Influence of microstructure variability on short crack growth behavior

Fatigue life in metals is predicted utilizing regression analysis of large sets of experimental data, thus representing the material’s macroscopic response. Furthermore, a high variability in the short crack growth (SCG) rate has been observed in polycrystalline materials, in which the evolution and distributionof local plasticity is strongly influenced by the microstructure features. The present work serves to (a) identify the relationship between the crack driving force based on the local microstructure in the proximity of the crack-tip and (b) defines the correlation between scatter observed in the SCG rates to variability in the microstructure. A crystal plasticity model based on the fast Fourier transform formulation of the elasto-viscoplastic problem (CP-EVP-FFT) is used, since the ability to account for the both elastic and plastic regime is critical in fatigue. Fatigue is governed by slip irreversibility, resulting in crack growth, which starts to occur during local elasto-plastic transition. To investigate the effects of microstructure variability on the SCG rate, sets of different microstructure realizations are constructed, in which cracks of different length are introduced to mimic quasi-static SCG in engineering alloys. From these results, the behavior of the characteristic variables of different length scale are analyzed: (i) Von Mises stress fields (ii) resolved shear stress/strain in the pertinent slip systems, and (iii) slip accumulation/irreversibilities. Through fatigue indicator parameters (FIP), scatter within the SCG rates is related to variability in the microstructural features; the results demonstrate that this relationship between microstructure variability and uncertainty in fatigue behavior is critical for accurate fatigue life prediction.

Multiphase flow in porous media: meta-modeling techniques for sensitivity analysis and risk assessment

One of the biggest challenges that contaminant hydrogeology is facing, is how to adequately address the uncertainty associated with model predictions. Uncertainty arise from multiple sources, such as: interpretative error, calibration accuracy, parameter sensitivity and variability. This critical issue needs to be properly addressed in order to support environmental decision-making processes. In this study, we perform Global Sensitivity Analysis (GSA) on a contaminant transport model for the assessment of hydrocarbon concentration in groundwater. We provide a quantification of the environmental impact and, given the incomplete knowledge of hydrogeological parameters, we evaluate which are the most influential, requiring greater accuracy in the calibration process. Parameters are treated as random variables and a variance-based GSA is performed in a optimized numerical Monte Carlo framework. The Sobol indices are adopted as sensitivity measures and they are computed by employing meta-models to characterize the migration process, while reducing the computational cost of the analysis. The proposed methodology allows us to: extend the number of Monte Carlo iterations, identify the influence of uncertain parameters and lead to considerable saving computational time obtaining an acceptable accuracy.