Stochastic Self-Consistent Clustering Theory for Composite Performance Prediction: from extreme value microstructure attributes to design of interphase for toughness
Abstract
The goal of the proposed research is to develop an integrated computational materialsengineering (ICME) approach encompassing (a) methodologies for microstructurecharacterization with anomalies; (b) self consistent clustering theory and interphase modeling forrapid computation; (c) coupon level stochastic damage assessment; and (d) the enhancement oftoughness and strength through interphase design. This approach for polymer matrix compositeperformance predictions includes a particular emphasis on the development of a data-basedapproach 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, andconstruct new microstructure reconstruction and analysis algorithms. We will apply theframework to generate methods to understand, predict and design the interphase in order tooptimally balance toughness with strength. Manufacturing defects such as voids, chemicaldefects, sizing irregularities, particle clustering and misalignment lead to early development ofdamage and reduced lifetime of composites. Therefore, we will construct algorithms for anomalydetection and reconstruction in microstructures and then use the framework to predict theinfluence of anomalies on composite behavior. The systems examined will include nanoparticlepolymersystems, unidirectional fiber composites, and ultimately hybrid composites with bothnano and micro fillers. The work will develop understanding and predictive models of theinterphase regime in polymer composite systems based on constituents and processing and will beapplied to a MoS2 composite testbed in collaboration with AFRL researchers. Overall, the workwill yield simulation and analysis tools with which to understand, predict and design compositesfor optimal properties, taking into account rare defects and interphase development.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 24, 2019
- Source ID
- FA95501810381
Entities
People
- L. Catherine Brinson
Organizations
- Air Force Office of Scientific Research
- Duke University
- United States Air Force