Parallel finite element modeling of earthquake ground response and liquefaction

作     者：陆金池 彭军 Ahmed Elgamal 杨朝晖 Kincho H.Law 

作者机构：Department of Structural EngineeringUniversity of California Department of Civil and Enviromental Engineering Stanford University Department of Civil and Enviromental EngineeringStanford University 

出 版 物：《Earthquake Engineering and Engineering Vibration》 (地震工程与工程振动（英文刊）)

年 卷 期：2004年第3卷第1期

页      面：23-37页

核心收录：

学科分类：070801[理学-固体地球物理学] 07[理学] 0708[理学-地球物理学] 

基　　金：the National Science Foundation Grants Number CMS-0084616,0200510 and ANI-0205720 to University of California,San Diego, and Grant Number CMS-0084530 to Stanford University Additional funding was also provided by the NSF cooperative agreement ACI-9619020 through computing resources provided by the National Partnership for Advanced Computational Infrastructure at the San Diego Supercomputer Center 

主　　题：parallel finite element domain decomposition liquefaction parallel speedup earthquake site amplification 

摘      要：Parallel computing is a promising approach to alleviate the computational demand in conducting large-scale finite element *** paper presents a numerical modeling approach for earthquake ground response and liquefaction using the parallel nonlinear finite element program,ParCYCLIC,designed for distributed-memory message-passing parallel computer *** ParCYCLIC,finite elements are employed within an incremental plasticity,coupled solid-fluid formulation,A constitutive model calibrated by physical tests represents the salient characteristics of sand liquefaction and associated accumulation of shear *** elements of the computational strategy employed in ParCYCLIC include the development of a parallel sparse direct solver,the deployment of an automatic domain decomposer,and the use of the Multilevel Nested Dissection algorithm for ordering of the finite element *** results of centrifuge test models using ParCYCLIC are *** results from grid models and geotechnical simulations show that ParCYCLIC is efficiently scalable to a large number of processors.