  Three-dimensional cardiovascular fluid dynamics simulations typically require
computation of several cardiac cycles before they reach a periodic solution,
rendering them computationally expensive. Furthermore, there is currently no
standardized method to determine whether a simulation has yet reached that
periodic state. In this work, we propose use of the asymptotic error measure to
quantify the difference between simulation results and their ideal periodic
state using lumped-parameter modeling. We further show that initial conditions
are crucial in reducing computational time and develop an automated framework
to generate appropriate initial conditions from a one-dimensional model of
blood flow. We demonstrate the performance of our initialization method using
six patient-specific models from the Vascular Model Repository. In our
examples, our initialization protocol achieves periodic convergence within one
or two cardiac cycles, leading to a significant reduction in computational cost
compared to standard methods. All computational tools used in this work are
implemented in the open-source software platform SimVascular. Automatically
generated initial conditions have the potential to significantly reduce
computation time in cardiovascular fluid dynamics simulations.
