Global ETD Search

1	Simulación de la Hemodinámica en Modelos de Aneurismas Cerebrales Incluyendo la Interacción Fluido-Estructura Araya Aburto, Sebastián Andrés January 2008 (has links) No description available. Mecánica Simulación hemodinámica aneurismas FSI CFD esfuerzo de corte esfuerzo efectivo womersley VRML
2	Design of a Bioreactor to Mimic Hemodynamic Shear Stresses on Endothelial Cells in Microfluidic Systems Lightstone, Noam S. 26 June 2014 (has links) The mechanisms behind cardiovascular disease (CVD) initiation and progression are not fully elucidated. It is hypothesized that blood flow patterns regulate endothelial cell (EC) function to affect the progression of CVDs. A system that subjects ECs to physiologically-relevant shear stress waveforms within microfluidic devices has not yet been demonstrated, despite the advantages associated with the use of these devices. In this work, a bioreactor was designed to fulfill this need. Waveforms from regions commonly affected by CVDs including were derived. Pump motion and fluid flow profiles were validated by actuator motion tracking, particle image velocimetry, and flowmeters. While several relevant waveforms were successfully replicated, physiological waveforms could not be produced at physiological frequencies owing to actuator velocity and accuracy limitations, as well as dampening effects in the system. Overall, this work lays the foundation for designing a system that provides insight into the role of shear stress in CVD pathogenesis. Endothelial cell Bioreactor Flow loop Cardiovascular disease CVD Waveform Microfluidics Microfluidic devices Hemodynamics EC Shear stress Shear Pump Actuator Mechanical engineering Biomedical engineering PIV Particle image velocimetry Fluid dynamics Fluid mechanics Pressure Model Syringe Peristaltic Blood Blood flow Wall shear stress Womersley number Womersley Purday Physiological Flowmeter Sinusoid Sinusoidal Disturbed Non-disturbed Undisturbed Oscillatory Pulsatile In vivo In vitro 0548 0541
3	Design of a Bioreactor to Mimic Hemodynamic Shear Stresses on Endothelial Cells in Microfluidic Systems Lightstone, Noam S. 26 June 2014 (has links) The mechanisms behind cardiovascular disease (CVD) initiation and progression are not fully elucidated. It is hypothesized that blood flow patterns regulate endothelial cell (EC) function to affect the progression of CVDs. A system that subjects ECs to physiologically-relevant shear stress waveforms within microfluidic devices has not yet been demonstrated, despite the advantages associated with the use of these devices. In this work, a bioreactor was designed to fulfill this need. Waveforms from regions commonly affected by CVDs including were derived. Pump motion and fluid flow profiles were validated by actuator motion tracking, particle image velocimetry, and flowmeters. While several relevant waveforms were successfully replicated, physiological waveforms could not be produced at physiological frequencies owing to actuator velocity and accuracy limitations, as well as dampening effects in the system. Overall, this work lays the foundation for designing a system that provides insight into the role of shear stress in CVD pathogenesis. Endothelial cell Bioreactor Flow loop Cardiovascular disease CVD Waveform Microfluidics Microfluidic devices Hemodynamics EC Shear stress Shear Pump Actuator Mechanical engineering Biomedical engineering PIV Particle image velocimetry Fluid dynamics Fluid mechanics Pressure Model Syringe Peristaltic Blood Blood flow Wall shear stress Womersley number Womersley Purday Physiological Flowmeter Sinusoid Sinusoidal Disturbed Non-disturbed Undisturbed Oscillatory Pulsatile In vivo In vitro 0548 0541
4	Image Segmentation, Parametric Study, and Supervised Surrogate Modeling of Image-based Computational Fluid Dynamics Islam, Md Mahfuzul 05 1900 (has links) Indiana University-Purdue University Indianapolis (IUPUI) / With the recent advancement of computation and imaging technology, Image-based computational fluid dynamics (ICFD) has emerged as a great non-invasive capability to study biomedical flows. These modern technologies increase the potential of computation-aided diagnostics and therapeutics in a patient-specific environment. I studied three components of this image-based computational fluid dynamics process in this work. To ensure accurate medical assessment, realistic computational analysis is needed, for which patient-specific image segmentation of the diseased vessel is of paramount importance. In this work, image segmentation of several human arteries, veins, capillaries, and organs was conducted to use them for further hemodynamic simulations. To accomplish these, several open-source and commercial software packages were implemented. This study incorporates a new computational platform, called InVascular, to quantify the 4D velocity field in image-based pulsatile flows using the Volumetric Lattice Boltzmann Method (VLBM). We also conducted several parametric studies on an idealized case of a 3-D pipe with the dimensions of a human renal artery. We investigated the relationship between stenosis severity and Resistive index (RI). We also explored how pulsatile parameters like heart rate or pulsatile pressure gradient affect RI. As the process of ICFD analysis is based on imaging and other hemodynamic data, it is often time-consuming due to the extensive data processing time. For clinicians to make fast medical decisions regarding their patients, we need rapid and accurate ICFD results. To achieve that, we also developed surrogate models to show the potential of supervised machine learning methods in constructing efficient and precise surrogate models for Hagen-Poiseuille and Womersley flows. Image-based Computational Fluid Dynamics Segmentation Machine Learning Surrogate Model Biomedical Fluid Stenosis Resistive Index 3D Slicer Choroid SEM CT MRI Lattice Boltzmann Method Gaussian Process Regression DACE Pulsatile Flow Hagen-Poiseuille Flow Womersley Flow Siemens NX SolidWorks
5	Properties of Flow Through the Ascending Aorta in Boxer Dogs with Mild Aortic Stenosis: Momentum, Energy, Reynolds Number, Womersley’s, Unsteadiness Parameter, Vortex Shedding, and Transfer Function of Oscillations from Aorta to Thoracic Wall da Cunha, Daise Nunes Queiroz 02 September 2009 (has links) No description available. Acoustics Anatomy and Physiology Animals Fluid Dynamics Radiology Boxer dogs systolic ejection murmur mild aortic stenois fluid mechanics Reynolds number Womersley parameter Vorticies Kinetic energy momentum Dobutamine aucultation catheterization acoustics Transfer function heart sounds frequency and amplit
6	IMAGE SEGMENTATION, PARAMETRIC STUDY, AND SUPERVISED SURROGATE MODELING OF IMAGE-BASED COMPUTATIONAL FLUID DYNAMICS MD MAHFUZUL ISLAM (12455868) 12 July 2022 (has links) <p> </p> <p>With the recent advancement of computation and imaging technology, Image-based computational fluid dynamics (ICFD) has emerged as a great non-invasive capability to study biomedical flows. These modern technologies increase the potential of computation-aided diagnostics and therapeutics in a patient-specific environment. I studied three components of this image-based computational fluid dynamics process in this work.</p> <p>To ensure accurate medical assessment, realistic computational analysis is needed, for which patient-specific image segmentation of the diseased vessel is of paramount importance. In this work, image segmentation of several human arteries, veins, capillaries, and organs was conducted to use them for further hemodynamic simulations. To accomplish these, several open-source and commercial software packages were implemented. </p> <p>This study incorporates a new computational platform, called <em>InVascular</em>, to quantify the 4D velocity field in image-based pulsatile flows using the Volumetric Lattice Boltzmann Method (VLBM). We also conducted several parametric studies on an idealized case of a 3-D pipe with the dimensions of a human renal artery. We investigated the relationship between stenosis severity and Resistive index (RI). We also explored how pulsatile parameters like heart rate or pulsatile pressure gradient affect RI.</p> <p>As the process of ICFD analysis is based on imaging and other hemodynamic data, it is often time-consuming due to the extensive data processing time. For clinicians to make fast medical decisions regarding their patients, we need rapid and accurate ICFD results. To achieve that, we also developed surrogate models to show the potential of supervised machine learning methods in constructing efficient and precise surrogate models for Hagen-Poiseuille and Womersley flows.</p> Flow analysis Biomechanical engineering Image-based COMPUTATIONAL FLUID DYNAMICS Segmentation Machine Learning Surrogate model CT MRI SEM 3D Slicer MATLAB Gaussian Process Regression DACE Lattice Boltzmann Method Hagen-Poiseuille Womersley Pulsatile flow Mechanical Engineering Computational Fluid Dynamics Flow Analysis Biomechanical Engineering

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