Acceleration of Primitive Variable Hydrocodes by the Non- Linear Generalized Minimum Residual Method

点击翻译按钮获取中文摘要

Computational modeling has become an essential tool in the analysis and design of engineering systems. Often such models involve the reduction of continuous system of governing partial differential equations to a set of discrete nonlinear algebraic equations. The accurate and efficient solution of the large set of nonlinear algebraic system poses a great challenge to the numerical analysts. Existing algorithms for the discrete nonlinear system convergence thus slowly limits the size of the problem that can be effectively handled. The dollar value of increasing the execution.time efficiency of these codes over their life.cycle is staggering. Yet many innovations from computational mathematics which can yield comparable results in less time are only slowly being incorporated into production codes. This is due to the high cost of retrofitting existing applications codes with new algorithmic kernels and of developing new codes which inherit all of the application.specific features of existing codes. An adaptive nonlinear generalized minimum residual (NLGMR) algorithm which would accelerate convergence where possible and pass the iterates through unmodified, otherwise, is the goal of this Engineering Initiation project. The NLGMR lgorithm treats an existing iteration scheme as a "black box" and computes improved iterates on the basis of iteration history alone. Thus, it can be used to retrofit existing codes. However, the memory requirements of an NLGMR.enhanced algorithm are more substantial than those of the original code, but with the availability of supercomputer resources limitations on memory storage, this should not be a problem. The institutional support is adequate and the P.I. is well qualified to carry out the research. I strongly recommend support.