Neural-network-based mixed subgrid-scale model for turbulent flow

نویسندگان

چکیده

An artificial neural-network-based subgrid-scale (SGS) model, which is capable of predicting turbulent flows at untrained Reynolds numbers and on grid resolution developed. Providing the grid-scale strain-rate tensor alone as an input leads model to predict a SGS stress that aligns with tensor, performs similarly dynamic Smagorinsky model. On other hand, providing resolved in addition found significantly improve prediction dissipation, thereby accuracy stability solution. In attempt apply trained for limited range number conditions resolution, special attention given normalisation output tensors. It successful generalization turbulence various possible if distributions normalised inputs outputs neural network remain unchanged vary. posteriori tests forced decaying homogeneous isotropic channel flows, developed neural-network statistics more accurately, maintain numerical without ad hoc stabilisation such clipping excessive backscatter, be computationally efficient than algebraic models.

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ژورنال

عنوان ژورنال: Journal of Fluid Mechanics

سال: 2023

ISSN: ['0022-1120', '1469-7645']

DOI: https://doi.org/10.1017/jfm.2023.260