Related references
Note: Only part of the references are listed.Multi-objective CFD-driven development of coupled turbulence closure models
Fabian Waschkowski et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2022)
Generalization enhancement of artificial neural network for turbulence closure by feature selection
Linyang Zhu et al.
ADVANCES IN AERODYNAMICS (2022)
Sparse identification of multiphase turbulence closures for coupled fluid-particle flows
S. Beetham et al.
JOURNAL OF FLUID MECHANICS (2021)
Transition Modeling for Low Pressure Turbines Using Computational Fluid Dynamics Driven Machine Learning
Harshal D. Akolekar et al.
ENERGIES (2021)
Integration of Machine Learning and Computational Fluid Dynamics to Develop Turbulence Models for Improved Low-Pressure Turbine Wake Mixing Prediction
Harshal D. Akolekar et al.
JOURNAL OF TURBOMACHINERY-TRANSACTIONS OF THE ASME (2021)
An interpretable framework of data-driven turbulence modeling using deep neural networks
Chao Jiang et al.
PHYSICS OF FLUIDS (2021)
Machine Learning for Fluid Mechanics
Steven L. Brunton et al.
ANNUAL REVIEW OF FLUID MECHANICS, VOL 52 (2020)
Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression
Martin Schmelzer et al.
FLOW TURBULENCE AND COMBUSTION (2020)
RANS turbulence model development using CFD-driven machine learning
Yaomin Zhao et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2020)
Turbulence Modeling in the Age of Data
Karthik Duraisamy et al.
ANNUAL REVIEW OF FLUID MECHANICS, VOL 51 (2019)
A framework to develop data-driven turbulence models for flows with organised unsteadiness
Chitrarth Lav et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2019)
Application of deep learning method to Reynolds stress models of channel flow based on reduced-order modeling of DNS data
Zhen Zhang et al.
JOURNAL OF HYDRODYNAMICS (2019)
Development and Use of Machine-Learnt Algebraic Reynolds Stress Models for Enhanced Prediction of Wake Mixing in Low-Pressure Turbines
H. D. Akolekar et al.
JOURNAL OF TURBOMACHINERY-TRANSACTIONS OF THE ASME (2019)
Prediction of Reynolds stresses in high-Mach-number turbulent boundary layers using physics-informed machine learning
Jian-Xun Wang et al.
THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS (2019)
Some Recent Developments in Turbulence Closure Modeling
Paul A. Durbin
ANNUAL REVIEW OF FLUID MECHANICS, VOL 50 (2018)
Machine learning strategies for systems with invariance properties
Julia Ling 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)
Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty
J. Ling et al.
PHYSICS OF FLUIDS (2015)
On the logarithmic region in wall turbulence
Ivan Marusic et al.
JOURNAL OF FLUID MECHANICS (2013)
Neural network modeling for near wall turbulent flow
M Milano et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2002)
Active flow separation control on wall-mounted hump at high Reynolds numbers
A Seifert et al.
AIAA JOURNAL (2002)