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We propose a meshless method to compute pressure fields from image velocimetry data, regardless of whether this is available on a regular grid as in cross-correlation based velocimetry or on scattered points as in tracking velocimetry. The…

流体动力学 · 物理学 2022-06-22 Pietro Sperotto , Sandra Pieraccini , Miguel A. Mendez

We describe a technique of image analysis providing the time resolved shape of a water surface simultaneously with the three-component velocity field on this surface. The method relies on a combination of stereoscopic surface mapping and…

流体动力学 · 物理学 2019-02-18 Quentin Aubourg , Joel Sommeria , Samuel Viboud , Nicolas Mordant

This work aims to estimate time-resolved velocity field that is directly associated with pressure fluctuations in a subsonic round jet. To achieve this goal, synchronous measurements of the velocity field and in-flow pressure fluctuations…

流体动力学 · 物理学 2021-06-15 Songqi Li , Lawrence Ukeiley

An important tool for experimental fluids mechanics research is Particle Image Velocimetry (PIV). Several robust methodologies have been proposed to perform the estimation of velocity field from the images, however, alternative methods are…

计算机视觉与模式识别 · 计算机科学 2025-02-03 Efraín Magaña , Francisco Sahli Costabal , Wernher Brevis

In this paper, a rapid approximation method is introduced to estimate the sea surface velocity field based on scattered measurements. The method uses a simplified two-dimensional flow model as a surrogate model, which mimics the real…

流体动力学 · 物理学 2024-11-05 Karlo Jakac , Luka Lanča , Ante Sikirica , Stefan Ivić

A method is proposed to estimate the velocity field of an unsteady flow using a limited number of flow measurements. The method is based on a non-linear low-dimensional model of the flow and on expanding the velocity field in terms of…

最优化与控制 · 数学 2009-11-13 Marcelo Buffoni , Simone Camarri , Angelo Iollo , Edoardo Lombardi , Maria-Vittoria Salvetti

Estimation of unsteady flow fields around flight vehicles may improve flow interactions and lead to enhanced vehicle performance. Although flow-field representations can be very high-dimensional, their dynamics can have low-order…

流体动力学 · 物理学 2023-03-23 John Graff , Albert Medina , Francis Lagor

A novel uncertainty-based pressure reconstruction method is proposed to evaluate the instantaneous pressure fields from velocity fields measured using particle image velocimetry (PIV) or particle tracking velocimetry (PTV). First, the…

流体动力学 · 物理学 2019-12-03 Jiacheng Zhang , Sayantan Bhattacharya , Pavlos P. Vlachos

Particle Image Velocimetry (PIV) is an imaging technique in experimental fluid dynamics that quantifies flow fields around bluff bodies by analyzing the displacement of neutrally buoyant tracer particles immersed in the fluid. Traditional…

流体动力学 · 物理学 2025-12-15 Alan Bonomi , Francesco Banelli , Antonio Terpin

A novel method to improve the accuracy of pressure field estimation from time-resolved Particle Image Velocimetry data is proposed. This method generates several new time-series of velocity field by propagating in time the original one…

流体动力学 · 物理学 2025-01-14 Junwei Chen , Marco Raiola , Stefano Discetti

Based on machine learning techniques, we propose a novel method to estimate flow fields using only floating sensor locations. This method does not require either ground-truth velocity fields or governing equations for fluid flows, which is…

流体动力学 · 物理学 2026-04-07 Tomoya Oura , Reno Miura , Koji Fukagata

A novel method to estimate unsteady aerodynamic force coefficients from pointwise velocity measurements is presented. The methodology is based on a resolvent-based reduced-order model which requires the mean flow to obtain physical flow…

流体动力学 · 物理学 2016-09-21 F. Gómez , A. S. Sharma , H. M. Blackburn

A three-dimensional (3D) scanning velocimetry system is developed to quantify the 3D configurations of particles and their surrounding volumetric, three-component velocity fields. The approach uses a translating laser sheet to rapidly scan…

流体动力学 · 物理学 2021-08-04 Matt K. Fu , Isabel A. Houghton , John O. Dabiri

For simulating incompressible flows by projection methods. it is generally accepted that the pressure-correction stage is the most time-consuming part of the flow solver. The objective of the present work is to develop a fast hybrid…

流体动力学 · 物理学 2023-06-05 Jiannong Fang

Using limited observations of the velocity field of the two-dimensional Navier-Stokes equations, we successfully reconstruct the steady body force that drives the flow. The number of observed data points is less than 10\% of the number of…

流体动力学 · 物理学 2024-02-26 Aseel Farhat , Adam Larios , Vincent R. Martinez , Jared P. Whitehead

This article describes two independent developments aimed at improving the Particle Tracking Method for measurements of flow or particle velocities. First, a stereoscopic multicamera calibration method that does not require any optical…

We propose a method using supervised machine learning to estimate velocity fields from particle images having missing regions due to experimental limitations. As a first example, a velocity field around a square cylinder at Reynolds number…

流体动力学 · 物理学 2021-09-09 Masaki Morimoto , Kai Fukami , Koji Fukagata

A method for estimating the base pressure in bluff body flows is proposed. The estimation uses the velocity at the separation edge with the Bernoulli equation to compute the base pressure. The assumption is that this is the base pressure…

流体动力学 · 物理学 2020-07-21 Benjamin Bock

Time--distance inversions usually provide tomographic maps of the interesting plasma properties (we will focus on flows) at various depths. These maps however do not correspond directly to the flow field, but rather to the true flow field…

太阳与恒星天体物理 · 物理学 2017-04-12 M. Svanda , M. Kozon

In Microscopic Particle Image Velocimetry ($\mu$PIV), velocity fields in microchannels are sampled over finite volumes within which the velocity fields themselves may vary significantly. In the past, this has limited measurements often to…

介观与纳米尺度物理 · 物理学 2014-07-31 P. W. Bryant , R. F. Neumann , M. J. B. Moura , M. Steiner , M. S. Carvalho , C. Feger
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