相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。RANS turbulence model development using CFD-driven machine learning
Yaomin Zhao et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2020)
Feature selection and processing of turbulence modeling based on an artificial neural network
Yuhui Yin et al.
PHYSICS OF FLUIDS (2020)
Turbulence Modeling in the Age of Data
Karthik Duraisamy et al.
ANNUAL REVIEW OF FLUID MECHANICS, VOL 51 (2019)
Reynolds-averaged Navier-Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned
Jinlong Wu et al.
JOURNAL OF FLUID MECHANICS (2019)
Towards a general data-driven explicit algebraic Reynolds stress prediction framework
Corrado Sotgiu et al.
INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW (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)
Machine learning methods for turbulence modeling in subsonic flows around airfoils
Linyang Zhu et al.
PHYSICS OF FLUIDS (2019)
Some Recent Developments in Turbulence Closure Modeling
Paul A. Durbin
ANNUAL REVIEW OF FLUID MECHANICS, VOL 50 (2018)
Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework
Jin-Long Wu et al.
PHYSICAL REVIEW FLUIDS (2018)
The development of algebraic stress models using a novel evolutionary algorithm
J. Weatheritt et al.
INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW (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)
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)
Machine learning strategies for systems with invariance properties
Julia Ling et al.
JOURNAL OF COMPUTATIONAL PHYSICS (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)
Formulation of the k-ω Turbulence Model Revisited
David C. Wilcox
AIAA JOURNAL (2008)
Turbulence models assessment for large-scale tip vortices in an axial compressor rotor
Yangwei Liu et al.
JOURNAL OF PROPULSION AND POWER (2008)
Review and assessment of turbulence models for hypersonic flows
Christopher J. Roy et al.
PROGRESS IN AEROSPACE SCIENCES (2006)
Surface heat-flux fluctuations in a turbulent channel flow up to Reτ=1020 with Pr=0.025 and 0.71
H Abe et al.
INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW (2004)
Direct numerical simulation of a fully developed turbulent channel flow with respect to the Reynolds number dependence
H Abe et al.
JOURNAL OF FLUIDS ENGINEERING-TRANSACTIONS OF THE ASME (2001)
Approximation of inverse system (many-valued function) by neural network
M Shinki et al.
ELECTRONICS AND COMMUNICATIONS IN JAPAN PART III-FUNDAMENTAL ELECTRONIC SCIENCE (2001)
An explicit algebraic Reynolds stress model for incompressible and compressible turbulent flows
S Wallin et al.
JOURNAL OF FLUID MECHANICS (2000)