Computational modeling, stochastic and experimental analysis with thermoelastic stress analysis for fiber reinforced polymeric composite material systems
Johnson, Shane Miguel
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Many studies with Thermoelastic Stress Analysis (TSA) and Infrared Thermography, in Fiber Reinforced Polymeric materials (FRPs), are concerned with surface detection of "hot spots" in order to locate and infer damage. Such experimental analyses usually yield qualitative relations where correlations between stress state and damage severity cannot be obtained. This study introduces quantitative experimental methodologies for TSA and Digital Image Correlation to expand the use of remote sensing technologies for static behavior, static damage initiation detection, and fatigue damage in FRPs. Three major experimental studies are conducted and coupled with nonlinear anisotropic material modeling: static and TSA of hybrid bio-composite material systems, a new stochastic model for fatigue damage of FRPs, and fracture analysis for FRP single-lap joints. Experimental calibration techniques are developed to validate the proposed macromechanical and micromechanical nonlinear anisotropic modeling frameworks under multi-axial states of stress. The High Fidelity Generalized Method of Cells (HFGMC) is a sophisticated micromechanical model developed for analysis of multi-phase composites with nonlinear elastic and elastoplastic constituents is employed in this study to analyze hybrid bio-composites. Macro-mechanical nonlinear anisotropic models and a linear orthotropic model for fracture behavior using the Extended Finite Element method (XFEM) are also considered and compared with the HFGMC method. While micromechanical and FE results provide helpful results for correlating with quasi-static behavior, analyzing damage progression after damage initiation is not straightforward and involves severe energy dissipation, especially with increasing damage progression. This is especially true for fatigue damage evolution, such as that of composite joints as it is associated with uncertainty and randomness. Towards that goal, stochastic Markov Chain fatigue damage models are used to predict cumulative damage with the new damage indices proposed using full-field TSA image analysis algorithms developed for continuously acquired measurements during fatigue loading of S2-Glass/E733FR unidirectional single-lap joints. Static damage initiation is also investigated experimentally with TSA in single-lap joints with thick adherends providing for new design limitations. The computational modeling, stochastic and experimental methods developed in this study have a wide range of applications for static, fracture and fatigue damage of different FRP material and structural systems.