A review of physics-informed machine learning in fluid mechanics
Physics-informed machine-learning (PIML) enables the integration of domain knowledge
with machine learning (ML) algorithms, which results in higher data efficiency and more …
with machine learning (ML) algorithms, which results in higher data efficiency and more …
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 …
A unified method of data assimilation and turbulence modeling for separated flows at high Reynolds numbers
Z Wang, W Zhang - Physics of Fluids, 2023 - pubs.aip.org
In recent years, machine learning methods represented by deep neural networks (DNNs)
have been a new paradigm of turbulence modeling. However, in the scenario of high …
have been a new paradigm of turbulence modeling. However, in the scenario of high …
Differentiable turbulence ii
V Shankar, R Maulik, V Viswanathan - arXiv preprint arXiv:2307.13533, 2023 - arxiv.org
Differentiable fluid simulators are increasingly demonstrating value as useful tools for
developing data-driven models in computational fluid dynamics (CFD). Differentiable …
developing data-driven models in computational fluid dynamics (CFD). Differentiable …
SAM-ML: Integrating data-driven closure with nuclear system code SAM for improved modeling capability
Advanced reactors often involve complicated thermal-fluid (TF) phenomena. Modeling such
phenomena with the traditional one-dimensional (1-D) system code is a challenging task …
phenomena with the traditional one-dimensional (1-D) system code is a challenging task …
Data-driven modeling of coarse mesh turbulence for reactor transient analysis using convolutional recurrent neural networks
Advanced nuclear reactors often exhibit complex thermal-fluid phenomena during
transients. To accurately capture such phenomena, a coarse-mesh three-dimensional (3-D) …
transients. To accurately capture such phenomena, a coarse-mesh three-dimensional (3-D) …
Development of turbulent heat flux model for unsteady forced convective heat transfer of small-to-medium Prandtl-number fluids based on deep learning
LX Chen, C Yuan, HN Zhang, XB Li, Y Ma… - International Journal of …, 2022 - Elsevier
Turbulent heat flux (THF) models are used for the closure of the THF term when solving the
steady/unsteady Reynolds-averaged scalar transport equation to simulate the turbulent heat …
steady/unsteady Reynolds-averaged scalar transport equation to simulate the turbulent heat …
Data-Driven RANS Turbulence Closures for Forced Convection Flow in Reactor Downcomer Geometry
Recent progress in data-driven turbulence modeling has shown its potential to enhance or
replace traditional equation-based Reynolds-averaged Navier-Stokes (RANS) turbulence …
replace traditional equation-based Reynolds-averaged Navier-Stokes (RANS) turbulence …
Turbulence modeling for compressible flows using discrepancy tensor-basis neural networks and extrapolation detection
View Video Presentation: https://doi. org/10.2514/6.2023-2126. vid The Reynolds-averaged
Navier–Stokes (RANS) equations remain a workhorse technology for simulating …
Navier–Stokes (RANS) equations remain a workhorse technology for simulating …
A Perspective on Data-Driven Coarse Grid Modeling for System-Level Thermal Hydraulics
In the future, advanced reactors are expected to play an important role in nuclear power.
However, their development and deployment are hindered by the absence of reliable and …
However, their development and deployment are hindered by the absence of reliable and …