67 citations · 129 across the 12 of their papers we have counts for
12 papers
Ultra-high-speed chemiluminescence tomography of spinning-mode detonation waves
Amit K. Singh, Mateo Gomez, Kevin Y. Cho +2
This work presents a chemiluminescence tomography campaign to reconstruct time-resolved, three-dimensional reacting structures in detonation waves propagating through ethylene-base…
Degree-of-freedom and optimization-dynamic effects on the observability of Kuramoto-Sivashinsky systems
Noah B. Frank, Joshua L. Pughe-Sanford, Samuel J. Grauer
Simulations of chaotic systems can only produce high-fidelity trajectories if the initial and boundary conditions are well specified. When these conditions are unknown but measurem…
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…
Unsupervised neural-implicit laser absorption tomography for quantitative imaging of unsteady flames
Joseph P. Molnar, Jiangnan Xia, Rui Zhang +2
This paper presents a novel neural-implicit approach to laser absorption tomography (LAT) with an experimental demonstration. A coordinate neural network is used to represent therm…
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…
Stochastic particle advection velocimetry (SPAV): theory, simulations, and proof-of-concept experiments
Ke Zhou, Jiaqi Li, Jiarong Hong +1
Particle tracking velocimetry (PTV) is widely used to measure time-resolved, three-dimensional velocity and pressure fields in fluid dynamics research. Inaccurate localization and…
Estimating density, velocity, and pressure fields in supersonic flow using physics-informed BOS
Joseph P. Molnar, Lakshmi Venkatakrishnan, Bryan E. Schmidt +2
We report a new workflow for background-oriented schlieren (BOS), termed "physics-informed BOS," to extract density, velocity, and pressure fields from a pair of reference and dist…
Flow field tomography with uncertainty quantification using a Bayesian physics-informed neural network
Joseph P. Molnar, Samuel J. Grauer
We report a new approach to flow field tomography that uses the Navier-Stokes and advection-diffusion equations to regularize reconstructions. Tomography is increasingly employed t…