Happening @ Michigan from Department of Mathematics