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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 tracking (LPT) data. LPT offers time-resolved, volumetric…

Fluid Dynamics · Physics 2026-05-26 Ke Zhou , Samuel J. Grauer

The standard approach to densely reconstruct the motion in a volume of fluid is to inject high-contrast tracer particles and record their motion with multiple high-speed cameras. Almost all existing work processes the acquired multi-view…

Computer Vision and Pattern Recognition · Computer Science 2019-11-25 Katrin Lasinger , Christoph Vogel , Thomas Pock , Konrad Schindler

The three-dimensional Time-Resolved Lagrangian Particle Tracking (3D TR-LPT) technique has recently advanced flow diagnostics by providing high spatiotemporal resolution measurements under the Lagrangian framework. To fully exploit its…

Fluid Dynamics · Physics 2023-08-21 Lanyu Li , Zhao Pan

We propose a defiltering method of turbulent flow fields for Lagrangian particle tracking using machine learning techniques. Numerical simulation of Lagrangian particle tracking is commonly used in various fields. In general, practical…

Fluid Dynamics · Physics 2024-11-21 Tomoya Oura , Koji Fukagata

Positron Emission Particle Tracking (PEPT) is an imaging method for the visualization of fluid motion, capable of reconstructing three-dimensional trajectories of small tracer particles suspended in nearly any medium, including fluids that…

Instrumentation and Detectors · Physics 2023-03-20 Avshalom Offner , Sam Manger , Jacques Vanneste

Lagrangian Particle Tracking (LPT) enables practitioners to study various concepts in turbulence by measuring particle positions in flows of interest. This data is subject to measurement errors, and filtering techniques are applied to…

Fluid Dynamics · Physics 2026-01-16 Griffin M. Kearney , Kasey M. Laurent , Reece V. Kearney

Fluid deformation and strain history are central to wide range of fluid mechanical phenomena ranging from fluid mixing and particle transport to stress development in complex fluids and the formation of Lagrangian coherent structures…

Fluid Dynamics · Physics 2025-10-03 Daniel R. Lester , Marco Dentz , Tanguy Le Borgne , Felipe P. J. de Barros

Physics-Informed Neural Networks (PINNs) have gained widespread popularity for solving inverse and forward problems across a range of scientific and engineering domains. However, most existing PINN frameworks are limited to the Eulerian…

Fluid Dynamics · Physics 2025-04-15 Sukirt Thakur , Maziar Raissi

The study of moving particles (e.g. molecules, virus, vesicles, organelles, or whole cells) is crucial to decipher a plethora of cellular mechanisms within physiological and pathological conditions. Powerful live-imaging approaches enable…

Numerical Analysis · Mathematics 2023-11-13 Eloina Corradi , Maurizio Tavelli , Marie-Laure Baudet , Walter Boscheri

Advances in time-resolved Particle Tracking Velocimetry (4D-PTV) techniques have been consistently revealed more accurate Lagrangian particle motions. A novel track initialisation technique as a complementary part of 4D-PTV, based on local…

Fluid Dynamics · Physics 2021-09-10 Ali Rahimi Khojasteh , Yin Yang , Dominique Heitz , Sylvain Laizet

We present a new application of Lagrangian Perturbation Theory (LPT): the stability analysis of fluid flows. As a test case that demonstrates the framework we focus on the plane Couette flow. The incompressible Navier-Stokes equation is…

Fluid Dynamics · Physics 2018-05-01 Sharvari Nadkarni-Ghosh , Jayanta K. Bhattacharjee

This paper demonstrates particle tracking velocimetry performed for a model system wherein particle-laden liquid metal flow about a cylindrical obstacle was studied. We present the image processing methodology developed for particle…

We explore the application of the reference map technique, originally developed for the Eulerian simulation of solid mechanics, in Lagrangian kinematics of fluid flows. Unlike traditional methods based on explicit particle tracking, the…

Fluid Dynamics · Physics 2024-12-04 Imran Hayat , Ryan T. Black , George Ilhwan Park

The purpose of this paper is to examine the Lagrangian stochastic modeling of the fluid velocity seen by inertial particles in a nonhomogeneous turbulent flow. A new Langevin-type model, compatible with the transport equation of the drift…

Fluid Dynamics · Physics 2009-07-01 Boris Arcen , Anne Tanière

A numerical model and parallel software for 3D simulations of granular flows have been developed based on the Lagrangian particle (LP) method [R.Samulyak, X. Wang, H.-C. Chen, Lagrangian particle method for compressible fluid dynamics, J.…

Computational Physics · Physics 2022-06-29 Mario Zepeda , Roman Samulyak

We studied the reconstruction of turbulent flow fields from trajectory data recorded by actively migrating Lagrangian agents. We propose a deep-learning model, track-to-flow (T2F), which employs a vision transformer as the encoder to…

Fluid Dynamics · Physics 2026-01-15 Hua-Lin Wu , Ao Xu , Heng-Dong Xi

Non-invasive flow measurement techniques, such as particle tracking velocimetry, resolve 3D velocity fields by pairing tracer particle positions in successive time steps. These trajectories are crucial for evaluating physical quantities…

Data Analysis, Statistics and Probability · Physics 2024-07-08 Kairui Hao , Atharva Hans , Pavlos Vlachos , Ilias Bilionis

In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the…

Data Analysis, Statistics and Probability · Physics 2021-06-10 Joosep Pata , Javier Duarte , Jean-Roch Vlimant , Maurizio Pierini , Maria Spiropulu

We present a frame-invariant method for detecting coherent structures from Lagrangian flow trajectories that can be sparse in number, as is the case in many fluid mechanics applications of practical interest. The method, based on principles…

Fluid Dynamics · Physics 2017-03-08 Kristy L. Schlueter-Kuck , John O. Dabiri

Time-varying vector fields produced by computational fluid dynamics simulations are often prohibitively large and pose challenges for accurate interactive analysis and exploration. To address these challenges, reduced Lagrangian…

Machine Learning · Computer Science 2022-04-11 Mengjiao Han , Sudhanshu Sane , Chris R. Johnson
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