Numerical Modeling of Flow in Complex Arterial Geometries using FreeFem ++

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P. Carvalho, V. de O. Carvalho, V. L. Braga, O. P. de Sá Neto This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3835167/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study performs a numerical analysis of the flow in a common thoracic aortic artery, to model the experimental results of fluid flow, with emphasis on volumetric velocity input data. We used the open-source software FreeFem++ to encode the data, the anatomical arterial geometries were collected from Vascular Model Repository, and Paraview as a post-processing tool.The mathematical model adopted is based on the incompressible Navier-Stokes equations under Dirichlet-Neumann boundary conditions, solved numerically using the Finite Element Method, Finite Difference Method, and Method of Characteristics. The resulting computer simulations systematically examine various parameters associated with blood flow, particularly mean pressure and flow characteristics at the inlet and outlet surfaces Physical sciences/Physics/Fluid dynamics Health sciences/Cardiology/Cardiovascular biology Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3835167","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":268313719,"identity":"1958d045-2e49-4f63-b9a5-350514e7e419","order_by":0,"name":"P. P. 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