We find using dissipative particle dynamics (DPD) simulations that a deformable droplet sheared in a narrow microchannel migrates to steady-state position that depends upon the dimensionless particle capillary number , which controls the droplet deformability (with V the centerline velocity, μ the fluid viscosity, Γ the surface tension, R the droplet radius, and H the gap), the droplet (particle) Reynolds number , which controls inertia, where ρ is the fluid density, as well as on the viscosity ratio of the droplet to the suspending fluid κ = μ/μ. We find that when the Ohnesorge number is around 0.06, so that inertia is stronger than capillarity, at small capillary number Ca < 0.1, the droplet migrates to a position close to that observed for hard spheres by Segre and Silberberg, around 60% of the distance from the centerline to the wall, while for increasing Ca the droplet steady-state position moves smoothly towards the centerline, reaching around 20% of the distance from centerline to the wall when Ca reaches 0.5 or so. For higher Oh, the droplet position is much less sensitive to Ca, and remains at around 30% of the distance from centerline to the wall over the whole accessible range of Ca. The results are insensitive to viscosity ratios from unity to the highest value studied here, around 13, and the drift towards the centerline for increasing Ca is observed for ratios of droplet diameter to gap size ranging from 0.1 to 0.3. We also find consistency between our predictions and existing perturbation theory for small droplet or particle size, as well as with experimental data. Additionally, we assess the accuracy of the DPD method and conclude that with current computer resources and methods DPD is not readily able to predict cross-stream-line drift for small particle Reynolds number (much less than unity), or for droplets that are less than one tenth the gap size, owing to excessive noise and inadequate numbers of DPD particles per droplet.
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http://dx.doi.org/10.1039/c7sm02294h | DOI Listing |
JTCVS Open
December 2024
Department of Cardiovascular Surgery, Seirei Mikatahara General Hospital, Hamamatsu, Japan.
Objective: A novel approach to 3-dimensional morphometry of the thoracic aorta was developed by applying centerline analysis based on least-squares plane fitting, and a preliminary study was conducted using computed tomography imaging data.
Methods: We retrospectively compared 3 groups of patients (16 controls without aortic disease, and 16 cases each with acute type B aortic dissection and congenital bicuspid aortic valve). In addition to the standard assessment indices for curvature κ and torsion τ, we conducted coordinate transformation based on the least-squares plane, divided the centerline into 3 representative features (transverse, anterior-posterior, and longitudinal displacements), and analyzed the overall and local displacement in each direction.
Int J Comput Assist Radiol Surg
December 2024
Department of Surgical Oncology, Netherlands Cancer Institute, 1066 CX, Amsterdam, The Netherlands.
Purpose: Surgical navigation aids surgeons in localizing and adequately resecting pelvic malignancies. Accuracy of the navigation system highly depends on the preceding registration procedure, which is generally performed using intraoperative fluoroscopy or CT. However, these ionizing methods are time-consuming and peroperative updates of the registration are cumbersome.
View Article and Find Full Text PDFAJNR Am J Neuroradiol
November 2024
From the Department of Neurosurgery and Clinical Neurocenter, University Hospital of Zürich, Switzerland (EC, LPR, TPCvD), The Image Sciences Institute, Division of Imaging and Oncology, University Medical Centre Utrecht, The Netherlands (MdB, LWB).
J Med Imaging (Bellingham)
November 2024
University of Copenhagen, Department of Computer Science, Copenhagen, Denmark.
Purpose: Pancreatic ductal adenocarcinoma is forecast to become the second most significant cause of cancer mortality as the number of patients with cancer in the main duct of the pancreas grows, and measurement of the pancreatic duct diameter from medical images has been identified as relevant for its early diagnosis.
Approach: We propose an automated pancreatic duct centerline tracing method from computed tomography (CT) images that is based on deep reinforcement learning, which employs an artificial agent to interact with the environment and calculates rewards by combining the distances from the target and the centerline. A deep neural network is implemented to forecast step-wise values for each potential action.
Eur Radiol Exp
November 2024
State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, 100101, Beijing, China.
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