Abstract
Swimmers and flyers in nature take advantage of distributed sensor feedback to control the interaction between the surrounding fluid and their bodies, even in challenging environments such as wake flows or gusts. Inspired by this behavior, we suggest a novel data-driven method that thereby could enable effective flow control. Sparse sensor data captured on the propulsor are combined with a pre-trained algorithm to provide an estimate of the present aerodynamic state. By combining transition network theory and Bayesian statistics, a low-order model of the highly non-linear system is obtained, which is robust towards the high noise levels ubiquitous in experimental (real) pressure data. For the current study, the flow around an accelerating elliptical plate is selected as a test case. The plate is accelerated and decelerated at various (fixed) angles of attack, and the flow is captured by load and pressure measurements. The aerodynamic loads are then estimated for angle-of-attack cases that were not included in the training data, thus showing the method effectiveness under unknown (untrained) configurations.