A fast approach to estimating Windkessel model parameters for patient-specific multi-scale CFD simulations of aortic flow

Computational fluid dynamics (CFD) study of hemodynamics in the aorta can provide a comprehensive analysis of relevant cardiovascular diseases. One trending approach is to couple the three-element Windkessel model with patient-specific CFD simulations to form a multi-scale model that captures more realistic flow fields. However, case-specific parameters (e.g., Rc, Rp, and C) for the Windkessel model must be tuned to reflect patient-specific flow conditions. In this study, we propose a fast approach to estimate these parameters under both physiological and pathological conditions. The approach consists of the following steps: (1) finding geometric resistances for each branch using steady CFD simulation; (2) using the pattern search algorithm from Matlab toolbox 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 flexibility and robustness, including physiological and pathological flow distributions at each downstream branch from healthy or stenosed aortic geometries. This study demonstrates a flexible and computationally efficient way to capture patient-specific hemodynamics in the aorta, facilitating personalized biomechanical analysis of aortic flow.

Automated summary

Computational fluid dynamics (CFD) study of hemodynamics in the aorta can provide a comprehensive analysis of relevant cardiovascular diseases. One trending approach is to couple the three-element Windkessel model with patient-specific CFD simulations to form a multi-scale model that captures more realistic flow fields. However, case-specific parameters (e.g., Rc, Rp, and C) for the Windkessel model must be tuned to reflect patient-specific flow conditions.