Mechanistic Data Science for Modeling and Design of Aerospace Composite Materials

Abstract

Polymer matrix composites exhibit remarkable lightweight and high strength properties that make them attractive for aerospace applications. Constituents' materials such as advanced polymers and fibers or fillers with their hierarchical structure embed these exceptional properties to the composite materials. This hierarchical structure in multiple length scales provides an opportunity for designing the composite materials for optimized properties. However, the high dimensional design space for the constituents' materials and architectures choice of the composites makes it a challenging design problem. To tackle this high dimensional design space, a systematic, efficient approach named mechanistic data science framework is proposed in this work to identify the governing mechanisms of materials systems from the limited available data and create a composite knowledge database. Our composite knowledge database comprises the knowledge of polymers at the nanoscale with nano reinforcement, the unidirectional structure at the microscale, and woven structure at mesoscale mechanisms that can be further used for the part scale composite design and analysis. The mechanistic data science framework presented in this work can be further extended to other materials systems that will provide the materials designer with the necessary tools to evaluate a new materials system design rapidly.