In order to obtain more reaction information of neutron or the other secondary particles by forcing the collision to occur within a small time step, three Monte Carlo Time Forced Collision (TFC) sampling methods called discrete, large cross section and uniform time sampling method are presented, derived and tested. TFC method can be used in more complicated eases. For example, particle transporte through continuously changing materials within a cut time. Numerical results of simple multiplication system show that the calculation efficiency of TFC method is higher than that of the analog method by 2~4 magnitudes.

A parallel algorithm for time-independent Monte Carlo transport is successful since particles are independent and they are distributed to multiple processors.However,for time-dependent Monte Carlo transport problems, the parallel efficiency reduces and the parallel scale is limited due to the communication of scattering source attribute and meshes in each time-step.We propose two algorithms in them adaptive processor assignment and optimized processor choice are obtained.With a Monte Carlo stratified sampling technique for scattering source treatment the communication cost is reduced greatly.The parallel expandability is improved.A large speedup over the basic algorithm is obtained.

The parallel design of Monte Carlo code involves computational method and module designs,which is crucial to the parallel efficiency.The coupled of neutron and photon transport Monte Carlo code MCNP has been realized the parallelization in PVM and MPI by modifying the serial code.Due to the form having module being optimized, the parallel efficiency is good where the efficiency of MPI code is stronger than that of PVM code and the speedup of MPI code is higher than that of PVM in most cases.The calculated results of parallel code are reasonable.Both the speedups of PVM code and MPI code have been the linear increasing with the processors.The parallel efficiencies are up to 99% in 16-processors,97% in 32-processors and 89% in 64-processors respectively.