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Francis D. Lagor

Publications and source records attributed to Francis D. Lagor.

2 recordsLinked to original sources

Experimental mitigation of large-amplitude transverse gusts via closed-loop pitch control

Air vehicles of all scales are susceptible to large-amplitude gusts that may lead to vehicle damage and instability. Therefore, effective physics-based control strategies and an understanding of the dominant unsteady flow physics underpinning gust encounters are desired to improve safety and reliability of flight in these conditions. To this end, this paper develops and experimentally demonstrates a proportional output-feedback lift regulation strategy based on the classical unsteady aerodynamic theories of Wagner and Kussner for wings encountering large-amplitude transverse gusts without a priori knowledge of gust strength or onset time. The tested vertical gust velocities ranged between 25% and 71% of the freestream speed. This strategy is found to successfully generalize to gusts of different strengths and directions, as well as wings at pre- and post-stall angles of attack. In addition, this paper applies dynamical systems analysis to Wagner's aerodynamic model to reveal the effect of the pitch-axis location, pitch input, and closed-loop feedback gains on the stability and robustness of the system along with the flow physics responsible. The real-time output feedback control strategy is experimentally tested in a water tow tank facility equipped with a transverse gust generator. Time-resolved force and flow-field measurements are used to discover the salient flow physics during these encounters and illustrate how closed-loop actuation mitigates their lift transients.

physics.flu-dyn

Reduced-order modeling using Dynamic Mode Decomposition and Least Angle Regression

Dynamic Mode Decomposition (DMD) yields a linear, approximate model of a system's dynamics that is built from data. We seek to reduce the order of this model by identifying a reduced set of modes that best fit the output. We adopt a model selection algorithm from statistics and machine learning known as Least Angle Regression (LARS). We modify LARS to be complex-valued and utilize LARS to select DMD modes. We refer to the resulting algorithm as Least Angle Regression for Dynamic Mode Decomposition (LARS4DMD). Sparsity-Promoting Dynamic Mode Decomposition (DMDSP), a popular mode-selection algorithm, serves as a benchmark for comparison. Numerical results from a Poiseuille flow test problem show that LARS4DMD yields reduced-order models that have comparable performance to DMDSP. LARS4DMD has the added benefit that the regularization weighting parameter required for DMDSP is not needed.

stat.ML