4.5 Article

An iterative machine-learning framework for RANS turbulence modeling

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。
Article Computer Science, Interdisciplinary Applications

RANS turbulence model development using CFD-driven machine learning

Yaomin Zhao et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2020)

Review Mechanics

Turbulence Modeling in the Age of Data

Karthik Duraisamy et al.

ANNUAL REVIEW OF FLUID MECHANICS, VOL 51 (2019)

Article Thermodynamics

Towards a general data-driven explicit algebraic Reynolds stress prediction framework

Corrado Sotgiu et al.

INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW (2019)

Article Engineering, Mechanical

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)

Review Mechanics

Some Recent Developments in Turbulence Closure Modeling

Paul A. Durbin

ANNUAL REVIEW OF FLUID MECHANICS, VOL 50 (2018)

Article Physics, Fluids & Plasmas

Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework

Jin-Long Wu et al.

PHYSICAL REVIEW FLUIDS (2018)

Article Thermodynamics

The development of algebraic stress models using a novel evolutionary algorithm

J. Weatheritt et al.

INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW (2017)

Article Engineering, Aerospace

Machine-Learning-Augmented Predictive Modeling of Turbulent Separated Flows over Airfoils

Anand Pratap Singh et al.

AIAA JOURNAL (2017)

Article Mechanics

Deep earning in fluid dynamics

J. Nathan Kutz

JOURNAL OF FLUID MECHANICS (2017)

Article Computer Science, Interdisciplinary Applications

A methodology to evaluate statistical errors in DNS data of plane channel flows

Roney L. Thompson et al.

COMPUTERS & FLUIDS (2016)

Article Computer Science, Interdisciplinary Applications

Machine learning strategies for systems with invariance properties

Julia Ling et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2016)

Article Computer Science, Interdisciplinary Applications

A paradigm for data-driven predictive modeling using field inversion and machine learning

Eric J. Parish et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2016)

Article Computer Science, Interdisciplinary Applications

A novel evolutionary algorithm applied to algebraic modifications of the RANS stress-strain relationship

Jack Weatheritt et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2016)

Article Engineering, Aerospace

Formulation of the k-ω Turbulence Model Revisited

David C. Wilcox

AIAA JOURNAL (2008)

Article Engineering, Aerospace

Turbulence models assessment for large-scale tip vortices in an axial compressor rotor

Yangwei Liu et al.

JOURNAL OF PROPULSION AND POWER (2008)

Review Engineering, Aerospace

Review and assessment of turbulence models for hypersonic flows

Christopher J. Roy et al.

PROGRESS IN AEROSPACE SCIENCES (2006)

Article Thermodynamics

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)

Article Engineering, Mechanical

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)

Article Engineering, Electrical & Electronic

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)