Turbulent scalar flux in inclined jets in crossflow: counter gradient transport and deep learning modelling

DOI10.1017/jfm.2020.820zbMath1461.76236arXiv2001.04600OpenAlexW3000120686MaRDI QIDQ5144535

John K. Eaton, Julia Ling, Pedro M. Milani

Publication date: 18 January 2021

Published in: Journal of Fluid Mechanics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2001.04600

zbMATH Keywords

jetsturbulence modellingturbulent mixing

Mathematics Subject Classification ID

Direct numerical and large eddy simulation of turbulence (76F65) Turbulent transport, mixing (76F25)

Related Items (4)

Multi-objective CFD-driven development of coupled turbulence closure models ⋮ The anisotropic graph neural network model with multiscale and nonlinear characteristic for turbulence simulation ⋮ Determining \textit{a priori} a RANS model's applicable range via global epistemic uncertainty quantification ⋮ Data-driven RANS closures for wind turbine wakes under neutral conditions

Uses Software

• TensorFlow
• Adam

Cites Work

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• Mixing in circular and non-circular jets in crossflow
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• Direct numerical simulation of passive scalar transport in transverse jets
• An eddy-viscosity subgrid-scale model for turbulent shear flow: Algebraic theory and applications
• A more general effective-viscosity hypothesis
• Large-eddy simulations of a round jet in crossflow
• Simultaneous measurements of scalar and velocity field evolution in turbulent crossflowing jets
• The Interaction of Jets with Crossflow
• Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned
• Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
• Turbulence Modeling in the Age of Data
• Sub-grid scale model classification and blending through deep learning
• A rational model for the turbulent scalar fluxes

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