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- W4317583837 abstract "Sensitivity analysis for computational fluid dynamics (CFD) simulations is a complicated procedure, which still relies, in many cases, on engineering judgment and factors of safety. This is in part because the computational cost of quantifying the simulation’s sensitivity to all meaningful parameters (e.g., body surface roughness) and hyperparameters (e.g., subiteration convergence criterion) is intractable for even a single simulation. Reduced-order modeling dramatically lowers this computational cost of simulating fluid flows, but usually only where similar data are already available. In this work, fluid reduced-order models are utilized to quantify flow sensitivity to certain physical parameters for the purposes of improved sensitivity analysis. Characteristic observability and sensitivity are both explored. The sensitivity results enable more informed CFD frameworks and more rigorous uncertainty bounds on the resulting data." @default.
- W4317583837 created "2023-01-21" @default.
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- W4317583837 date "2023-01-19" @default.
- W4317583837 modified "2023-09-27" @default.
- W4317583837 title "Low-cost Quantification of Fluid Flow Parameter Sensitivity using Reduced-order Modeling" @default.
- W4317583837 doi "https://doi.org/10.2514/6.2023-1378" @default.
- W4317583837 hasPublicationYear "2023" @default.
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