Numerical modeling is of crucial importance in understanding the behavior of regional groundwater system. However, the demand on modeling capability is intensive when performing high-resolution simulation over long time span. This paper presents the application of a parallel program to speed up the detailed modeling of the groundwater flow system in the North China Plain. The parallel program is implemented by rebuilding the well-known MODFLOW program on our parallelcomputing framework, which is achieved by designing patch-based parallel data structures and algorithms but maintaining the compute flow and functionalities of MODFLOW. The detailed model with more than one million grids and a decade of time has been solved. The parallel simulation results were examined against the field observed data and these two data are generally in good agreement. For the comparison on solution time, the parallel program running on 32 cores is 6 times faster than the fastest MICCG-based MODFLOW program and 11 times faster than the GMG-based MODFLOW program. Therefore, remarkable computational time can be saved when using the parallel program, which facilitates the rapid modeling and prediction of the groundwater flow system in the North China Plain.
Tangpei ChengJingli ShaoYali CuiZeyao MoZhong HanLing Li
Based on the two-dimensional three-temperature （2D3T） radiation diffusion equations and its discrete system, using the block diagonal structure of the three-temperature matrix, the reordering and symbolic decomposition parts of the RSMF method are replaced with corresponding block operation in order to improve the solution efficiency. We call this block form method block RSMF （in brief, BRSMF） method. The new BRSMF method not only makes the reordering and symbolic decomposition become more effective, but also keeps the cost of numerical factorization from increasing and ensures the precision of solution very well. The theoretical analysis of the computation complexity about the new BRSMF method shows that the solution efficiency about the BRSMF method is higher than the original RSMF method. The numerical experiments also show that the new BRSMF method is more effective than the original RSMF method.
