Pressure-field extraction on unstructured flow data using a Voronoi tessellation-based networking algorithm: a proof-of-principle study

Neeteson, Nathan; Rival, David E.

doi:10.1007/s00348-015-1911-0

Cited by 33 publications

(14 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent developments in tomographic PIV (Elsinga et al 2006) and three-dimensional particle tracking velocimetry (PTV) ) allow three-dimensional velocity field characterization inside a volume, further extending the capacity of pressure estimation (Violato et al 2011;Ghaemi et al 2012;Neeteson and Rival 2015;Laskari et al 2016;Schneiders et al 2016). For volumetric data, Poisson equation based methods are widely used and are relatively computationally inexpensive (Blinde et al 2016;Huhn et al 2016).…”

Section: Introductionmentioning

confidence: 98%

Optimization of planar PIV-based pressure estimates in laminar and turbulent wakes

McClure

Yarusevych

2017

Exp Fluids

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 98%

Optimization of planar PIV-based pressure estimates in laminar and turbulent wakes

McClure

Yarusevych

2017

Exp Fluids

View full text Add to dashboard Cite

“…Further, most pressure measurement techniques have limitations in dynamic range and resolvable frequency bandwidth. With the development of flow measurement techniques such as particle image velocimetry (PIV) and particle tracking velocimetry (PTV), the velocity fields can be obtained and utilized for instantaneous pressure evaluation (Fujisawa et al 2005;Liu and Katz 2006;Charonko et al 2010;Neeteson and Rival 2015;Huhn et al 2016). Most pressure reconstruction methods require two major steps to calculate the pressure fields from velocity measurements.…”

Section: Introductionmentioning

confidence: 99%

“…K𝒖 KL is the material acceleration which can be evaluated using the Eulerian approach from gridded velocity data (Fujisawa et al 2005;de Kat et al 2009;Charonko et al 2010;Tronchin et al 2015) or the Lagrangian approach from particle tracks (Neeteson and Rival 2015;Gesemann et al 2016;Huhn et al 2016). For pressure integration, one common approach is path-integration (also referred to as spatial-marching) which integrates the pressure gradient along paths across the flow domain (Liu and Katz 2006;Dabiri et al 2014;Tronchin et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Using uncertainty to improve pressure field reconstruction from PIV/PTV flow measurements

2020

View full text Add to dashboard Cite

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 pressure gradient fields are calculated from velocity fields, while the local and instantaneous pressure gradient uncertainty is estimated from the velocity uncertainty using a linear-transformation based algorithm. The pressure field is then reconstructed by solving an overdetermined linear system which involves the pressure gradients and boundary conditions. This linear system is solved with generalized least-squares (GLS) which incorporates the previously estimated variances and covariances of the pressure gradient errors as inverse weights to optimize the reconstructed pressure field. The method was validated with synthetic velocity fields of a 2D pulsatile flow and the results show significantly improved pressure accuracy with an error reduction of as much as 250% compared to the existing baseline method of solving the pressure Poisson equation (PPE). The GLS was more robust to the velocity errors and provides greater improvement with spatially correlated velocity errors. For experimental validation, the volumetric pressure fields were evaluated from a laminar pipe flow velocity field measured using 3D PTV. The GLS reduced the median absolute pressure errors by as much as 96%.

show abstract

“…These have been recently enabled by advances in tracking techniques , nowadays capable of providing accurate trajectories with high seeding densities. Lagrangian approaches can be further distinguished in techniques that interpolate the particle acceleration onto a Cartesian grid Huhn et al, 2016Huhn et al, , 2018 and techniques that integrate the pressure on scattered data such as the Voronoi integration proposed by Neeteson and Rival (2015).…”

Section: Introductionmentioning

confidence: 99%

A Meshless Method to Compute Pressure Fields from Image Velocimetry

Sperotto,

Pieraccini,

Mendez

2021

Preprint

View full text Add to dashboard Cite

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 proposed approach is based on Radial Basis Functions (RBFs) regression and relies on the solution of two constrained least square problems. The first one is the regression of the measurements to create an analytic representation of the velocity field. This regression can be constrained to impose boundary conditions (e.g. no-slip velocity on a wall or inlet conditions) or differential constraints (e.g. the solenoidal condition for an incompressible flow). The second one is the meshless integration of the pressure Poisson equation, achieved by seeking a solution in the form of a RBF expansion and using constraints to impose boundary conditions.We first illustrate the derivation of the two least square problems and the numerical techniques implemented for their solution. Then, we showcase the method with three numerical test cases of growing complexity. These are a 2D Gaussian Vortex, a 2D flow past a cylinder from CFD and a 3D Stokes flow past a sphere. For each case, we consider randomly sampled vector fields simulating particle tracking measurements and analyze the sensitivity to noise and seeding density.

show abstract

Pressure-field extraction on unstructured flow data using a Voronoi tessellation-based networking algorithm: a proof-of-principle study

Cited by 33 publications

References 15 publications

Optimization of planar PIV-based pressure estimates in laminar and turbulent wakes

Optimization of planar PIV-based pressure estimates in laminar and turbulent wakes

Using uncertainty to improve pressure field reconstruction from PIV/PTV flow measurements

A Meshless Method to Compute Pressure Fields from Image Velocimetry

Contact Info

Product

Resources

About