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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…

流体动力学 · 物理学 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…

计算机视觉与模式识别 · 计算机科学 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…

流体动力学 · 物理学 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…

流体动力学 · 物理学 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…

仪器与探测器 · 物理学 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…

流体动力学 · 物理学 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…

流体动力学 · 物理学 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…

流体动力学 · 物理学 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…

数值分析 · 数学 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…

流体动力学 · 物理学 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…

流体动力学 · 物理学 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…

流体动力学 · 物理学 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…

流体动力学 · 物理学 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.…

计算物理 · 物理学 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…

流体动力学 · 物理学 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…

数据分析、统计与概率 · 物理学 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…

数据分析、统计与概率 · 物理学 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…

流体动力学 · 物理学 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…

机器学习 · 计算机科学 2022-04-11 Mengjiao Han , Sudhanshu Sane , Chris R. Johnson
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