Related references
Note: Only part of the references are listed.Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks
Nicholas Geneva et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2019)
Subgrid modelling for two-dimensional turbulence using neural networks
R. Maulik et al.
JOURNAL OF FLUID MECHANICS (2019)
Machine learning methods for turbulence modeling in subsonic flows around airfoils
Linyang Zhu et al.
PHYSICS OF FLUIDS (2019)
Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework
Jin-Long Wu et al.
PHYSICAL REVIEW FLUIDS (2018)
Subgrid-scale scalar flux modelling based on optimal estimation theory and machine-learning procedures
A. Vollant et al.
JOURNAL OF TURBULENCE (2017)
Searching for turbulence models by artificial neural network
Masataka Gamahara et al.
PHYSICAL REVIEW FLUIDS (2017)
Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data
Jian-Xun Wang et al.
PHYSICAL REVIEW FLUIDS (2017)
A neural network approach for the blind deconvolution of turbulent flows
R. Maulik et al.
JOURNAL OF FLUID MECHANICS (2017)
Improved RANS Computations of Flow over the 25°-Slant-Angle Ahmed Body
Robert Maduta et al.
SAE INTERNATIONAL JOURNAL OF PASSENGER CARS-MECHANICAL SYSTEMS (2017)
Machine-Learning-Augmented Predictive Modeling of Turbulent Separated Flows over Airfoils
Anand Pratap Singh et al.
AIAA JOURNAL (2017)
A methodology to evaluate statistical errors in DNS data of plane channel flows
Roney L. Thompson et al.
COMPUTERS & FLUIDS (2016)
A paradigm for data-driven predictive modeling using field inversion and machine learning
Eric J. Parish et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2016)
A novel evolutionary algorithm applied to algebraic modifications of the RANS stress-strain relationship
Jack Weatheritt et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2016)
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
Julia Ling et al.
JOURNAL OF FLUID MECHANICS (2016)
Using field inversion to quantify functional errors in turbulence closures
Anand Pratap Singh et al.
PHYSICS OF FLUIDS (2016)
On the accuracy of RANS simulations with DNS data
Svetlana V. Poroseva et al.
PHYSICS OF FLUIDS (2016)
Direct numerical simulation of turbulent channel flow up to Reτ ≈ 5200
Myoungkyu Lee et al.
JOURNAL OF FLUID MECHANICS (2015)
Modeling of Non-Equilibrium Homogeneous Turbulence in Rapidly Compressed Flows
Peter E. Hamlington et al.
FLOW TURBULENCE AND COMBUSTION (2014)
Velocity statistics in turbulent channel flow up to Reτ=4000
Matteo Bernardini et al.
JOURNAL OF FLUID MECHANICS (2014)
Incompact3d: A powerful tool to tackle turbulence problems with up to O(105) computational cores
Sylvain Laizet et al.
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS (2011)
Reynolds number dependence of mean flow structure in square duct turbulence
Alfredo Pinelli et al.
JOURNAL OF FLUID MECHANICS (2010)
Flow over periodic hills - Numerical and experimental study in a wide range of Reynolds numbers
M. Breuer et al.
COMPUTERS & FLUIDS (2009)
High-order compact schemes for incompressible flows: A simple and efficient method with quasi-spectral accuracy
Sylvain Laizet et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2009)
Reynolds stress closure for nonequilibrium effects in turbulent flows
Peter E. Hamlington et al.
PHYSICS OF FLUIDS (2008)
Spectra of the very large anisotropic scales in turbulent channels
JC del Alamo et al.
PHYSICS OF FLUIDS (2003)
A new hybrid turbulence modelling strategy for industrial CFD
B Basara et al.
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS (2003)
Share Paper
