Turbulence modeling in the age of data
Data from experiments and direct simulations of turbulence have historically been used to
calibrate simple engineering models such as those based on the Reynolds-averaged Navier …
calibrate simple engineering models such as those based on the Reynolds-averaged Navier …
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 …
Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework
Reynolds-averaged Navier-Stokes (RANS) equations are widely used in engineering
turbulent flow simulations. However, RANS predictions may have large discrepancies due to …
turbulent flow simulations. However, RANS predictions may have large discrepancies due to …
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 …
Predictive large-eddy-simulation wall modeling via physics-informed neural networks
While data-based approaches were found to be useful for subgrid scale (SGS) modeling in
Reynolds-averaged Navier-Stokes (RANS) simulations, there have not been many attempts …
Reynolds-averaged Navier-Stokes (RANS) simulations, there have not been many attempts …
Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach
Despite their well-known limitations, Reynolds-Averaged Navier–Stokes (RANS) models are
still the workhorse tools for turbulent flow simulations in today's engineering analysis, design …
still the workhorse tools for turbulent flow simulations in today's engineering analysis, design …
Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network
An artificial neural network (ANN) is used to establish the relation between the resolved-
scale flow field and the subgrid-scale (SGS) stress tensor, to develop a new SGS model for …
scale flow field and the subgrid-scale (SGS) stress tensor, to develop a new SGS model for …
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 …
Machine-learning for turbulence and heat-flux model development: A review of challenges associated with distinct physical phenomena and progress to date
RD Sandberg, Y Zhao - International Journal of Heat and Fluid Flow, 2022 - Elsevier
This review paper surveys some of the progress made to date in the use of machine learning
(ML) for turbulence and heat transfer modeling. We start by identifying the challenges that …
(ML) for turbulence and heat transfer modeling. We start by identifying the challenges that …
A survey of Bayesian calibration and physics-informed neural networks in scientific modeling
FAC Viana, AK Subramaniyan - Archives of Computational Methods in …, 2021 - Springer
Computer simulations are used to model of complex physical systems. Often, these models
represent the solutions (or at least approximations) to partial differential equations that are …
represent the solutions (or at least approximations) to partial differential equations that are …