Evidence-based medicine is at the heart of current medical practice where clinical decisions are driven by research data. However, most current therapy recommendations follow generalized protocols and guidelines that are based on epidemiological (population) studies and thus not suited for the individual patient's demands. Patient-tailored therapies are considered, hence, an unmet clinical need. We believe that mathematical models of the physiology can attend to such a clinical need, because they can be tuned to the individual patient. Such models provide a sound mathematical framework for personalized clinical decisions. In particular, physiological models in medicine can serve the following two purposes: 1) They can be an efficient tool to quantify cardiopulmonary dynamics, conduct virtual clinical/physiological experiments, and investigate the effects of specific treatments. 2) Model-based estimation techniques can assess physiological parameters or variables, which are otherwise impractical or dangerous to measure; they can effectively tune a generic model to become patient-specific, able to mimic the behavior of a particular patient. In this thesis, we propose a series of modifications to a previously developed cardiopulmonary model (CP Model) in order to better replicate heart-lung interaction phenomena that are typically observed under mechanical ventilation, hence allowing for a more accurate analysis of ventilation-induced changes in cardiac function. The response of this modified model is validated with experimental data collected during mechanical ventilation conditions. Further, as an industrial application of mathematical models, we present a patient emulator system that comprises the modified CP Model, a physical ventilator, and a piston-cylinder arrangement that serves as an electrical-to-hydraulic transducer. The modified CP Model then serves as the virtual patient that is being ventilated, where disease conditions can be instilled. Such a system is designed to offer a well-controlled experimental environment for ventilator manufacturers to efficaciously test and compare ventilation modalities and therapies, thereby enhancing their verification and validation manufacturing processes. Finally, we develop a model-based approach to estimate (noninvasively) the function of the cardiovascular system, in terms of cardiac performance (i.e., cardiac output) and the dynamics of the systemic arterial tree (i.e., time constant). With this technique, we envision to provide continuous and real-time bedside monitoring of changes in cardiovascular function, such as those induced by changes in ventilator settings.

Indiana University-Purdue University Indianapolis (IUPUI) / Atherosclerotic lesions of human beings are common diagnosed in regions of arte- rial branching and curvature. The prevalence of atherosclerosis is usually associated with hardening and ballooning of aortic wall surfaces because of narrowing of flow path by the deposition of fatty materials, platelets and influx of plasma through in- timal wall of Aorta. High Wall Shear Stress (WSS) is proved to be the main cause behind all these aortic diseases by physicians and researchers. Due to the fact that the atherosclerotic regions are associated with complex blood flow patterns, it has believed that hemodynamics and fluid-structure interaction play important roles in regulating atherogenesis. As one of the most complex flow situations found in cardio- vascular system due to the strong curvature effects, irregular geometry, tapering and branching, and twisting, theoretical prediction and in vivo quantitative experimental data regarding to the complex blood flow dynamics are substantial paucity. In recent years, computational fluid dynamics (CFD) has emerged as a popular research tool to study the characteristics of aortic flow and aim to enhance the understanding of the underlying physics behind arteriosclerosis. In this research, we study the hemo- dynamics and flow-vessel interaction in patient specific normal (healthy) and dilated (diseased) aortas using Ansys-Fluent and Ansys-Workbench. The computation con- sists of three parts: segmentation of arterial geometry for the CFD simulation from computed tomography (CT) scanning data using MIMICS; finite volume simulation of hemodynamics of steady and pulsatile flow using Ansys-Fluent; an attempt to perform the Fluid Structure Simulation of the normal aorta using Ansys-Workbench. Instead of neglecting the branching or smoothing out the wall for simplification as a lot of similar computation in literature, we use the exact aortic geometry. Segmen- tation from real time CT images from two patients, one young and another old to represent healthy and diseased aorta respectively, is on MIMICS. The MIMICS seg- mentation operation includes: first cropping the required part of aorta from CT dicom data of the whole chest, masking of the aorta from coronal, axial and saggital views of the same to extract the exact 3D geometry of the aorta. Next step was to perform surface improvement using MIMICS 3-matic module to repair for holes, noise shells and overlapping triangles to create a good quality surface of the geometry. A hexahe- dral volume mesh was created in T-Grid. Since T-grid cannot recognize the geometry format created by MIMICS 3-matic; the required step geometry file was created in Pro-Engineer. After the meshing operation is performed, the mesh is exported to Ansys Fluent to perform the required fluid simulation imposing adequate boundary conditions accordingly. Two types of study are performed for hemodynamics. First is a steady flow driven by specified parabolic velocity at inlet. We captured the flow feature such as skewness of velocity around the aortic arch regions and vortices pairs, which are in good agreement with open data in literature. Second is a pulsatile flow. Two pulsatile velocity profiles are imposed at the inlet of healthy and diseased aorta respectively. The pulsatile analysis was accomplished for peak systolic, mid systolic and diastolic phase of the entire cardiac cycle. During peak systole and mid-systole, high WSS was found at the aortic branch roots and arch regions and diastole resulted in flow reversals and low WSS values due to small aortic inflow. In brief, areas of sudden geometry change, i.e. the branch roots and irregular surfaces of the geom- etry experience more WSS. Also it was found that dilated aorta has more sporadic nature of WSS in different regions than normal aorta which displays a more uniform WSS distribution all over the aorta surface. Fluid-Structure Interaction simulation is performed on Ansys-WorkBench through the coupling of fluid dynamics and solid mechanics. Focus is on the maximum displacement and equivalent stress to find out the future failure regions for the peak velocity of the cardiac cycle.