Toward neural-network-based large eddy simulation: application to turbulent channel flow
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Publication:5856568
DOI10.1017/jfm.2020.931zbMath1461.76310OpenAlexW3134546754MaRDI QIDQ5856568
Jong Hwan Park, Hae Cheon Choi
Publication date: 26 March 2021
Published in: Journal of Fluid Mechanics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1017/jfm.2020.931
Related Items (6)
Artificial-neural-network-based nonlinear algebraic models for large-eddy simulation of compressible wall-bounded turbulence ⋮ Learned turbulence modelling with differentiable fluid solvers: physics-based loss functions and optimisation horizons ⋮ Ensemble Kalman method for learning turbulence models from indirect observation data ⋮ Comparison of neural closure models for discretised PDEs ⋮ Kinetic-energy-flux-constrained model using an artificial neural network for large-eddy simulation of compressible wall-bounded turbulence ⋮ A physics-inspired alternative to spatial filtering for large-eddy simulations of turbulent flows
Uses Software
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