Active Subspaces of Airfoil Shape Parameterizations
By Zach Grey, Paul G. Constantine. 2017
60 citations recorded by the source corpus.
Design and optimization benefit from understanding the dependence of a quantity of interest (e.g., a design objective or constraint function) on the design variables. A low-dimensional active subspace, when present, identifies important directions in the space of design variables; perturbing a design along the active subspace associated with a particular quantity of interest changes that quantity more, on average, than perturbing the design orthogonally to the active subspace. This low-dimensional structure provides insights that characterize the dependence of quantities of interest on design variables. Airfoil design in a transonic flowfield with a parameterized geometry is a popular test problem for design methodologies. Two particular airfoil shape parameterizations are examined (parametric sections and class-shape transformation), and the active subspaces present in two common design quantities of interest (transonic lift and wave-drag coefficients) are studied under each shape parameterization. The t...
Subfields: Computational Theory and Mathematics, Environmental Engineering, Statistics and Probability
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Citations in the corpus
- Machine Learning in Aerodynamic Shape Optimization (2022, methodology): “[296] T. W. Lukaczyk, P. Constantine, F. Palacios, J. J. Alonso, Active subspaces for shape optimization, in: 10th AIAA Multidisciplinary Design Optimization Conference, American Institute of Aeronautics and Astronautics, 2014. doi:10.2514/6.2014-1171.”