The human heart is a highly complex organ, and its primary function is to pump blood through the arteries, veins and to perfuse all other body tissues and organs, including itself. In the last decade, cardiac simulations have become increasingly crucial to gain clinical insight into cardiac function, treatment, and testing. Nowadays, multi-physics cardiovascular simulations applied to patient-specific modeling can help in the diagnosis of cardiovascular diseases and in studying relevant clinical treatments. Hence, our central objective here is to develop a generalized multi-physics finite element (FE) framework that includes thermal-fluid structure interaction coupling to study cardiac function and treatments. First, we developed a stabilized FE based flow solver with heat transfer to study hemodynamics. A python based open-source FE library (FEniCS) is used from ground-up to custom-build the solver. We benchmark and validate the solver and study convergence for classical test cases at intermediate Reynolds and Peclet number. Second, we utilize the solver to investigate cryoballoon ablation (CBA), which is a minimally invasive surgery that uses freezing or cryoenergy to treat atrial fibrillation (AF). To begin with, we use a patient-specific left atrium (LA) geometry and realistic pulmonary vein (PV) blood flow boundary conditions to validate hemodynamics of the LA chamber. Next, we position a cryoballoon (CB) at the pulmonary vein ostium to simulate incomplete occlusion during cryotherapy and investigate the factors affecting lesion formation. We observe that lesion size is highly sensitive to the CB position and balloon tissue contact area. The threshold gap for lesion formation is 2.4 mm. We also note that as the balloon tissue contact area increases, the surgery is more effective, and the power absorbed across the CB reduces. Third, we extend our development to a fully coupled fluid-structure interaction (FSI) solver with heat transfer using FEniCS. The FSI solver (named vanDANA) that uses the immersed boundary (IB) method is based on the Distributed Lagrange Multiplier based Fictitious Domain method and the interpolation of variables is conducted using the smeared delta-functions. Additionally, the structure can be set as incompressible or compressible. We benchmark our solver and analyze the scalability on HPC. This builds a solid foundation for the future use of this solver.
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