Quantification of model uncertainty in RANS simulations: A review
H Xiao, P Cinnella - Progress in Aerospace Sciences, 2019 - Elsevier
In computational fluid dynamics simulations of industrial flows, models based on the
Reynolds-averaged Navier–Stokes (RANS) equations are expected to play an important …
Reynolds-averaged Navier–Stokes (RANS) equations are expected to play an important …
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS)
turbulence models that are informed by and can represent a richer set of turbulence physics …
turbulence models that are informed by and can represent a richer set of turbulence physics …
Data-driven modeling and learning in science and engineering
In the past, data in which science and engineering is based, was scarce and frequently
obtained by experiments proposed to verify a given hypothesis. Each experiment was able …
obtained by experiments proposed to verify a given hypothesis. Each experiment was able …
Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data
Turbulence modeling is a critical component in numerical simulations of industrial flows
based on Reynolds-averaged Navier-Stokes (RANS) equations. However, after decades of …
based on Reynolds-averaged Navier-Stokes (RANS) equations. However, after decades of …
Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks
Data-driven methods for improving turbulence modeling in Reynolds-Averaged Navier–
Stokes (RANS) simulations have gained significant interest in the computational fluid …
Stokes (RANS) simulations have gained significant interest in the computational fluid …
The development of algebraic stress models using a novel evolutionary algorithm
J Weatheritt, RD Sandberg - International Journal of Heat and Fluid Flow, 2017 - Elsevier
This work presents developments to a novel evolutionary framework that symbolically
regresses algebraic forms of the Reynolds stress anisotropy tensor. This work contributes to …
regresses algebraic forms of the Reynolds stress anisotropy tensor. This work contributes to …
Prediction of turbulent heat transfer using convolutional neural networks
With the recent rapid development of artificial intelligence (AI) and wide applications in many
areas, some fundamental questions in turbulence research can be addressed, such as:'Can …
areas, some fundamental questions in turbulence research can be addressed, such as:'Can …
基于组合神经网络的雷诺平均湍流模型多次修正方法
张珍， 叶舒然， 岳杰顺， 王一伟， 黄晨光 - 力学学报, 2021 - lxxb.cstam.org.cn
求解雷诺平均(Reynolds-averaged Navier-Stokes, RANS) 方程依然是工程应用中有效且实用
的方法, 但对雷诺应力建模的不确定性会导致该方法的预测精度具有很大差异 …
的方法, 但对雷诺应力建模的不确定性会导致该方法的预测精度具有很大差异 …
Conditioning and accurate solutions of Reynolds average Navier–Stokes equations with data-driven turbulence closures
BP Brener, MA Cruz, RL Thompson… - Journal of Fluid …, 2021 - cambridge.org
The possible ill conditioning of the Reynolds average Navier–Stokes (RANS) equations
when an explicit data-driven Reynolds stress tensor closure is employed is a discussion of …
when an explicit data-driven Reynolds stress tensor closure is employed is a discussion of …
Classification of machine learning frameworks for data-driven thermal fluid models
Thermal fluid processes are inherently multi-physics and multi-scale, involving mass-
momentum-energy transport phenomena at multiple scales. Thermal fluid simulation (TFS) …
momentum-energy transport phenomena at multiple scales. Thermal fluid simulation (TFS) …