@misc{b8bb10f047f04ddc9120ede8c6f19789,
title = "Grassmannian Shape Representations for Aerodynamic Applications",
abstract = "Airfoil shape design is a classical problem in engineering, science, and manufacturing. Our motivation is to combine principled physics-based considerations for the shape design problem with modern computational techniques informed by a data-driven approach. Traditional analyses of airfoil shapes emphasize a flow-based sensitivity to deformations which can be represented generally by affine transformations (rotation, scaling, shearing, shifting). We present a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data, (ii) improved low-dimensional parameter domain for inferential statistics, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes.",
keywords = "blade representation, data-driven deformations, Grassmannian, principal geodesic analysis, shape representation",
author = "Olga Doronina and Zach Grey and Andrew Glaws",
year = "2022",
language = "American English",
series = "Presented at the Association for the Advancement of Artificial Intelligence (AAAI-22), 22 February - 1 March 2022",
publisher = "National Laboratory of the Rockies (NLR)",
address = "United States",
type = "Other",
}