Uncertainty Quantification of Test Derived Model Parameters for Life Prediction of Composite Bolted Joints in an ICME Framework

ABSTRACT:This program implements a Bayesian framework for uncertainty quantification of material parameters needed for life prediction of polymer matrix composite (PMC) bolted joints. Accurate estimations of fatigue strength parameters and their distributions are needed for durability assessment of composite airframes with bolted joints. The proposed software tool will have capabilities to quantify the experimental random and bias errors encountered in fatigue testing of composites, and eliminate the propagation of such errors when quantifying statistical distributions of fatigue strength parameters. The Bayesian inferencing of parameters for fatigue damage growth using macro-, meso- and micro-scale modeling approaches, with consistency in uncertainty distributions across these scales will advance the state of Integrated Computational Materials Engineering (ICME) efforts for composite airframe structural life and durability assessments.BENEFIT:N&R Engineering and San Diego State University will collaborate with Northup Grumman to develop analyses and uncertainty quantification tools for composite bolted joint failure predictions. This effort will support ongoing work on certification/life management of airframes for the Triton, BAMS, Global Hawk and other high-altitude, long-endurance unmanned aerial reconnaissance systems.