Mechanistic Data Science for Modeling and Design of Aerospace Composite Materials
Satyajit Mojumder, Lei Tao, Ying Li, Wing Kam Liu

TL;DR
This paper introduces a mechanistic data science framework that systematically models aerospace composite materials across multiple scales, enabling efficient design and analysis despite high complexity and limited data.
Contribution
It presents a novel framework combining mechanistic modeling and data science to understand and design hierarchical composite materials across scales.
Findings
Developed a composite knowledge database across nanoscale, microscale, and mesoscale mechanisms.
Demonstrated the framework's ability to rapidly evaluate new material designs.
Enabled systematic understanding of structure-property relationships in composites.
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…
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Taxonomy
TopicsMachine Learning in Materials Science · Carbon Nanotubes in Composites · Composite Material Mechanics
