UC Berkeley

Cardiovascular disease (CVD) is the leading cause of death worldwide. Image-based computational fluid dynamic (CFD) simulations have been widely used to characterize patient-specific blood flow features in the cardiovascular system. The simulation results provide high spatiotemporal resolution of functional indicators of CVD not revealed through imaging. Moreover, computational modeling provides the ability to virtually test interventions or devices. Despite this upside, patient-specific simulations remain computationally expensive, prone to numerical instabilities, and can be sensitive to method parameters, all of which have limited their clinical use. These limitations have also been prohibitive in research applications requiring parametric analyses such as uncertainty quantification, data assimilation, optimization, parameter tuning, etc. These factors motivate the need for reduced-order modeling (ROM) of blood flow to be used in place of, or in conjunction with, fully-resolved CFD modeling.

As such, this dissertation focuses on developing ROMs to efficiently compute blood flow in cardiovascular domains that can enable timely decision support, and applications requiring ensembles of numerical simulations. Through fundamental understanding of physics and physiology, we developed a novel physics-based ROM strategy, that not only calculates blood flow and pressure at a fraction of the computational cost compared to the current standard CFD, but can do so much more accurately than other existing ROM methods. The proposed method demonstrated consistent agreement with CFD simulations in terms of flow rate and pressure distribution over a broad range of hemodynamic conditions and vascular geometries. To evaluate the utility of this ROM methodology, we applied this framework to several translational and research applications.