Quantification of model uncertainty in RANS simulations: A review
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 …
[HTML][HTML] Impact of turbulence models and roughness height in 3D steady RANS simulations of wind flow in an urban environment
The accuracy and reliability of 3D steady RANS CFD simulations of wind flow in urban
environments can be affected by numerical settings including the turbulence model and the …
environments can be affected by numerical settings including the turbulence model and the …
Recent advances and effectiveness of machine learning models for fluid dynamics in the built environment
Indoor environmental quality is crucial for human health and comfort, necessitating precise
and efficient computational methods to optimise indoor climate parameters. Recent …
and efficient computational methods to optimise indoor climate parameters. Recent …
Conditioning and accurate solutions of Reynolds average Navier–Stokes equations with data-driven turbulence closures
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 …
Bayesian uncertainty analysis for underwater 3D reconstruction with neural radiance fields
Neural radiance fields (NeRFs) are a deep learning technique that generates novel views of
3D scenes from multi-view images. As an extension of NeRFs, SeaThru-NeRF mitigates the …
3D scenes from multi-view images. As an extension of NeRFs, SeaThru-NeRF mitigates the …
Data-driven Reynolds-averaged turbulence modeling with generalizable non-linear correction and uncertainty quantification using Bayesian deep learning
H Tang, Y Wang, T Wang, L Tian, Y Qian - Physics of Fluids, 2023 - pubs.aip.org
The past few years have witnessed a renewed blossoming of data-driven turbulence
models. Quantification of the concomitant modeling uncertainty, however, has mostly been …
models. Quantification of the concomitant modeling uncertainty, however, has mostly been …
Sparse Bayesian learning of explicit algebraic Reynolds-stress models for turbulent separated flows
Abstract A novel Sparse Bayesian Learning (SBL) framework is introduced for generating
stochastic Explicit Algebraic Reynolds Stress (EARSM) closures for the Reynolds-Averaged …
stochastic Explicit Algebraic Reynolds Stress (EARSM) closures for the Reynolds-Averaged …
Space-dependent Aggregation of Stochastic Data-driven Turbulence Models
A stochastic machine-learning framework is developed to enhance Reynolds-Averaged
Navier-Stokes (RANS) predictions of turbulent flows while quantifying model uncertainty …
Navier-Stokes (RANS) predictions of turbulent flows while quantifying model uncertainty …
Regularized ensemble Kalman methods for inverse problems
Inverse problems are common and important in many applications in computational physics
but are inherently ill-posed with many possible model parameters resulting in satisfactory …
but are inherently ill-posed with many possible model parameters resulting in satisfactory …
Bayesian model evaluation of three k–ω turbulence models for hypersonic shock wave–boundary layer interaction flows
J Li, F Zeng, S Chen, K Zhang, C Yan - Acta Astronautica, 2021 - Elsevier
Shock wave–boundary layer interaction (SWBLI) is one of the most prevalent challenges in
the field of fluid mechanics. Excessive interaction may lead to strong flow separation, which …
the field of fluid mechanics. Excessive interaction may lead to strong flow separation, which …