Revisiting tensor basis neural network for Reynolds stress modeling: application to plane channel and square duct flows
type: Item
instance of: scholarly article
title: Revisiting tensor basis neural network for Reynolds stress modeling: application to plane channel and square duct flows
author: Jiayi Cai
author: Pierre-Emmanuel Angeli
author: J. M. Martinez
author: Guillaume Damblin
author: Didier Lucor
published in: Computers and Fluids
publication date: 2024-07-04T00:00:00Z
Mathematics Subject Classification ID: 76-XX
zbMATH DE Number: 7875891
zbMATH Keywords: machine learning
zbMATH Keywords: turbulence modeling
zbMATH Keywords: Reynolds-averaged Navier-Stokes
zbMATH Keywords: tensor basis neural network
zbMATH Keywords: plane channel flow
zbMATH Keywords: square duct flow
MaRDI profile type: Publication
cites work: Some Recent Developments in Turbulence Closure Modeling
cites work: The numerical computation of turbulent flows
cites work: Turbulence Modeling in the Age of Data
cites work: Machine Learning for Fluid Mechanics
cites work: Bayesian estimates of parameter variability in the \(k-\varepsilon\) turbulence model
cites work: Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
cites work: A more general effective-viscosity hypothesis
cites work: Data-driven modelling of the Reynolds stress tensor using random forests with invariance
cites work: A novel evolutionary algorithm applied to algebraic modifications of the RANS stress-strain relationship
cites work: RANS turbulence model development using CFD-driven machine learning
cites work: On the Generalizability of Machine-Learning-Assisted Anisotropy Mappings for Predictive Turbulence Modelling
cites work: Direct numerical simulation of turbulent channel flow up to Reτ=590
cites work: Velocity gradient statistics in turbulent shear flow: an extension of Kolmogorov's local equilibrium theory
cites work: Turbulence and secondary motions in square duct flow
cites work: A methodology to evaluate statistical errors in DNS data of plane channel flows
cites work: A perspective on machine learning methods in turbulence modeling
cites work: Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned
cites work: Stable \textit{a posteriori} LES of 2D turbulence using convolutional neural networks: backscattering analysis and generalization to higher \(Re\) via transfer learning
cites work: Théorie de l'écoulement tourbillonnant et tumultueux des liquides dans les lits rectilignes à grande section.
cites work: On nonlinear K-l and K-ε models of turbulence
cites work: Q5270493
cites work: Turbulent Flows
cites work: Q4089994
cites work: Reynolds number dependence of mean flow structure in square duct turbulence
cites work: NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations
cites work: Simple computational strategies for more effective physics-informed neural networks modeling of turbulent natural convection
DOI: 10.1016/J.COMPFLUID.2024.106246
description: scientific article; zbMATH DE number 7875891