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- W4205237002 abstract "This paper presents a model reduction approach based on interpolation of linear systems that govern a reduced state approximation. These systems are constructed at training points, from state and residual perturbation data, in an offline stage. In the online stage, the matrix systems of equations are interpolated to a desired point in parameter space, allowing for a rapid solution of the modal state coefficients. The method is similar to traditional interpolation methods, in that quantities are interpolated in the parameter domain, but it extends the interpolated quantities beyond just outputs or state coefficients. It is also similar to projection-based methods, in that the reduced systems are obtained from a projection step using a suitably-chosen test space, but it does not require a direct link back to the full-order code for efficient online implementation. The intended application is the generation of reduced models for external aerodynamics simulations of parametrized shapes, and results are presented for prototypical two-dimensional flows with modeled turbulence. The results indicate that, when the parameter space sampling is sparse, projection-based methods offer improved solution fidelity over interpolation methods. Furthermore, the interpolated systems method exhibits results similar to projection, at a much lower implementation and run-time complexity." @default.
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- W4205237002 date "2022-01-03" @default.
- W4205237002 modified "2023-09-27" @default.
- W4205237002 title "Model Reduction Using Interpolated Systems of Equations" @default.
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- W4205237002 doi "https://doi.org/10.2514/6.2022-2323" @default.
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