GOALI: Robust Sheet Forming Simulation via Implicit Methods

GOALI：通过隐式方法进行稳健的板材成型模拟

• 批准号: 0355429
• 负责人: Robert Wagoner
• 金额: --
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2008-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0355429&HistoricalAwards=false
• 关键词: GOALI; Robust; Sheet; Forming; Simulation

The objective of this research is to provide more accurate and robust simulation of sheet metal forming processes in order to reduce the costs, masses, lead times, and defects of transportation vehicles. The approach is a cooperative use-directed basic research project between the Ohio State University and General Motors R&D Center, with supporting partners Altair Engineering and The Ohio Supercomputer Center. Novel and practical finite-element analysis procedures will be developed following basic numerical advances made in the early stages of the project. In particular, advances in implicit numerical technology will be sought to confer the usual advantages of accuracy and consistency, while improving robustness and efficiency.Societal benefits of the project include improved fuel economy of vehicles, reduced impact on the environment, and safer and more affordable transportation for consumers. The highly cooperative and leveraged plan includes 12 months of student internships at GM and Altair aimed at broadening the training of graduate-level engineers to include experience in industry. The supported graduate students will profit from the opportunity, via daily cooperation with partners and internships at partner sites, to work within and learn the cultures of large companies (GM) and smaller software developers (Altair). Co-PI Wagoner will utilize the numerical advances realized in the project to construct reliable instruction modules for student-generated simulation of sheet forming programs.

这项研究的目的是提供更准确、更稳健的金属板材成形过程模拟，以减少运输车辆的成本、质量、交货时间和缺陷。 该方法是俄亥俄州立大学和通用汽车研发中心之间的一个以使用为导向的合作基础研究项目，支持合作伙伴包括 Altair Engineering 和俄亥俄超级计算机中心。 根据项目早期阶段取得的基本数值进展，将开发新颖实用的有限元分析程序。 特别是，将寻求隐式数值技术的进步，以赋予通常的准确性和一致性优势，同时提高鲁棒性和效率。该项目的社会效益包括提高车辆的燃油经济性，减少对环境的影响，以及为消费者提供更安全、更实惠的交通。 这项高度合作和杠杆化的计划包括学生在通用汽车和 Altair 进行为期 12 个月的实习，旨在扩大研究生水平工程师的培训范围，纳入行业经验。 受支持的研究生将通过与合作伙伴的日常合作以及在合作伙伴地点的实习，在大公司 (GM) 和小型软件开发商 (Altair) 内部工作并学习其文化，从而受益匪浅。 联合 PI 瓦格纳将利用该项目中实现的数值进展来构建可靠的教学模块，用于学生生成的板材成形程序模拟。