  Hemodynamics in the aorta from computational fluid dynamics (CFD) simulations
can provide a comprehensive analysis of relevant cardiovascular diseases.
Coupling the three-element Windkessel model with the patient-specific CFD
simulation to form a multi-scale model is a trending approach to capture more
realistic flow fields. However, a set of parameters (e.g., R_c, R_p, and C) for
the Windkessel model need to be tuned case by case to reflect patient-specific
flow conditions. In this study, we propose a fast approach to estimating these
parameters under both physiological and pathological conditions. The approach
consists of the following steps: (1) finding geometric resistances for each
branch using a steady CFD simulation; (2) using the pattern search algorithm to
search the parameter spaces by solving the flow circuit system with the
consideration of geometric resistances; (3) performing the multi-scale modeling
of aortic flow with the optimized Windkessel model parameters. The method was
validated through a series of numerical experiments to show its flexibility and
robustness, including physiological and pathological flow distributions at each
downstream branch from a healthy aortic geometry or a stenosed geometry. This
study demonstrates a flexible and computationally efficient way to capture
patient-specific hemodynamics in the aorta, facilitating the personalized
biomechanical analysis of aortic flow.
