Sequential least-square reconstruction of instantaneous pressure field around a body from TR-PIV

Jeon, Young Jin; Gomit, Guillaume; Earl, Thomas; Chatellier, Ludovic; Lee, David

doi:10.1007/s00348-018-2489-0

Cited by 39 publications

(32 citation statements)

References 32 publications

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“…In order to reduce error propagation during pressure integration, Tronchin et al (2015) and Jeon et al (2018) employed approaches which divided the flow field into subdomains with respect to local velocity reliabilities, then performed pressure integration in the subdomains in descending order of the reliabilities. Consequently, the evaluated pressure in a more reliable subdomain was not affected by the erroneous velocity measurements in a less reliable subdomain.…”

Section: Introductionmentioning

confidence: 99%

“…This type of approaches is particularly effective for flow fields that can be segmented into regions with different levels of measurement accuracy. One example is the flow field around an airfoil which can be divided into outer-region, wake-region, near-body region, and near-edge region with descending accuracy (Jeon et al 2018). In these works, the reliability of each subdomain was defined by the Frobenius norm of the velocity gradient or pressure gradient tensor.…”

Section: Introductionmentioning

confidence: 99%

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

2020

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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%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2020

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“…Projection of instantaneous Eulerian velocity fields would require further optimisation of both the seeding concentration and the resolution of Eulerian meshing. Yet statistical 3D3C Eulerian projected results already open the road to the computation and analysis of quantities that require velocity gradients in all three space dimensions (shear rate, viscosity, shear stress...), and would otherwise only be accessible by tomographic-PIV (Jeon et al 2018a) or through scanning of the volume of measurement by several planar PIV measurement planes.…”

Section: Discussionmentioning

confidence: 99%

PTV measurements of oscillating grid turbulence in water and polymer solutions

et al. 2020

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Particle Tracking Velocimetry (PTV) is applied to measure the flow in an oscillating grid stirred tank filled with either water or shear thinning dilute polymer solutions (DPS) of Xanthan Gum (XG). There are many interests of studying turbulence in such complex non-Newtonian fluids (e.g. in the pharmaceutical, cosmetic, or food industry), and grid stirred tanks are commonly used for fundamental studies of turbulence in Newtonian fluids. Yet the case of oscillating grid flows in shear thinning solutions has been addressed recently by Lacassagne et al. (Exp Fluids 61(1):15, Phys Fluids 31(8):083102, 2019a, b), with only a single two dimensional (2D) Particle Image Velocimetry (PIV) characterization of mean flow and turbulence properties in the central vertical plane of the tank. Here, PTV data processed by the Shake The Box algorithm allows for the time resolved, three dimensional (3D) 3 components (3C) measurement of Lagrangian velocities for a large number of tracked particles in a central volume of interest of the tank. The possibility of projecting this Lagrangian information on an Eulerian grid is explored, and projected Eulerian results are compared with 2D PIV data from the previous work. Even if the mean flow is difficult to reproduce at the lowest polymer concentrations, a good agreement is found between measured turbulent decay laws, thus endorsing the use of this 3D-PTV metrology for the study of oscillating grid turbulence in DPS. The many possibilities of further analysis offered by the 3D3C nature of the data, either in the original Lagrangian form or in the projected Eulerian one, are finally discussed. Graphic Abstract

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“…The pressure calculation strategy adopted here is based on the work of Jeon et al [8], and relies on integrating the pressure gradient of the Navier-Stokes equations…”

Section: Pressure Calculation Methodsmentioning

confidence: 99%

Mechanical vs. phenomenological formulations to determine mean aerodynamic drag from stereo-PIV wake measurements

et al. 2019

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The present work compares two different drag breakdown methods based on the wake survey of a finite size wing. The traditionnal control volume approach based on a global balance of momentum (cf. for instance [17]) is considered, and compared to the phenomenological drag breakdown formulation put forward by Méheut & Bailly [14] within the aerodynamic context. Both formulations require information on the velocity field, but also the static or stagnation pressure in the wake plane of the model of interest. In this paper, we focus on computing the results based on velocity data exclusively, acquired by stereo-PIV. These two methods are benchmarked experimentally on the wake of a SACCON [19] model, that has been measured in one of ONERA's wind-tunnels, and their performance is evaluated by comparing their results to direct force balance measurements. It is shown that while both methods perform similarly, with drag predictions lying within 10% of the balance measurements, the control volume approach systematically underestimates the total drag. This behavior is attributed to the PIV-reconstructed pressure field failing in capturing the high depression occurring in the cores of the

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Sequential least-square reconstruction of instantaneous pressure field around a body from TR-PIV

Cited by 39 publications

References 32 publications

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

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

PTV measurements of oscillating grid turbulence in water and polymer solutions

Mechanical vs. phenomenological formulations to determine mean aerodynamic drag from stereo-PIV wake measurements

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