[HTML][HTML] Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty
J Ling, J Templeton - Physics of Fluids, 2015 - pubs.aip.org
Reynolds Averaged Navier Stokes (RANS) models are widely used in industry to predict
fluid flows, despite their acknowledged deficiencies. Not only do RANS models often …
fluid flows, despite their acknowledged deficiencies. Not only do RANS models often …
Comparison of different data-assimilation approaches to augment RANS turbulence models
Abstract Reynolds-averaged Navier–Stokes (RANS) simulations are the most widespread
approach to predict turbulent flows typical of industrial problems. Despite its success, the …
approach to predict turbulent flows typical of industrial problems. Despite its success, the …
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 …
Towards integrated field inversion and machine learning with embedded neural networks for RANS modeling
JR Holland, JD Baeder, K Duraisamy - AIAA Scitech 2019 forum, 2019 - arc.aiaa.org
R averaged Navier-Stokes (RANS) model deficiencies have been well documented in a
wide variety of common applications. Additionally, despite advancements in computing …
wide variety of common applications. Additionally, despite advancements in computing …
A priori assessment of prediction confidence for data-driven turbulence modeling
Abstract Although Reynolds-Averaged Navier–Stokes (RANS) equations are still the
dominant tool for engineering design and analysis applications involving turbulent flows …
dominant tool for engineering design and analysis applications involving turbulent flows …
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 …
Recommendations for future efforts in RANS modeling and simulation
RH Bush, TS Chyczewski, K Duraisamy… - AIAA scitech 2019 …, 2019 - arc.aiaa.org
The roadmap laid out in the CFD Vision 2030 document [1] suggests that a decision to move
away from RANS research needs to be made in the current timeframe (around 2020). This …
away from RANS research needs to be made in the current timeframe (around 2020). This …
[HTML][HTML] Data-driven modelling of the Reynolds stress tensor using random forests with invariance
MLA Kaandorp, RP Dwight - Computers & Fluids, 2020 - Elsevier
A novel machine learning algorithm is presented, serving as a data-driven turbulence
modeling tool for Reynolds Averaged Navier-Stokes (RANS) simulations. This machine …
modeling tool for Reynolds Averaged Navier-Stokes (RANS) simulations. This machine …
Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned
Reynolds-averaged Navier–Stokes (RANS) simulations with turbulence closure models
continue to play important roles in industrial flow simulations. However, the commonly used …
continue to play important roles in industrial flow simulations. However, the commonly used …