MDC: A Numerical Laboratory for Multi-Model Multi-Domain Computational Methods in Aerodynamics and Acoustics

MDC：空气动力学和声学多模型多领域计算方法的数值实验室

• 批准号: 9527169
• 负责人: David Keyes
• 金额: $ 113.06万
• 依托单位: Old Dominion University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-10-01 至 1999-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9527169&HistoricalAwards=false
• 关键词: MDC; Numerical; Laboratory; Multi; Model

9527169 Keyes This project will develop public use software to implement two important paradigms for domain decomposition: medium-grained data decomposition and course-grained task decomposition. In addition, it will enhance the theory of task decomposition with respect to two specific applications involving multiple physics, aerodynamics and physics. It will attempt to verify the theory behind the two paradigms, and the develop software which combines the strength of both. Effective multi-domain parallel solutions to multi-model coupled field problems will have widespread usefulness, particularly in light of the growing emphasis on multidisciplinary analysis and optimization. The current pioneers of multidisciplinary optimization are finding it necessary to revisit the lower levels of the constituent modeling hierarchies. It is not practical to simultaneously upgrade all of the low-level models employed inside of optimization loops to the complex models used in the state-of -the-art of the individual disciplines, for reasons of complexity and severe ill-conditioning. This creates an obvious niche for the deliverables of this proposal - highly performant solvers with built-in support for multi-model analysis.

9527169 Keyes这个项目将开发公共使用软件来实现域分解的两个重要范例：中等粒度的数据分解和粗粒度的任务分解。 此外，它将增强任务分解理论方面的两个具体应用，涉及多个物理，空气动力学和物理。 它将试图验证这两种范式背后的理论，并开发结合两者优势的软件。 多模型耦合场问题的有效多域并行解决方案将具有广泛的用途，特别是考虑到对多学科分析和优化的日益重视。 当前多学科优化的先驱们发现有必要重新审视构成建模层次结构的较低层次。 由于复杂性和严重病态的原因，同时将优化循环内采用的所有低级模型升级为在各个学科的最新技术中使用的复杂模型是不切实际的。 这为该提案的可交付成果创造了一个明显的利基市场-具有内置多模型分析支持的高性能求解器。