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.

本研究的目的是开发一种预测能力，从高阶取向张量评估短纤维和长纤维增强聚合物复合材料的力学性能。该方法是开发一个自动化的三维有限元程序，用于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。这项工作还包括用微CT成像技术测量纤维取向，并开发一种计算方法，用于从聚合物熔体流动模拟结果中数值生成纤维样品。作为这项研究的一部分，蒙特卡罗模拟程序将被用来评估预测的属性的统计性质，和模拟方法将被证明在工业相关的products.Fiber增强聚合物复合材料是在众多的工程应用的材料的选择，部分原因是其上级强度重量比。对于长纤维和短纤维复合材料产品尤其如此，这些产品也受益于注塑成型等极其通用的制造方法。因此，在商业产品中加入更多的纤维增强聚合物仍然是一项重大努力，特别是在美国汽车工业中，未来的车辆必须具有减少的重量，排放和燃料消耗。事实上，美国能源部2010年FreedomCAR计划的一个具体目标是，在与当今产品相同的成本和耐用性下，将汽车结构的重量减轻50%。他们的积极目标得到了该研究项目的支持，其重点是纤维增强聚合物产品及其制造工艺之间的关键环节。该研究还包括开发教育可视化工具，为未来的工程专业学生提供更清晰的纤维悬浮力学理解。