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 …
[HTML][HTML] Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
High-resolution (HR) information of fluid flows, although preferable, is usually less
accessible due to limited computational or experimental resources. In many cases, fluid data …
accessible due to limited computational or experimental resources. In many cases, fluid data …
Neural operator prediction of linear instability waves in high-speed boundary layers
We investigate if neural operators can predict the linear evolution of instability waves in high-
speed boundary layers. To this end, we extend the design of the DeepOnet to ensure …
speed boundary layers. To this end, we extend the design of the DeepOnet to ensure …
Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low dimensionalization
We focus on a convolutional neural network (CNN), which has recently been utilized for fluid
flow analyses, from the perspective on the influence of various operations inside it by …
flow analyses, from the perspective on the influence of various operations inside it by …
[HTML][HTML] 智能空气动力学若干研究进展及展望
唐志共， 朱林阳， 向星皓， 何磊， 赵暾， 王岳青… - 空气动力学 …, 2023 - pubs.cstam.org.cn
智能空气动力学是人工智能与空气动力学的结合, 融入了第四研究范式(数据驱动)
的独特研究方法, 已逐步发展成为一门独立的交叉学科. 本文首先对智能空气动力学的概念和 …
的独特研究方法, 已逐步发展成为一门独立的交叉学科. 本文首先对智能空气动力学的概念和 …
Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression
We use Gaussian stochastic weight averaging (SWAG) to assess the epistemic uncertainty
associated with neural-network-based function approximation relevant to fluid flows. SWAG …
associated with neural-network-based function approximation relevant to fluid flows. SWAG …
Transition modeling for low pressure turbines using computational fluid dynamics driven machine learning
Existing Reynolds Averaged Navier–Stokes-based transition models do not accurately
predict separation induced transition for low pressure turbines. Therefore, in this paper, a …
predict separation induced transition for low pressure turbines. Therefore, in this paper, a …
Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids
Constitutive models are widely used for modeling complex systems in science and
engineering, where first-principle-based, well-resolved simulations are often prohibitively …
engineering, where first-principle-based, well-resolved simulations are often prohibitively …
Recurrent neural network for end-to-end modeling of laminar-turbulent transition
Accurate prediction of laminar-turbulent transition is a critical element of computational fluid
dynamics simulations for aerodynamic design across multiple flow regimes. Traditional …
dynamics simulations for aerodynamic design across multiple flow regimes. Traditional …
Assessment of RANS-based transition models based on experimental data of the common research model with natural laminar flow
BS Venkatachari, P Paredes, JM Derlaga… - AIAA Scitech 2021 …, 2021 - arc.aiaa.org
View Video Presentation: https://doi. org/10.2514/6.2021-1430. vid Transition models based
on auxiliary transport equations augmenting the Reynolds-averaged Navier-Stokes (RANS) …
on auxiliary transport equations augmenting the Reynolds-averaged Navier-Stokes (RANS) …