Incorporating Higher Order Tensors in the Computation of Polymer Composite Mechanical Properties

将高阶张量纳入聚合物复合材料力学性能的计算

• 批准号: 0522694
• 负责人: Douglas Smith
• 金额: $ 18万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-15 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0522694&HistoricalAwards=false
• 关键词: Incorporating; Higher; Order; Tensors; Computation

The objective of this research is to develop a predictive capability that evaluates the mechanical properties of short and long fiber reinforced polymer composites from higher-order orientation tensors. The approach is to develop an automated three-dimensional finite element procedure for predicting the effective mechanical properties of a fiber suspension from its orientation distribution and the mechanical properties of the constituent materials. The work also includes measuring fiber orientations with micro-CT imaging techniques and developing a computational approach for generating fiber samples numerically from polymer melt flow simulation results. As part of this research, a Monte Carlo simulation procedure will be used to assess the statistical nature of the predicted properties, and the simulation methodology will be demonstrated on industrially-relevant products.Fiber reinforced polymer composites are the material of choice in numerous engineering applications, due in part to their superior strength to weight ratio. This is especially true for long and short fiber composite products, which also benefit from extremely versatile manufacturing methods such as injection molding. As a result, there continues to be a major effort to incorporate more fiber reinforced polymers in commercial products, particularly in the US automotive industry where future vehicles must have reduced weight, emissions, and fuel consumption. Indeed, a specific objective of the 2010 FreedomCAR program at the US Department of Energy, a co-sponsor and collaborator of this research, is to reduce the weight of an automotive structure by 50 percent for the same cost and durability as seen in today's products. Their aggressive goal is supported under this research project by its focus on a critical link between a fiber reinforced polymer product and its manufacturing process. The research also includes the development of educational visualization tools that will provide a clearer understanding of fiber suspension mechanics for future engineering students.

这项研究的目的是开发一种预测能力，该能力评估了来自高阶方向张量的短纤维增强聚合物复合材料的机械性能。该方法是开发一种自动化的三维有限元程序，以预测纤维悬浮液的有效机械性能从其取向分布和组成材料的机械性能中预测。这项工作还包括用微型CT成像技术测量纤维方向，并开发一种计算方法，以从聚合物熔体流量模拟结果中生成数值的纤维样品。作为这项研究的一部分，将使用蒙特卡洛模拟程序来评估预测性质的统计性质，并且将在与工业相关的产品上证明模拟方法。纤维增强的聚合物复合材料是许多工程应用中选择的材料，部分归因于其优势的强度优势。对于长纤维复合产品，尤其如此，这也受益于极具用途的制造方法，例如注入成型。结果，继续努力将更多的纤维增强聚合物纳入商业产品，尤其是在美国汽车行业中，未来车辆的重量，排放和燃油消耗必须减少。的确，这项研究的共同赞助商和合作者在美国能源部2010年的自由卡计划的一个具体目标是，以与当今产品相同的成本和耐用性，将汽车结构的重量降低了50％。在该研究项目下，他们的积极目标是通过关注纤维增强聚合物产品与其制造过程之间的关键联系的支持。该研究还包括开发教育可视化工具，这些工具将为未来的工程专业的学生提供对纤维悬架机制的清晰了解。