Determination of fluid flow adjacent to a gas/liquid interface using particle tracking velocimetry (PTV) and a high-quality tessellation approach

Azadi, Reza; Wong, Jaime G.; Nobes, David S.

doi:10.1007/s00348-020-03103-5

Cited by 7 publications

(16 citation statements)

References 35 publications

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“…Important flow parameters such as pressure can be calculated after distributing the dispersed PTV velocity vectors, on a discretized domain Azadi et al (2021). The structured rectangular grids are mostly used for simple geometries.…”

Section: Image Processingmentioning

confidence: 99%

“…The structured rectangular grids are mostly used for simple geometries. In cases with complex geometries, moving boundaries, or high curved regions such as the Tesla valve geometry, generating a high-quality structured grid becomes more challenging Azadi et al (2021). To overcome this issue, a tessellation meshing method can be used.…”

Section: Image Processingmentioning

confidence: 99%

“…To overcome this issue, a tessellation meshing method can be used. This method maps the dispersed PTV velocity data onto an optimized high-quality triangular node structure Azadi et al (2021).…”

Section: Image Processingmentioning

confidence: 99%

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Determination of the Near Wall Flow of a Multi-Stage Tesla Diode Using PIV and PTV

Saffar¹,

Ansari²,

Azadi³

et al. 2021

ISPIV21

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Particle image velocimetry (PIV) and particle tracking velocimetry (PTV) are two popular methods to measure the velocity in complex geometries such as the Tesla valve. This paper provides an investigation on the application of a tessellation meshing method for interpolating non-uniform velocity vectors calculated using PTV. The procedure to apply this method containing mask generation and mesh study is described. The results are compared to the PIV results particularly where the near wall results are important. The result of the flow field calculated by the application of the tessellation method on the PTV results are presented for a two-stage Tesla valve operated in the range of Re = 100 to 600 both in forward and reverse configuration.

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Section: Image Processingmentioning

confidence: 99%

Section: Image Processingmentioning

confidence: 99%

See 1 more Smart Citation

Determination of the Near Wall Flow of a Multi-Stage Tesla Diode Using PIV and PTV

Saffar¹,

Ansari²,

Azadi³

et al. 2021

ISPIV21

Self Cite

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show abstract

“…The mesh must be dynamic and adjust itself to the moving boundaries. This tessellation method has already been developed by the current authors [5]. As the next step, equations of motion for a deformable C.V. need to be coupled with the tessellation method to calculate the instantaneous pressures in a two-phase flow field, with a moving interface, which will be the ultimate goal of the current study.The conservation equations of volume, mass, and momentum, governing an isotropic fluid flow, embedded in a control volume (C.V.…”

mentioning

confidence: 99%

“…To do this, the first step is to interconnect the sparse PTV velocity vectors in the flow domain using a high-quality mesh grid, which needs to dynamically adjust to the deformable and moving boundaries of the flow field. This part of the algorithm has been successfully developed and validated, for which the details are given in Azadi et al [5]. Figure 1 represents the sample tessellation results for two different snapshots of the gas-liquid flow field in the sinusoidal channel.…”

mentioning

confidence: 99%

Pressure calculation for flows with moving surface boundaries from particle tracking velocimetry (PTV)

Azadi¹,

Nobes²

2021

ISPIV21

Self Cite

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The examples of flow conditions, where an object of a fixed or deformable body moves in a fluid, or the interface between the flow phases instantaneously changes its topology, are numerous in industry and natural sciences. The advent of particle image velocimetry (PIV) [1] and particle tracking velocimetry (PTV) [2] enabled the measurement of the instantaneous velocity fields in these types of complicated flow fields. As a next step, several methodologies have been developed in the past decade to calculate the pressure fields from PIV or PTV data [3,4]. These methods were developed based on the assumption of a stationary flow domain, with surface boundaries that are fixed and independent of time. This makes the current pressure calculation methods inapplicable to a flow domain with deformable moving surface boundaries. Also, for most of the two-phase flows, the capillary forces are significant and the pressure drop over the two-phase interface must be considered. Therefore, the current pressure calculators require an improvement in the formulation of the algorithms to account for the deformable volume conditions and the effect of the surface tension force. For the calculation of pressure from sparse PTV velocity data, firstly, a tessellation method is required to interconnect the irregularly spaced vectors in the flow field using a highquality mesh grid. The mesh must be dynamic and adjust itself to the moving boundaries. This tessellation method has already been developed by the current authors [5]. As the next step, equations of motion for a deformable C.V. need to be coupled with the tessellation method to calculate the instantaneous pressures in a two-phase flow field, with a moving interface, which will be the ultimate goal of the current study.

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Single-camera PTV within interfacially sheared drops in microgravity

McMackin,

Adam,

Riley

et al. 2023

Exp Fluids

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Development of experimental methods for in situ particle tracking velocimetry (PTV) is fundamental for allowing measurement of moving systems nontailored for velocimetry. This investigation focuses on the development of a post-processing methodology for single-camera PTV, without laser-sheet illumination, tracking native air-bubbles as tracer particles within a liquid drop of human insulin in microgravity. This PTV scenario was facilitated by microgravity technology known as the ring-sheared drop (RSD), aboard the International Space Station, which produced an optical imaging scenario and fluid flow geometry suitable as a testbed for PTV research. The post processing methodology performed included five steps: (i) physical system characterization and native air-bubble tracer identification, (ii) image projection and single-camera calibration, (iii) depth determination and 3D particle position determination, (iv) ray tracing and refraction correction, and (v) particle history and dataIn situ PTV in sheared drops expansion for suboptimal particles. Overall, this post processing methodology successfully allowed for a total of 1,085 particle measurements in a scenario where none were previously possible. Such post processing methodologies have promise for application to other in situ PTV scenarios allowing better understanding of physical systems whose flow is difficult to measure and or where PTV-specific optical elements (such as laser lights sheets and dual camera setups) are not permissible due to physical or safety constraints.

show abstract

Determination of fluid flow adjacent to a gas/liquid interface using particle tracking velocimetry (PTV) and a high-quality tessellation approach

Cited by 7 publications

References 35 publications

Determination of the Near Wall Flow of a Multi-Stage Tesla Diode Using PIV and PTV

Determination of the Near Wall Flow of a Multi-Stage Tesla Diode Using PIV and PTV

Pressure calculation for flows with moving surface boundaries from particle tracking velocimetry (PTV)

Single-camera PTV within interfacially sheared drops in microgravity

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