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42 results for “Computational fluid dynamics”
Improvements in airflow characteristics and effect on the NOSE score after septoturbinoplasty: A computational fluid dynamics analysis
<p><span>Septoturbinoplasty is a surgical procedure that can improve nasal congestion symptoms in patients with nasal septal deviation and inferior turbinate hypertrophy. However, it is unclear which physical domains of nasal airflow after septoturbinoplasty are related to symptomatic improvement. This work employs computational fluid dynamics modeling to identify the physical variables and domains associated with symptomatic improvement. Sixteen numerical models were generated using eight patients' pre- and postoperative computed tomography scans. Changes in unilateral nasal resistance, surface heat flux, relative humidity, and air temperature and their correlations with improvement in the </span><span>Nasal Obstruction Symptom Evaluation (NOSE) score were analyzed. The NOSE score significantly improved after septoturbinoplasty, from 14.4 ± 3.6 to 4.0 ± 4.2 (p < 0.001). The surgery not only increased the airflow partition </span><span>on the more obstructed side (MOS) from 31.6 ± 9.6 to 41.9 ± 4.7% (p = 0.043), but also reduced the unilateral nasal resistance in the MOS from 0.200 ± 0.095 to 0.066 ± 0.055 Pa/(mL</span><span>×</span><span>s) (p = 0.004). Improvement in the NOSE score correlated significantly with the reduction in unilateral nasal resistance in the preoperative MOS (<em>r</em>=0.81). Also, improvement in the NOSE score correlated better with the increase in surface heat flux in the preoperative MOS region from the nasal valve to the choanae (<em>r</em>=0.87) than in the vestibule area (<em>r</em>=0.63). Therefore, </span><span>unilateral nasal resistance and mucous cooling in the preoperative MOS can explain the perceived improvement in symptoms after septoturbinoplasty. Moreover, the physical domain between the nasal valve and the choanae might be more relevant to patient-reported patency than the vestibule area. </span></p>
Test Model for GTT Computational Fluid Dynamics Simulation by TransAT
<p>This archive contains the model and associated code need to run the GTT benchmark test for TransAT.</p>
Implementing amplified MRI in a computational fluid dynamics model of the cerebrospinal fluid
<div> <p>This folder respository contains files to implement measured amplified MRI motion into a CFD model. The files should be imported in FLUENT (Ansys Inc.) in the following order:</p> <ol> <li><strong>setup_case.jou</strong>: journal file containing the commands to setup the CFD model in FLUENT (Ansys Inc.). This file also compiles and loads the UDF <strong>mesh_motion_all_v2023.c</strong>. The UDF is directly based on the code provided in the Github respiratory (<a href="https://pyfsi.github.io/coconut/">CoCoNuT</a>). A define-on-demand function should then be executed to export the unique node ids from FLUENT. </li> <li>Run the Python file <strong>link_node_id_spline_interpolation.ipynb</strong> which returns .dat files for the different timesteps. </li> <li><strong>run_motion.jou</strong>: journal file that reads the .dat files generated in step 2 and applies the mesh motion in the CFD model.</li> </ol> </div>
PROGRESS IN COUPLING COMPUTATIONAL THERMODYNAMICS AND COMPUTATIONAL FLUID DYNAMICS TO SUPPORT MOLTEN SALT REACTOR APPLICATIONS (Revised)
<p>This research was undertaken, in part, thanks to funding from the Canada Research Chairs program and the Discovery Grant Program of the Natural Sciences and Engineering Research Council of Canada. This study is in direct support of the SAMOSAFER project.</p> <p> </p>
Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary CT Angiography and Computational Fluid Dynamics II (EMERALD II) Study
ClinicalTrials.gov study NCT03591328. IPD Sharing: UNDECIDED. Countries: 1. Publications: 21.
Exploring the MEchanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary CT Angiography and computationaL Fluid Dynamic
ClinicalTrials.gov study NCT02374775. IPD Sharing: YES. Countries: 1. Publications: 1.
CT Coronary Angiography and Computational Fluid Dynamics
ClinicalTrials.gov study NCT01189331. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Study of Carotid Artery Stenosis Through the Integration of Multimodal Imaging and Computational Fluid Dynamics
ClinicalTrials.gov study NCT06294496. IPD Sharing: YES. Countries: 1. Publications: 8.
Effect of space diffuser on flow characteristics of a centrifugal pump by computational fluid dynamic analysis
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Improvements in airflow characteristics and effect on the NOSE score after septoturbinoplasty: A computational fluid dynamics analysis
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Fluid-structure simulations outperform computational fluid dynamics in the differentiation of progressive dilation in Marfan syndrome patients
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Data from: Force and torque on spherical particles in micro-channel flows using computational fluid dynamics
To delineate the influence of hemodynamic force on cell adhesion processes, model in vitro fluidic assays that mimic physiological conditions are commonly employed. Herein, we offer a framework for solution of the three-dimensional Navier–Stokes equations using computational fluid dynamics (CFD) to estimate the forces resulting from fluid flow near a plane acting on a sphere that is either stationary or in free flow, and we compare these results to a widely used theoretical model that assumes Stokes flow with a constant shear rate. We find that while the full three-dimensional solutions using a parabolic velocity profile in CFD simulations yield similar translational velocities to those predicted by the theoretical method, the CFD approach results in approximately 50% larger rotational velocities over the wall shear stress range of 0.1–5.0 dynes cm−2. This leads to an approximately 25% difference in force and torque calculations between the two methods. When compared with experimental measurements of translational and rotational velocities of microspheres or cells perfused in microfluidic channels, the CFD simulations yield significantly less error. We propose that CFD modelling can provide better estimations of hemodynamic force levels acting on perfused microspheres and cells in flow fields through microfluidic devices used for cell adhesion dynamics analysis.
Datasets for Computational Fluid Dynamics
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FIGURE 2 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 2. All of the shells employed in this study plotted in Westermann morphospace. The 3D models used for each member are shown to the right of the name/designation in the legend. Cardioceras, Oppelia, and Sphenodiscus are based on the shells employed in Jacobs (1992), Nautilus pompelius was created using laser scans and scaled to two different sizes (life-size [approx. 14.5 cm] and 5 cm diameter), and all other shells were created to match basic Westermann morphotypes (Westermann, 1996; Ritterbush and Bottjer, 2012).
Data from: Force and torque on spherical particles in micro-channel flows using computational fluid dynamics
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Computed Tomography (CT) and Lung Function Data Collection for Computational Fluid Dynamics (CFD) in Chronic Obstructive Pulmonary Disease (COPD) Patients
ClinicalTrials.gov study NCT00966459. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Improving Outcomes in Pediatric Obstructive Sleep Apnea With Computational Fluid Dynamics
ClinicalTrials.gov study NCT04991389. IPD Sharing: NO. Countries: 1. Publications: 0.
Prediction of Vulnerable Plaque Using Coronary CT Angiography and Computational Fluid Dynamic in Acute Coronary Syndrome
ClinicalTrials.gov study NCT04524117. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Computational Fluid Dynamics Predicting Intraprosthetic Thrombus Formation and Progression Following EVAR
ClinicalTrials.gov study NCT06701773. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Limited depth in bone defect-combining computational fluid dynamics and genes sequence analysis due to osteogenic effect under negative pressure wound therapy
GEO Series GSE216691. Mus musculus. 5 samples. Type: Expression profiling by high throughput sequencing.
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