Parallel algorithms for modelling blood flows in the human cardiovascular system with clinical accuracy
Xiao-Chuan Cai, University of Macau and University of Colorado Boulder
The numerical simulation of blood flows in the human body with a certain level of clinical accuracy is important for the understanding of the human physiology. The success of the modeling relies on a robust numerical method with the corresponding software that can handle the complex geometry, the complex fluid flows and run efficiently on a supercomputer. In this talk, we present a highly parallel domain decomposition method to solve the three-dimensional incompressible Navier-Stokes equations on a patient-specific artery at the full-body scale from neck to feet. Moreover, a two-level method is introduced to determine the model parameters in the Windkessel outlet boundary condition to guarantee clinically correct flow distributions to 14 major organs/regions. A fully implicit Newton-Krylov-Schwarz method with learning capabilities is used to solve the nonlinear algebraic system at each time step and numerical experiments show that the proposed method is robust with respect to the complex geometry, different model parameters, and is scalable with more than 10,000 processor cores.
Bio: X.-C. Cai received his PhD in 1989 from Courant Institute. He was a postdoc during 1989-1990 at Yale University. After about 30 years in the Department of Computer Science, University of Colorado Boulder, he recently joined the Department of Mathematics, University of Macau, as a Chair Professor. His research interests are in the areas of large scale scientific computing and computational biomechanics. He is a fellow of SIAM.
