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 …
An interpretable framework of data-driven turbulence modeling using deep neural networks
Reynolds-averaged Navier–Stokes simulations represent a cost-effective option for practical
engineering applications, but are facing ever-growing demands for more accurate …
engineering applications, but are facing ever-growing demands for more accurate …
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 …
Feature selection and processing of turbulence modeling based on an artificial neural network
Data-driven turbulence modeling has been considered an effective method for improving the
prediction accuracy of Reynolds-averaged Navier–Stokes equations. Related studies aimed …
prediction accuracy of Reynolds-averaged Navier–Stokes equations. Related studies aimed …
Flows over periodic hills of parameterized geometries: A dataset for data-driven turbulence modeling from direct simulations
Computational fluid dynamics models based on Reynolds-averaged Navier–Stokes
equations with turbulence closures still play important roles in engineering design and …
equations with turbulence closures still play important roles in engineering design and …
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 …
Unsteady flow prediction from sparse measurements by compressed sensing reduced order modeling
Prediction of complex fluid flows from sparse and noisy sensor measurements is widely
applied to many engineering fields. In the present study, a novel compressed sensing …
applied to many engineering fields. In the present study, a novel compressed sensing …
[HTML][HTML] Xcompact3D: An open-source framework for solving turbulence problems on a Cartesian mesh
Xcompact3D is a Fortran 90–95 open-source framework designed for fast and accurate
simulations of turbulent flows, targeting CPU-based supercomputers. It is an evolution of the …
simulations of turbulent flows, targeting CPU-based supercomputers. It is an evolution of the …