Employing simulation to customize patient treatment requires an affordable and fast computational technique due to the time-critical nature of the clinical intervention, with some being limited to a few hours. To lower this cost and allow for seamless adoption of predictive simulations in patient-specific treatment planning, this thesis proposes a novel strategy called Forked-Accelerated Simulation Technique (FAST). FAST is built on a reduced-order model (ROM) network and orthogonal solution basis (OSB), which are constructed based on a time-spectral Complex-valued Stokes solver (SCVS). FAST presents the following improvements comparing to the traditional computational fluid dynamics (CFD) solver. Firstly, it provides a significant saving in computational cost for boundary conditions smoothly varying in time, as is typically the case of blood flow simulation. It accomplishes this feat by solving only a few modes rather than thousands of time steps by formulating the problem in the time-spectral domain. Secondly, the proposed technique is embarrassingly parallelizable as the modes and solution basis are linearly independent, thus enabling scalable calculations at a much larger number of processors. Thirdly, the ROM and solution basis constructed in FAST are reusable for different boundary conditions, which provides a highly efficient solution procedure for inverse problems such as parameter identification, uncertainty quantification, and optimization. The comparison of FAST and a traditional stabilized finite element CFD solver is performed using five canonical and complex geometries. The results show that the FAST algorithm can provide accurate results at less than 20% CPU hours and 1.6% wall-clock time of the standard CFD solver.