Impact of inertia and channel angles on flow distribution in microfluidic junctions

Błoński, Sławomir; Zaremba, Damian; Jachimek, M.; Jakieła, Sławomir; Wacławczyk, Tomasz; Korczyk, Piotr M.

doi:10.1007/s10404-020-2319-6

Cited by 10 publications

(7 citation statements)

References 39 publications

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“…This condition was enhanced for the rectangular shape. Moreover, it was proven from experimental results that angular edges (as can be identified in the rhomboidal and rectangular shapes) are more prone to accumulate bubbles than round edges [ 55 , 56 ]. Thus, in our final device, we incorporated the elliptical shape for realizing the microfluidic reservoir.…”

Section: Resultsmentioning

confidence: 99%

SMILE Platform: An Innovative Microfluidic Approach for On-Chip Sample Manipulation and Analysis in Oral Cancer Diagnosis

et al. 2021

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Oral cancer belongs to the group of head and neck cancers, and, despite its large diffusion, it suffers from low consideration in terms of prevention and early diagnosis. The main objective of the SMILE platform is the development of a low-cost device for oral cancer early screening with features of high sensitivity, specificity, and ease of use, with the aim of reaching a large audience of possible users and realizing real prevention of the disease. To achieve this goal, we realized two microfluidic devices exploiting low-cost materials and processes. They can be used in combination or alone to obtain on-chip sample preparation and/or detection of circulating tumor cells, selected as biomarkers of oral cancer. The realized devices are completely transparent with plug-and-play features, obtained thanks to a highly customized architecture which enables users to easily use them, with potential for a common use among physicians or dentists with minimal training.

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Section: Resultsmentioning

confidence: 99%

SMILE Platform: An Innovative Microfluidic Approach for On-Chip Sample Manipulation and Analysis in Oral Cancer Diagnosis

et al. 2021

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“…We enforce conservation of flow and equal pressures at the junction outlets. Models exist to predict the pressure losses over junctions [22,21,73]. However, in our preliminary studies, these did not lead to a significant improvement in approximation quality and were thus not included.…”

Section: Discussionmentioning

confidence: 98%

Automated generation of 0D and 1D reduced-order models of patient-specific blood flow

Pfaller,

Pham,

Verma

et al. 2021

Preprint

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Three-dimensional (3D) cardiovascular fluid dynamics simulations typically require hours to days of computing time on a high-performance computer. One-dimensional (1D) and lumped-parameter zero-dimensional (0D) models show great promise for accurately predicting blood flow and pressure with only a fraction of the cost. They can accelerate uncertainty quantification, optimization, and design parameterization studies. Previously, these models needed to be created laboriously by hand, and this limited assessment of their approximation accuracy to very few models in prior studies. This work proposes a fully automated and openly available framework to generate and simulate 0D and 1D models from 3D patient-specific geometries. Our only input is the 3D geometry; we do not use any prior knowledge from 3D simulations. All computational tools presented in this work are implemented in the open-source software platform SimVascular. We demonstrate the reduced-order approximation quality against full 3D solutions in a comprehensive comparison with N = 73 publicly available models from various anatomies, vessel types, and disease conditions. Relative average approximation errors of flows and pressures typically ranged from 1 % to 10 % for both 0D and 1D models, at the caps and inside the vessel branches. Though they have minimally higher approximation errors than 1D, we recommend using 0D models due to their robustness and computational efficiency. Automatically generated reduced-order models can significantly speed up model development and shift the computational load from high-performance to personal computers.

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“…16,32,48 Furthermore, models exist to predict the pressure losses over junctions. 31,32,85 However, in our preliminary studies, these did not lead to a significant improvement in approximation quality and were thus not included. Furthermore, we found little difference in accuracy over preserving total versus static pressure.…”

Section: Discussionmentioning

confidence: 99%

“…Other models aim to preserve total pressure, that is, static plus dynamic pressure 16,32,48 . Furthermore, models exist to predict the pressure losses over junctions 31,32,85 . However, in our preliminary studies, these did not lead to a significant improvement in approximation quality and were thus not included.…”

Section: Discussionmentioning

confidence: 99%

Automated generation of 0D and 1D reduced‐order models of patient‐specific blood flow

Pfaller

Pham

Verma

et al. 2022

Numer Methods Biomed Eng

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Three-dimensional (3D) cardiovascular fluid dynamics simulations typically require hours to days of computing time on a high-performance computing cluster.One-dimensional (1D) and lumped-parameter zero-dimensional (0D) models show great promise for accurately predicting blood bulk flow and pressure waveforms with only a fraction of the cost. They can also accelerate uncertainty quantification, optimization, and design parameterization studies. Despite several prior studies generating 1D and 0D models and comparing them to 3D solutions, these were typically limited to either 1D or 0D and a singular category of vascular anatomies. This work proposes a fully automated and openly available framework to generate and simulate 1D and 0D models from 3D patient-specific geometries, automatically detecting vessel junctions and stenosis segments. Our only input is the 3D geometry; we do not use any prior knowledge from 3D simulations. All computational tools presented in this work are implemented in the opensource software platform SimVascular. We demonstrate the reduced-order approximation quality against rigid-wall 3D solutions in a comprehensive comparison with N = 72 publicly available models from various anatomies, vessel types, and disease conditions. Relative average approximation errors of flows and pressures typically ranged from 1% to 10% for both 1D and 0D models, measured at the outlets of terminal vessel branches. In general, 0D model errors were only slightly higher than 1D model errors despite requiring only a third of the 1D runtime. Automatically generated ROMs can significantly speed up model development and shift the computational load from high-performance machines to personal computers.

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Impact of inertia and channel angles on flow distribution in microfluidic junctions

Cited by 10 publications

References 39 publications

SMILE Platform: An Innovative Microfluidic Approach for On-Chip Sample Manipulation and Analysis in Oral Cancer Diagnosis

SMILE Platform: An Innovative Microfluidic Approach for On-Chip Sample Manipulation and Analysis in Oral Cancer Diagnosis

Automated generation of 0D and 1D reduced-order models of patient-specific blood flow

Automated generation of 0D and 1D reduced‐order models of patient‐specific blood flow

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