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.
Original language | American English |
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Number of pages | 8 |
State | Published - 2022 |
Event | 36th AAAI Conference on Artificial Intelligence (AAAI-22) - Duration: 22 Feb 2022 → 1 Mar 2022 |
Conference
Conference | 36th AAAI Conference on Artificial Intelligence (AAAI-22) |
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Period | 22/02/22 → 1/03/22 |
NREL Publication Number
- NREL/CP-2C00-81333
Keywords
- blade representation
- data-driven deformations
- Grassmannian
- Principal Geodesic Analysis
- shape representation