Enabling Multidisciplinary Design Optimization: Inexact-Newton-Krylov and the Individual-Discipline-Feasible Formulation

实现多学科设计优化：不精确牛顿克雷洛夫和个别学科可行公式

• 批准号: 1332819
• 负责人: Jason Hicken
• 金额: $ 30.3万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1332819&HistoricalAwards=false
• 关键词: Enabling; Multidisciplinary; Design; Optimization; Inexact

This project is investigating matrix-free Newton-Krylov algorithms as a means of solving multidisciplinary design optimization problems. More precisely, we are exploring the use of matrix-free algorithms to solve simulation-based design optimization problems in a modular way. Modularity is attractive for these problems because many legacy software libraries already exist that can analyze and optimize problems involving a single discipline, for example fluid dynamics. In contrast, few libraries are available that can analyze complex multidisciplinary systems, let alone optimize them. To achieve a modular approach, we have adopted the so-called individual-discipline-feasible (IDF) formulation. Historically, the IDF formulation has been limited by the need to form computationally expensive matrices demanded by conventional optimization algorithms. This motivates our investigation of Newton-Krylov algorithms, which will enable a scalable and matrix-free implementation of IDF.Engineering systems governed by complex multi-physics are challenging to design, because they often exhibit subtle tradeoffs and defy our intuition. When the physics are modeled accurately with high-fidelity simulations, numerical optimization can help guide and inform the design of complex engineering systems. By showing that matrix-free Newton-Krylov methods can be used to efficiently solve IDF-formulated problems, this project promises to make high-fidelity design optimization more tractable and easier to implement for industrial practitioners. This makes powerful optimization tools more useful to designers, leading to improved products and processes that benefit society; examples include aircraft with lower emissions, more efficient power plants, and better artificial hearts. Such tools can also streamline the design process, which would improve the economic competitiveness of domestic industries.

该项目研究无矩阵牛顿-克雷洛夫算法作为解决多学科设计优化问题的一种手段。 更确切地说，我们正在探索使用无矩阵算法以模块化方式解决基于仿真的设计优化问题。 模块化对于这些问题是有吸引力的，因为已经存在许多遗留软件库，可以分析和优化涉及单个学科的问题，例如流体动力学。 相比之下，很少有库可以分析复杂的多学科系统，更不用说优化它们了。 为了实现模块化的方法，我们采用了所谓的个别学科可行（IDF）制定。 从历史上看，IDF公式已经受到限制，需要形成传统的优化算法所需的计算昂贵的矩阵。 这激发了我们对Newton-Krylov算法的研究，这将使IDF的可扩展和无矩阵实现成为可能。复杂的多物理场控制的工程系统设计具有挑战性，因为它们经常表现出微妙的权衡并违背我们的直觉。 通过高保真仿真对物理过程进行精确建模，数值优化可以帮助指导和告知复杂工程系统的设计。 通过证明无矩阵牛顿-克雷洛夫方法可以有效地解决IDF公式化问题，该项目有望使高保真设计优化更易于处理，更容易为工业从业者实现。 这使得强大的优化工具对设计师更有用，从而改进产品和流程，造福社会;例如，具有更低排放的飞机，更高效的发电厂和更好的人工心脏。 这些工具还可以简化设计过程，从而提高国内工业的经济竞争力。