7 citations · 7 across the 5 of their papers we have counts for
6 papers · 1 filter
On the joint estimation of flow fields and particle properties from Lagrangian data
Ke Zhou, Samuel J. Grauer
We numerically investigate the feasibility and limits of jointly estimating flow fields and unknown particle properties (e.g., position, size, and density) from Lagrangian particle…
Open-source BOS tomography dataset of high-speed flow over a flight body
Joseph P. Molnar, Amit K. Singh, Christopher J. Clifford +4
We present an open-source background-oriented schlieren dataset with 70 views of high-speed flow over a flight body. Sample analyses are performed using a neural-implicit reconstru…
Neural inference of fluid-structure interactions from sparse off-body measurements
Rui Tang, Ke Zhou, Jifu Tan +1
We report a novel physics-informed neural framework for reconstructing unsteady fluid-structure interactions (FSI) from sparse, single-phase observations of the flow. Our approach…
Neural optical flow for planar and stereo PIV
Andrew I. Masker, Ke Zhou, Joseph P. Molnar +1
Neural optical flow (NOF) offers improved accuracy and robustness over existing OF methods for particle image velocimetry (PIV). Unlike other OF techniques, which rely on discrete…
Forward and inverse modeling of depth-of-field effects in background-oriented schlieren
Joseph P. Molnar, Elijah J. LaLonde, Christopher S. Combs +3
We report a novel "cone-ray" model of background-oriented schlieren (BOS) imaging that accounts for depth-of-field effects. Reconstructions of the density field performed with this…
Flow reconstruction and particle characterization from inertial Lagrangian tracks
Ke Zhou, Samuel J. Grauer
This text describes a method to simultaneously reconstruct flow states and determine particle properties from Lagrangian particle tracking (LPT) data. LPT is a popular measurement…