This paper focuses on the optimal sensor placement (OSP) strategy based on a deep neural
network (DNN) for turbulent flow recovery within the data assimilation framework of the …

A generalized data assimilation model for turbulent flows using the continuous adjoint
formulation is proposed. Within this formulation, the Spalart–Allmaras turbulence model is …

This paper proposes a data-driven turbulence model for predicting the mean flow in
turbulent circular jets over a wide range of Reynolds numbers (Re). The main formulation is …

This study investigated the performance of proper orthogonal decomposition (POD), discrete
Fourier transformation (DFT), time-domain spectral proper orthogonal decomposition (td …

Sequential data assimilation (DA) was performed on three-dimensional flow fields of a
circular jet measured by tomography particle image velocimetry (tomo-PIV). The work …

This study establishes a continuous sliding window weak-constraint four-dimensional
variational approach for reproducing a complete instantaneous flow from sparse …

A sequential data assimilation (DA) method is developed for pressure determination of
turbulent velocity fields measured by particle image velocimetry (PIV), based on the …

In the present study, a large eddy simulation was conducted to simulate the three-
dimensional cavitation turbulence flow inside a Helmholtz oscillator under a high Reynolds …

The super-temporal-resolution (STR) reconstruction of turbulent flows is an important data
augmentation application for increasing the data reach in measurement techniques and …

This paper is focused on the recovery of the global flow field through data assimilation of
local flow quantity measurement and Reynolds-averaged Navier–Stokes (RANS) modeling …