Direct calculation of inter-particle distance in suspension by image processing

Park, Dong Yong

doi:10.1016/j.powtec.2018.02.039

Cited by 4 publications

(7 citation statements)

References 13 publications

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“…We also see that, at these high solid volume fractions, the difference between b using the Krieger-Dougherty matching and the optical method in Ref. [48] is relatively small, at most 11.39% for our discrete concentrations. However, it is clear that this small disparity between the values causes a large change in the effective viscosity and bulk normal stress differences, the maximum difference being in second normal stress difference at φ = 0.4.…”

Section: E Which Interparticle Distance Is Appropriate?mentioning

confidence: 51%

“…It is notoriously difficult in experimentation to measure the mean distance between particles in a fluid suspension, and recent work has gone into its measurement. Park [48] has developed an image processing method for measuring the mean interparticle distance and applied it to two large choices of solid volume fraction φ = 0.4 and 0.5. We have taken these data points and converted them to values of the nondimensionalised cell radius.…”

Section: E Which Interparticle Distance Is Appropriate?mentioning

confidence: 99%

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Investigation into the rheology of a solid sphere suspension in second-order fluid using a cell model

Escott

Wilson

2020

Phys. Rev. Fluids

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We consider a suspension of solid spheres in a viscoelastic suspending fluid. We use the second-order fluid model to capture the first effects of viscoelasticity in the suspension and a mean-field cell model to express the influence of the solid particles. We create a semianalytical constitutive equation for the whole suspension, a second-order fluid with modified material parameters. We find that, in simple shear, the ratio of first normal stress difference to shear stress is independent of volume fraction, but that the second normal stress difference can change sign as the volume fraction of solids increases. Our model is valid for all flows for which the second-order fluid expansion is applicable.

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Section: E Which Interparticle Distance Is Appropriate?mentioning

confidence: 51%

Section: E Which Interparticle Distance Is Appropriate?mentioning

confidence: 99%

Investigation into the rheology of a solid sphere suspension in second-order fluid using a cell model

Escott

Wilson

2020

Phys. Rev. Fluids

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“…Algorithms for dispersion analysis were developed to evaluate the interparticle distance as a measure of particle aggregation and coagulation in selected pH and ionic strength conditions that are relevant to waste tank storage conditions 6 . Previously, statistical simulation of freespace was implemented in MATLAB™ to evaluate agglomeration in TEM images of polymers 7 .…”

Section: Introductionmentioning

confidence: 99%

“…Previously, statistical simulation of freespace was implemented in MATLAB™ to evaluate agglomeration in TEM images of polymers 7 . A recent work shows SEM image analysis using threshold intensity to filter particles from the binder, and mean interparticle distances were calculated using values of multiple neighbors with Monte-Carlo iterations 6 . However, there is still a lack of coherent interparticle distance measurement of secondary electron (SE) images for the determination of interparticle distances 7 .…”

Section: Introductionmentioning

confidence: 99%

In situ liquid SEM imaging analysis revealing particle dispersity in aqueous solutions

Yao

Ossana

Chun

et al. 2020

Journal of Microscopy

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A quantitative description on dispersity of boehmite (γ-AlOOH) particles, a key component for waste slurry at Hanford sites, can provide useful knowledge for understanding various physicochemical nature of the waste. In situ liquid scanning electron microscopy (SEM) was used to evaluate the dispersity of particles in aqueous conditions using a microfluidic sample holder, System for Analysis at Liquid Vacuum Interface (SALVI). Secondary electron (SE) images and image analyses were performed to determine particle centroid locations and the distance to the nearest neighbor particle centroid, providing reliable rescaled interparticle distances as a function of ionic strength in acidic and basic conditions. Our finding of the particle dispersity is consistent with physical insights from corresponding particle interactions under physicochemical conditions, demonstrating delicate changes in dispersity of boehmite particles based on novel in situ liquid SEM imaging and analysis.

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“…The image processing applications are the application that requires a high specification computer or parallel processing techniques to speed up the processing time, especially in applications that use a complex algorithms or methods. Some publication about image processing have been reported, Park et al have implemented the direct calculation of interparticle distance in suspension by image processing using monte carlo method [1], Saleem et al explain a comparison of feature points method on multisensor images [2], Husin et al [3] on the poisonous shrimp detection system for litopenaeus vannamei using k-Nearest Neighbor (k-NN) method, and Mirdanies et al [4] has also successfully implemented the multi-object object recognition software on Remote Controlled Weapon Station (RCWS) using Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) methods.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Parallel Processing on Multi-Object Recognition System Software

Mirdanies

2018

LKJITI

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Multi-object recognition software on Remote Controlled Weapon Station (RCWS) had been implemented in previous paper using Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) methods, but the processing time in one cycle is quite slow so it is need to be optimized using parallel processing. In this paper, implementation of parallel processing on multi-object recognition software has been done on a multicore processor. The Openmp Application Programming Interface (API), C programming language, and Visual studio Integrated Development Environment (IDE) is used to implement the parallel processing in this paper. The parallel processing was implemented in the for loop of the matching process between the capturing object from the camera and the database under two conditions, i.e., the original of the for loop syntax and after optimization of the for loop syntax. Experiments have been done on the core processor i7-4790 @ 3.60Ghz, 8 GB DDR3 of memory, windows 8.1 os using two, four, six, and eight cores to recognize one, two, three and four objects at once using SIFT and SURF methods. Based on the experiments, it was found that the processing time in parallel is faster than sequential process, where the fastest of the processing time is obtained after optimization in the loop syntax, with the processing time in recognizing one to four objects using SIFT method is 927.13 ms (8 core), 1019.31 ms (6 core), 1190.72 ms (8 core), and 1283.05 ms (4 core), where the sequential processing time in recognizing one to four objects is 1067.35 ms, 1164.78 ms, 1352.93 ms, and 1497.35 ms, while the processing time in recognizing one to four objects using SURF method is 1157.13 ms (8 core), 1517.83 ms (6 core), 1572.14 ms (4 core), dan 1472.64 ms (6 core), where the sequential processing time in recognizing one to four objects is 5635.99 ms, 6268.47 ms, 3256.63 ms, dan 3883.78 ms.

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Direct calculation of inter-particle distance in suspension by image processing

Cited by 4 publications

References 13 publications

Investigation into the rheology of a solid sphere suspension in second-order fluid using a cell model

Investigation into the rheology of a solid sphere suspension in second-order fluid using a cell model

In situ liquid SEM imaging analysis revealing particle dispersity in aqueous solutions

Implementation of Parallel Processing on Multi-Object Recognition System Software

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