Fluid-Structure Interaction Analysis of Carotid Artery Blood Flow with Machine Learning Algorithm and OpenFOAM

In this study, a patient-specific carotid artery model was analyzed with an open source program foam-extend. The research includes the effect of arterial wall deformation by fluid-structure analysis. Pulsatile velocity cycle is trained for 144 patients different hemodynamic parameters, machine learning algorithm using blood flow measured from 337 points artery. Data used training obtained in literature. Here, created help code Phyton. Then, values learning, and known systole diastole pressures specific chosen patient, pulsatile estimated. estimated then fitted to Fourier series. This as input boundary condition outlet condition, pressure found 4-Element Windkessel algorithm. Wall shear stresses time averaged were both rigid fluid structure interaction models, variation displacement throughout FSI model. stresses, velocity, displacements high at peak systole, consistent cycles. Like also lower than showed increase towards exit internal external