Physics-Driven CFD Modeling of Complex Anatomical Cardiovascular Flows—A TCPC Case Study

Recent developments in medical image acquisition combined with the latest advancements in numerical methods for solving the Navier-Stokes equations have created unprecedented opportunities for developing simple and reliable computational fluid dynamics (CFD) tools for meeting patient-specific surgical planning objectives. However, for CFD to reach its full potential and gain the trust and confidence of medical practitioners, physics-driven numerical modeling is required. This study reports on the experience gained from an ongoing integrated CFD modeling effort aimed at developing an advanced numerical simulation tool capable of accurately predicting flow characteristics in an anatomically correct total cavopulmonary connection (TCPC). An anatomical intra-atrial TCPC model is reconstructed from a stack of magnetic resonance (MR) images acquired in vivo. An exact replica of the computational geometry was built using transparent rapid prototyping. Following the same approach as in earlier studies on idealized models, flow structures, pressure drops, and energy losses were assessed both numerically and experimentally, then compared. Numerical studies were performed with both a first-order accurate commercial software and a recently developed, second-order accurate, in-house flow solver. The commercial CFD model could, with reasonable accuracy, capture global flow quantities of interest such as control volume power losses and pressure drops and time-averaged flow patterns. However, for steady inflow conditions, both flow visualization experiments and particle image velocimetry (PIV) measurements revealed unsteady, complex, and highly 3D flow structures, which could not be captured by this numerical model with the available computational resources and additional modeling efforts that are described. Preliminary time-accurate computations with the in-house flow solver were shown to capture for the first time these complex flow features and yielded solutions in good agreement with the experimental observations. Flow fields obtained were similar for the studied total cardiac output range (1–3 l/min); however hydrodynamic power loss increased dramatically with increasing cardiac output, suggesting significant energy demand at exercise conditions. The simulation of cardiovascular flows poses a formidable challenge to even the most advanced CFD tools currently available. A successful prediction requires a two-pronged, physics-based approach, which integrates high-resolution CFD tools and high-resolution laboratory measurements.