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Subclinical Atherosclerosis Quantified Through Cumulative Shear Measurement

With the high mortality rate of cardiovascular disease, it is important to study the early signs. The early detection of cardiovascular disease can lead to saved lives. Currently the most prevalent detection methods are the Framingham Risk Score and the carotid intima media thickness, both of which are insufficient. The necessary tool for early detection requires a uniform quantification system. The stimulus leading to endothelial dysfunction, the most significant predictor of a major adverse cardiovascular event (MACE)—and subsequently subclinical atherosclerosis—is reduced shear stress. Increased surface relative roughness affects the flow profile transition from laminar to turbulent resulting in reduced shear rate. The relationship between the shear stress and the relative roughness was studied using a computer model for fluid flow. A model of the brachial artery was generated to study its hemodynamics. Roughness values for both laminar and turbulent flow were calculated to use with the governing equations programmed in COMSOL Multiphysics. With all other factors remaining constant in the model, the roughness values were changed. From the model profile plots, line graphs, and numeral data are generated. This data provides information about how the shear stress and the shear rate change with respect to the relative roughness value. The models with different wall boundary conditions—slip versus Navier slip—were unable to be directly compared due to the differences in value magnitude. When the flow profile transitions from laminar to turbulent, there is a corresponding drop in both the shear stress and the shear rate values. Additional testing is required to determine a critical relative roughness value for this change in cumulative shear.

Identiferoai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-3928
Date01 August 2021
CreatorsPapka, Margaret Lynne
PublisherDigitalCommons@CalPoly
Source SetsCalifornia Polytechnic State University
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceMaster's Theses

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