Year: 2021
Type: article
Abstract: The weights of a deep neural-network model are optimized over the governing flow equations to provide a model for the subgrid-scale stresses in a turbulent plane jet. The training, which is done in $a\phantom{\rule{0}{0ex}}p\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}s\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}e\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}i$ large-eddy simulations (LES), solves the adjoint Navier-Stokes equations to provide end-to-end sensitivities. Out-of-sample testing on multiple dual-jet configurations confirms that the required grid resolution is half that needed by dynamic models for comparable accuracy. The coupled formulation is generalized to train based only on the mean flow and Reynolds stresses, which are more readily available from experiments.
Source: Physical Review Fluids
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Awards: G1326603436, Leadership Class Scientific and Engineering Computing: Breaking Through the Limits, G277867621, G2952167421, G4810716312, Sustained-Petascale In Action: Blue Waters Enabling Transformative Science And Engineering, G5127309730, G5367696895, G5606180237, CENTER FOR EXASCALE SIMULATION OF PLASMA-COUPLED COMBUSTION MULTI-DISCIPLINARY SIMULATION CENTER (MSC), G8324313409
OpenAlex ID: W3158764724