A model for self-healing anisotropic composites

自修复材料属于智能材料的范畴。这些材料可以由于外部影响而改变其特性。受生物系统的启发，鉴于工程、材料科学和医学的新应用，自修复材料的开发和制造是国际研究的一个主题。该研究项目的目的是开发一种基于连续介质的微观力学模型和算法，用于描述自愈合各向异性复合材料。纤维增强复合材料由弱固体基体和不同的纤维族组成，愈合过程是基于嵌入固体基体中的愈合剂的存在。对于这些材料的宏观描述，有限元离散化的微观结构是用来适应微观行为的均匀化的宏观结构。在多孔介质理论（TPM）的框架内对基质的愈合过程进行建模，其中根据实验观察制定愈合的开始和进展的标准。损伤（裂纹）的位置、修复剂的量以及基本材料和自修复材料可以通过有限元模拟中的代表性体积元（RVE）的相应体积分数来识别。

Self-healing materials belong to the class of smart materials. These materials can change their properties due to external influences. Inspired by biological systems, the development and manufacture of self-healing materials in view of new applications in engineering, material science and medicine is a subject of international research. The aim of the research project is to develop a continuum-based micromechanical model and algorithms for the description of self-healing anisotropic composites. The fiber reinforced composites consist of a weak solid matrix and different fiber families, the healing process is based on the existence of healing agents embedded in the solid matrix. For the macroscopic description of these materials, an FE-discretization of the microstructure is used to fit the microscopic behavior to the homogenized macrostructure. The healing process of the matrix is modeled within the framework of the Theory of Porous Media (TPM), where criteria for the onset and progression of healing are formulated following experimental observations. The places of damage (cracks), the amount of healing agents as well the basic and the self-healed material can be identified by the corresponding volume fractions of a Representative Volume Element (RVE) in a finite element simulation.