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

非线性广义最小残差法对原始变量水编码的加速

• 批准号: 8707109
• 负责人: David Keyes
• 金额: $ 5.99万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1987
• 资助国家: 美国
• 起止时间: 1987-07-01 至 1989-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8707109&HistoricalAwards=false
• 关键词: Acceleration; Primitive; Variable; Hydrocodes; Non

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.

计算建模已成为工程系统分析和设计的重要工具。 通常，这样的模型涉及将控制部分微分方程的连续系统减少到一组离散的非线性代数方程中。 大量非线性代数系统的准确有效解决方案对数值分析师构成了巨大挑战。 因此，离散非线性系统收敛的现有算法慢慢限制了可以有效处理的问题的大小。 这些代码在其寿命中的时间效率提高了执行的美元价值。循环令人震惊。 然而，从计算数学中的许多创新可以在更少的时间内产生可比的结果，只是缓慢地纳入生产代码中。 这是由于使用新算法内核改造现有应用程序代码的高成本以及开发新的代码，这些代码继承了所有应用程序。现有代码的特定功能。 该工程启动项目的目标是一种自适应非线性最小残差（NLGMR）算法，该算法将在可能的情况下加速收敛并通过未修饰的迭代术传递。 NLGMR LGORITHM将现有的迭代方案视为“黑匣子”，并且仅基于迭代历史记录就可以改善迭代。 因此，它可用于改造现有代码。 但是，NLGMR的内存需求比原始代码的内存要求更为实质，但是随着内存存储的SuperComputer资源限制的可用性，这应该不是问题。 机构支持足够，P.I.有资格进行研究。 我强烈建议支持。