A scalable Euler-Lagrange strategy for particle-laden anatomical flows in subject-specific geometries

We develop an Euler-Lagrange method for large scale particle-laden biological flows in subject-specific geometries. Towards that end, the CRIMSON cardiovascular flow modeling framework is extended to include Lagrangian particles that interact with each other and are coupled with the fluid. CRIMSON solves the incompressible Navier-Stokes equations using a stabilized finite-element method with equal order interpolation for velocity and pressure on unstructured grids. The complex morphology of the vasculature presents unique challenges in implementing scalable particle tracking and collision algorithms. Due to the significant unoccupied space in a bounding box, purely Cartesian-based particle collision acceleration schemes are not memory efficient. Here, we propose an efficient hash-table based cell-list to accelerate particle collision detection. Additionally, efficient procedures to initialize and inject non-overlapping particles in arbitrary geometries are developed. The initialization procedure uses a fast-marching level set method to generate a signed distance field to accelerate particle seeding. Poisson disc sampling with linear complexity is employed to initialize the particles.