Abstract
This paper explores and implements the Sparse Identification of Nonlinear Dynamics (SINDy) for gust response analysis in low Reynold’s numbers. The SINDy is an algorithm to extract sparse dynamics from measured (or training) data and is rooted in system identification methods. We explored the response to a wide range of 1-cos gusts. We combined SINDy with interpolation over gust parameters to create a predictive model for three data sets. We studied the response to a given gust at variable angles of attack and the response at a given angle of attack to a wide range of gusts identified by gust length and gust intensity. More studies are required to improve SINDy to predict the gust response without using interpolation over the gust parameters.
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