SBIR-STTR Award

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.

Benefits:
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.

Keywords:
Uncertainty analysis, model parameter estimation, modeling and simulation, verification and validation, polymer matrix composites, composite bolted structures, PMC, Bayesian inference

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 quantify the experimental random and bias errors encountered in fatigue testing of composites, and thereby eliminate the propagation of such errors when quantifying statistical distributions of fatigue strength parameters. 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.

Benefits:
A number of current US military planes have composite bolted joints. These include the legacy aircraft such as F/A-18 E/F, new aircraft such as F-35, and unmanned systems such as the Global Hawk. Traditionally, the design of such joints has relied upon qualification testing, which drives up the costs of programs. The issue of damage tolerance of the bolted joints continues to be concerns on these existing platforms when they undergo life extension reviews. Newer aircraft in development are also required to be certified for fatigue performance requirements. Fatigue life prediction of bolted connections in composite structures is currently a challenge that requires development of tools and methods.

Keywords:
Uncertainty quantification Bayesian Statistics Finite Elements Bolted Joint Polymer Matrix Composites Fatigue life prediction