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 …
Perspectives on machine learning-augmented Reynolds-averaged and large eddy simulation models of turbulence
K Duraisamy - Physical Review Fluids, 2021 - APS
This work presents a review and perspectives on recent developments in the use of machine
learning (ML) to augment Reynolds-averaged Navier-Stokes (RANS) and large eddy …
learning (ML) to augment Reynolds-averaged Navier-Stokes (RANS) and large eddy …
Experimental velocity data estimation for imperfect particle images using machine learning
We propose a method using supervised machine learning to estimate velocity fields from
particle images having missing regions due to experimental limitations. As a first example, a …
particle images having missing regions due to experimental limitations. As a first example, a …
Generalization techniques of neural networks for fluid flow estimation
We demonstrate several techniques to encourage practical uses of neural networks for fluid
flow estimation. In the present paper, three perspectives which are remaining challenges for …
flow estimation. In the present paper, three perspectives which are remaining challenges for …
Equation discovery of dynamized coefficients in the k-ε model for urban airflow and airborne contaminant dispersion
Urban airflow and pollution modeling using steady Reynolds-averaged Navier-Stokes
(SRANS) with two-equation models has faced accuracy challenges, limiting its reliability for …
(SRANS) with two-equation models has faced accuracy challenges, limiting its reliability for …
Multi-objective CFD-driven development of coupled turbulence closure models
This paper introduces two novel concepts in data-driven turbulence modeling that enable
the simultaneous development of multiple closure models and the training towards multiple …
the simultaneous development of multiple closure models and the training towards multiple …
Towards robust and accurate Reynolds-averaged closures for natural convection via multi-objective CFD-driven machine learning
Robust and accurate Reynolds-averaged stresses and scalar fluxes closure models for
natural convection developed by machine learning are presented in this work. In …
natural convection developed by machine learning are presented in this work. In …
Differentiable physics-enabled closure modeling for Burgers' turbulence
Data-driven turbulence modeling is experiencing a surge in interest following algorithmic
and hardware developments in the data sciences. We discuss an approach using the …
and hardware developments in the data sciences. We discuss an approach using the …
Neural network prediction for ice shapes on airfoils using icefoam simulations
S Strijhak, D Ryazanov, K Koshelev, A Ivanov - Aerospace, 2022 - mdpi.com
In this article the procedure and method for the ice accretion prediction for different airfoils
using artificial neural networks (ANNs) are discussed. A dataset for the neural network is …
using artificial neural networks (ANNs) are discussed. A dataset for the neural network is …
Optimization of the semi-sphere vortex generator for film cooling using generative adversarial network
Film cooling has shown great potential in protecting hot section of high-pressure turbine
from melting down. A counter-rotating vortex pair (CVP) is produced downstream of the …
from melting down. A counter-rotating vortex pair (CVP) is produced downstream of the …