Turbulence from an observer perspective
TA Zaki - Annual Review of Fluid Mechanics, 2024 - annualreviews.org
Turbulence is often studied by tracking its spatiotemporal evolution and analyzing the
dynamics of its different scales. The dual to this perspective is that of an observer who starts …
dynamics of its different scales. The dual to this perspective is that of an observer who starts …
DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks
Electroconvection is a multiphysics problem involving coupling of the flow field with the
electric field as well as the cation and anion concentration fields. Here, we use …
electric field as well as the cation and anion concentration fields. Here, we use …
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 …
Reconstructing turbulent velocity and pressure fields from under-resolved noisy particle tracks using physics-informed neural networks
Volume-resolving imaging techniques are rapidly advancing progress in experimental fluid
mechanics. However, reconstructing the full and structured Eulerian velocity and pressure …
mechanics. However, reconstructing the full and structured Eulerian velocity and pressure …
Instability-wave prediction in hypersonic boundary layers with physics-informed neural operators
Fast and accurate prediction of the nonlinear evolution of instability waves in high-speed
boundary layers requires specialized numerical algorithms, and augmenting limited …
boundary layers requires specialized numerical algorithms, and augmenting limited …
State estimation in minimal turbulent channel flow: A comparative study of 4DVar and PINN
The state of turbulent, minimal-channel flow is estimated from spatio-temporal sparse
observations of the velocity, using both a physics-informed neural network (PINN) and …
observations of the velocity, using both a physics-informed neural network (PINN) and …
Ensemble-variational assimilation of statistical data in large-eddy simulation
A nonintrusive data assimilation methodology is developed to improve the statistical
predictions of large-eddy simulations (LES). The ensemble-variational (EnVar) approach …
predictions of large-eddy simulations (LES). The ensemble-variational (EnVar) approach …
Unsteady flow enhancement on an airfoil using sliding window weak-constraint four-dimensional variational data assimilation
S Li, C He, Y Liu - Physics of Fluids, 2023 - pubs.aip.org
This study establishes a continuous sliding window weak-constraint four-dimensional
variational approach for reproducing a complete instantaneous flow from sparse …
variational approach for reproducing a complete instantaneous flow from sparse …
Multi-scale reconstruction of turbulent rotating flows with generative diffusion models
We address the problem of data augmentation in a rotating turbulence set-up, a
paradigmatic challenge in geophysical applications. The goal is to reconstruct information in …
paradigmatic challenge in geophysical applications. The goal is to reconstruct information in …
Linear-model-based estimation in wall turbulence: improved stochastic forcing and eddy viscosity terms
We use Navier–Stokes-based linear models for wall-bounded turbulent flows to estimate
large-scale fluctuations at different wall-normal locations from their measurements at a …
large-scale fluctuations at different wall-normal locations from their measurements at a …