[HTML][HTML] Machine learning in aerodynamic shape optimization
Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …
optimization (ASO), thanks to the availability of aerodynamic data and continued …
[HTML][HTML] Deep learning to replace, improve, or aid CFD analysis in built environment applications: A review
G Calzolari, W Liu - Building and Environment, 2021 - Elsevier
Fast and accurate airflow simulations in the built environment are critical to provide
acceptable thermal comfort and air quality to the occupants. Computational Fluid Dynamics …
acceptable thermal comfort and air quality to the occupants. Computational Fluid Dynamics …
Review of machine learning for hydrodynamics, transport, and reactions in multiphase flows and reactors
Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
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 …
Solver-in-the-loop: Learning from differentiable physics to interact with iterative pde-solvers
Finding accurate solutions to partial differential equations (PDEs) is a crucial task in all
scientific and engineering disciplines. It has recently been shown that machine learning …
scientific and engineering disciplines. It has recently been shown that machine learning …
Deep learning methods for Reynolds-averaged Navier–Stokes simulations of airfoil flows
This study investigates the accuracy of deep learning models for the inference of Reynolds-
averaged Navier–Stokes (RANS) solutions. This study focuses on a modernized U-net …
averaged Navier–Stokes (RANS) solutions. This study focuses on a modernized U-net …
Deep learning methods for super-resolution reconstruction of turbulent flows
B Liu, J Tang, H Huang, XY Lu - Physics of Fluids, 2020 - pubs.aip.org
Two deep learning (DL) models addressing the super-resolution (SR) reconstruction of
turbulent flows from low-resolution coarse flow field data are developed. One is the static …
turbulent flows from low-resolution coarse flow field data are developed. One is the static …
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 …
Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework
Abstract Reynolds-averaged Navier-Stokes (RANS) equations are widely used in
engineering turbulent flow simulations. However, RANS predictions may have large …
engineering turbulent flow simulations. However, RANS predictions may have large …
Machine learning methods for turbulence modeling in subsonic flows around airfoils
In recent years, the data-driven turbulence model has attracted widespread concern in fluid
mechanics. The existing approaches modify or supplement the original turbulence model by …
mechanics. The existing approaches modify or supplement the original turbulence model by …