摘要
The cut-off negative pressure method is one of effective approaches to solve the seepage flow with free surface. According to the characteristics of this method, however, it has been found out that the deficiency of the algorithms is to easily cause inaccurate simulation in the vicinity of free surface owing to the penalty function with only one controlling penalty parameter. Therefore, an improved cut-off pressure method was presented by constructing penalty function with two controlling penalty parameters. The improved algorithms were illustrated to be able to improve the simulation accuracy and raise the convergence speed through considering the effects of the partially saturated or unsaturated elements more reasonably and calculating the nodal external forces more accurately. Finally, the improved cut-off negative pressure method was applied to the simulation of unsteady seepage flow with free surface, and the corresponding program codes were developed with validation of some cases.
The cut-off negative pressure method is one of effective approaches to solve the seepage flow with free surface. According to the characteristics of this method, however, it has been found out that the deficiency of the algorithms is to easily cause inaccurate simulation in the vicinity of free surface owing to the penalty function with only one controlling penalty parameter. Therefore, an improved cut-off pressure method was presented by constructing penalty function with two controlling penalty parameters. The improved algorithms were illustrated to be able to improve the simulation accuracy and raise the convergence speed through considering the effects of the partially saturated or unsaturated elements more reasonably and calculating the nodal external forces more accurately. Finally, the improved cut-off negative pressure method was applied to the simulation of unsteady seepage flow with free surface, and the corresponding program codes were developed with validation of some cases.
基金
ProjectsupportedbytheNationalNaturalScienceFoundationofChina(GrantNo:50139030)
