Integration of Manufacturing-Induced Variation in Multiscale Analysis of Composite Aerospace Structures

Researcher: Jamal Husseini

Sponsor: NASA Space Technology Graduate Research Fellowship

Collaborators: Dr. Evan Pineda

Advisor: Scott Stapleton

Description: The goal of this project is to create statistically equivalent RVEs of composite microstructures using machine learning and spatial statistics for more accurate and efficient multiscale models. This is done by quantifying the effect of microstructural arrangement on strength and stiffness and using that relation as a constitutive model to predict part failure. Novel algorithms have been developed to quantify fiber clustering and matrix pockets and simulated using a reduced order model. Colleagues are using machine learning techniques to establish these relationships and helping to understand the main influences of composite microstructure failure.