Stochastic Self-Consistent Clustering Theory for Composite Performance Prediction: from extreme value microstructure attributes to design of interphase for toughness

The objective of the proposed research is to develop an integrated computational materials engineering (ICME) approach encompassing (a) methodologies for microstructure characterization and reconstruction with anomalies; (b) self consistent clustering theory and interphase modeling for rapid, efficient computation; (c) coupon-level stochastic damage assessment; and (d) the enhancement of toughness and strength through design of the microstructure and interphase. This approach for polymer matrix composite performance predictions emphasizes the development of a data-based approach accounting for anomaly detection and initiation of defects and damage mechanisms. As part of this work, we will develop new theories, create open source libraries and tools, and construct new microstructure reconstruction and analysis algorithms. Five major tasks will be involved.