Spatial Parallel Algorithm Research Articles

A three-dimensional PWR-core pin-by-pin analysis code NECP-Bamboo2.0

A PWR-core pin-by-pin fuel management calculation code system named NECP-Bamboo2.0 has been developed, including the two-dimensional lattice calculation code Bamboo-Lattice2.0 and the three-dimensional steady-state whole-core pin-by-pin calculation code Bamboo-Core2.0. Pin-cell homogenized few-group constants are handled in an optimized least-square fitting form. A tightly coupled calculation strategy is employed for multi-physics coupling iterative process. A three-dimensional whole-core spatial parallel algorithm based on the unified domain decomposition is established to decrease both computing time and storage. (1) Compared with the traditional two-step method, the pin-by-pin calculation can ensure the accuracy of reactor reactivity, can reduce the average error of pin-power calculation from 2% to 1% for large commercial PWR core, can reduce the error of pin-power from 11% to 3% for the small PWR core. (2) NECP-Bamboo2.0 can directly provide pin-resolved distributions, including coolant temperature, fuel temperature, burnup and so on. The corresponding accuracy is comparable with one-step high-fidelity results.

Time domain parallelization for computational geodynamics

I present a time domain parallelization approach for geodynamic modeling. This algorithm, named parareal, is based on the use of coarse sequential and fine parallel propagators to predict and to iteratively correct the solution of the governing equations over a given time interal. Although the method has been successfully used to solve differential equations, in various scientific areas, it has not been applied to model solid‐state convective motions relevant to the Earth and other planetary mantles. In that case, the time‐dependence of the velocity is only implicit, which requires modifications to the original algorithm. The performances of this adapted version of the parareal algorithm were investigated using theoretical model predictions in good agreement with numerical experiments. I show that under optimum conditions, the parallel speedup increases linearly with the number of processors, and speedups close to 10 were measured, using only few tens of CPUs. This parareal approach can be used alone or combined with any spatial parallel algorithm, allowing significant additional increase in speedup with increasing number of processors.