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- W4317632368 abstract "This paper presents a method for performing output-based mesh adaptation for large-eddy simulations of turbulence. Instead of an unsteady adjoint, which is expensive to compute and requires non-trivial regularization, the method is based on the field-inversion and machine-learning approach to data-driven turbulence modeling. The data here come not from experiments but from statistics computed from unsteady forward simulations. The resulting trained turbulence models yield steady-state solutions that represent the time-averaged unsteady flow-fields. Adjoints computed from these steady-state models yield the sensitivity information required for an adjoint-weighted residual error estimate and adaptive indicator. Each adaptive iteration then only requires one unsteady primal solution with minimal storage: average statistics and fine-space residual information. Combined with unstructured mesh optimization, the method drives unsteady outputs to accurate values in only a few adaptive iterations. The performance is demonstrated on two-dimensional, low Reynolds number airfoil simulations, and comparisons are made to other techniques, including uniform and residual-based adaptation." @default.
- W4317632368 created "2023-01-21" @default.
- W4317632368 creator A5012374585 @default.
- W4317632368 date "2023-01-19" @default.
- W4317632368 modified "2023-09-27" @default.
- W4317632368 title "Adjoint-Based Adaptation of Large-Eddy Simulations using Dynamic Closures" @default.
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- W4317632368 doi "https://doi.org/10.2514/6.2023-1850" @default.
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