The transformative potential of machine learning for experiments in fluid mechanics
The field of machine learning (ML) has rapidly advanced the state of the art in many fields of
science and engineering, including experimental fluid dynamics, which is one of the original …
science and engineering, including experimental fluid dynamics, which is one of the original …
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 …
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 …
Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements
Different types of neural networks have been used to solve the flow sensing problem in
turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements …
turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements …
Characterizing small-scale dynamics of navier-stokes turbulence with transverse lyapunov exponents: A data assimilation approach
Data assimilation (DA) of turbulence, which involves reconstructing small-scale turbulent
structures based on observational data from large-scale ones, is crucial not only for practical …
structures based on observational data from large-scale ones, is crucial not only for practical …
Identifying causally significant features in three-dimensional isotropic turbulence
MP Encinar, J Jiménez - Journal of Fluid Mechanics, 2023 - cambridge.org
Flow patterns of causal significance to three-dimensional isotropic turbulence are identified
through the recently introduced algorithm of Jiménez (J. Fluid Mech., vol. 854, 2018, R1) …
through the recently introduced algorithm of Jiménez (J. Fluid Mech., vol. 854, 2018, R1) …
[HTML][HTML] Synchronizing large eddy simulations with direct numerical simulations via data assimilation
J Li, M Tian, Y Li - Physics of Fluids, 2022 - pubs.aip.org
The synchronization of large eddy simulations to direct numerical simulations via a data
assimilation scheme is investigated in Kolmogorov flows, where the large scales of the …
assimilation scheme is investigated in Kolmogorov flows, where the large scales of the …
[HTML][HTML] Chaotic measures as an alternative to spectral measures for analysing turbulent flow
Turbulence has associated chaotic features. In the past couple of decades, there has been
growing interest in the study of these features as an alternative means of understanding …
growing interest in the study of these features as an alternative means of understanding …