Particle image velocimetry in molten plastic

Bress, Thomas J.; Dowling, David R.

doi:10.1002/pen.21881

Cited by 3 publications

(13 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bress and Dowling [15] used digital particle image velocimetry (DPIV) to measure midplane velocity vector fields throughout the mold filling process. An optical access mold was built to allow passage of a pulsed laser sheet through the midplane of a corner‐gated nominally rectangular cavity 109.5 mm × 66.7 mm × 6.3 mm (4 5/16″ by 2 5/8″ by 0.25″).…”

Section: Resultsmentioning

confidence: 99%

“…Reliable PIV measurements could not be made within 4 cm of the gate because of the strong radial flow in that region and the finite temporal resolution of the digital camera used for particle‐imageacquisition. In a strong radial flow PIV particles separate from their neighbors leading to failure of the cross‐correlation process [15]. Thus, the vector‐free zone nearest the gate in each frame of Figs.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, real time velocity vector field data for polystyrene flowing into a corner‐gated rectangular mold have been reported [15], and the present study follows from this prior effort. An optical access mold was constructed that allowed passage of a laser sheet through the midplane of the mold [16].…”

Section: Introductionmentioning

confidence: 98%

“…Flow front geometry as a function of time was also extracted from the image data. The experimental details and results are given in [15].…”

Section: Introductionmentioning

confidence: 99%

“…Based on mold geometry, plastic material properties, and injection conditions, Moldflow predicts time‐varying average velocity fields and melt front locations. In the present work Moldflow was used to simulate the melt flow observed in [15], and the experimental data and simulation results are compared to assess the accuracy of the simulations.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Simulations and measurements of in‐mold melt flow during the injection molding of polystyrene

Bress

Dowling

2012

Polymer Engineering & Sci

Self Cite

View full text Add to dashboard Cite

Simulations are often used to model polymer flow during injection molding to design molds and select processing parameters. It is difficult to determine the accuracy of these simulations due to a lack of experimentally measured in‐mold velocimetry and melt‐front progression data. This article compares the results from commercial mold‐filling simulation software to experimental data obtained via particle image velocimetry (PIV) in a special optical‐access mold with a rectangular cavity. Moldflow was used to simulate the mold filling by a polystyrene melt in the experimental configuration, and these simulated results are compared to the appropriately averaged time‐varying velocity field measurements. Simulated results for melt‐front progression are also compared with experimentally observed flow fronts. The ratio of the experimentally measured average velocity magnitudes to the simulation magnitudes was found on average to be 0.99 with a standard deviation of 0.25, and the difference in velocity orientations was found to be 0.9° with a standard deviation of 3.2°. The corner area opposite the gate was most problematic for the simulation. The region behind the front also had a relatively high simulation error, though not as severe as that in the corner. POLYM. ENG. SCI. 2013. © 2012 Society of Plastics Engineers

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

“…Flow front geometry as a function of time was also extracted from the image data. The experimental details and results are given in [15].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Simulations and measurements of in‐mold melt flow during the injection molding of polystyrene

Bress

Dowling

2012

Polymer Engineering & Sci

Self Cite

View full text Add to dashboard Cite

show abstract

Velocity and Flow Pattern Around Bottom Outlet as Revealed by Particle Image Velocimetry

Laksitaningtyas

2023

GEOMATE

View full text Add to dashboard Cite

This experimental study is to determine the velocity vector and flow contours in a reservoir with a bottom outlet. Velocity vector flow and contour characteristics are studied by making laboratory studies using acrylic models. The model combines the first pond as a constant head tank and the second pond as a reservoir with a hole at the bottom or bottom outlet. Particle Image Velocimetry (PIV) is an optical visualization qualitative and quantitative technique for measuring the velocity of a fluid by measuring the slight movement of a particle or object in a particular fluid area by observing the location of the tracer particle used in education and research detail. The PIV method relies on recording particle images and measuring object markers (tracer particles) distribution at several locations to measure instantaneous velocity and different phases, velocity fluctuations, and accelerations well in fluid flow. Brown shellac was chosen and used for tracer particles after several experiments using other seeding materials such as white shellack, glitter, and glycerine. The brown shellack is crushed manually, filtered to pass sieve 30 (0.5 mm), and retained on sieve 50 (0.3 mm). The recorded image file is then read and processed to obtain the magnitude and direction of velocity at the tracer particle locations captured in the image recording. The experiment was carried out by running the water flow from the constant head to the reservoir model by giving tracer particles of brown shellac. Experiments produce the most significant velocity around the bottom outlet is 265 cm/s or 2.65 m/s.

show abstract

Multi-Scale Modeling of Plastic Waste Gasification: Opportunities and Challenges

et al. 2022

View full text Add to dashboard Cite

Among the different thermo-chemical recycling routes for plastic waste valorization, gasification is one of the most promising, converting plastic waste into syngas (H2+CO) and energy in the presence of an oxygen-rich gas. Plastic waste gasification is associated with many different complexities due to the multi-scale nature of the process, the feedstock complexity (mixed polyolefins with different contaminations), intricate reaction mechanisms, plastic properties (melting behavior and molecular weight distribution), and complex transport phenomena in a multi-phase flow system. Hence, creating a reliable model calls for an extensive understanding of the phenomena at all scales, and more advanced modeling approaches than those applied today are required. Indeed, modeling of plastic waste gasification (PWG) is still in its infancy today. Our review paper shows that the thermophysical properties are rarely properly defined. Challenges in this regard together with possible methodologies to decently define these properties have been elaborated. The complexities regarding the kinetic modeling of gasification are numerous, compared to, e.g., plastic waste pyrolysis, or coal and biomass gasification, which are elaborated in this work along with the possible solutions to overcome them. Moreover, transport limitations and phase transformations, which affect the apparent kinetics of the process, are not usually considered, while it is demonstrated in this review that they are crucial in the robust prediction of the outcome. Hence, possible approaches in implementing available models to consider these limitations are suggested. Finally, the reactor-scale phenomena of PWG, which are more intricate than the similar processes—due to the presence of molten plastic—are usually simplified to the gas-solid systems, which can result in unreliable modeling frameworks. In this regard, an opportunity lies in the increased computational power that helps improve the model’s precision and allows us to include those complexities within the multi-scale PWG modeling. Using the more accurate modeling methodologies in combination with multi-scale modeling approaches will, in a decade, allow us to perform a rigorous optimization of the PWG process, improve existing and develop new gasifiers, and avoid fouling issues caused by tar.

show abstract

Particle image velocimetry in molten plastic

Cited by 3 publications

References 29 publications

Simulations and measurements of in‐mold melt flow during the injection molding of polystyrene

Simulations and measurements of in‐mold melt flow during the injection molding of polystyrene

Velocity and Flow Pattern Around Bottom Outlet as Revealed by Particle Image Velocimetry

Multi-Scale Modeling of Plastic Waste Gasification: Opportunities and Challenges

Contact Info

Product

Resources

About