A crystalline dislocation-density formulation that was incorporated with a non-linear finite-element (FE) method was utilized to understand and to predict the thermo-mechanical behavior of an hexagonal closest packed (h.c.p.) zircaloy system with hydrides with either face centered cubic (f.c.c.) or body centered cubic (b.c.c.) hydrides. This formulation was then used with a recently developed fracture methodology that is adapted for finite inelastic strains and multiphase crystalline systems to understand how different microstructurally-based fracture modes nucleate and propagate. The interrelated microstructural characteristics of the different crystalline hydride and matrix phases with the necessary orientation relationships (ORs) have been represented, such that a detailed physical understanding of fracture nucleation and propagation can be predicted for the simultaneous thermo-mechanical failure modes of hydride populations and the matrix. The effects of volume fraction, morphology, crystalline structure, and orientation and distribution of the hydrides on simultaneous and multiple fracture modes were investigated for radial, circumferential, and mixed distributions. Another key aspect was accounting for temperatures changes due to the effects of thermal conduction and dissipated plastic work and their collective effects on fracture. For hydrided aggregates subjected to high temperatures, thermal softening resulted in higher ductility due to increased dislocation-density activity, which led to higher shear strain accumulation and inhibited crack nucleation and growth. The predictions provide validated insights of why circumferential hydrides are more fracture resistant than radial hydrides for different volume fractions and thermo-mechanical loading conditions.

Zirconium alloys are critical material components of systems subjected to harsh environments such as high temperatures, irradiation, and corrosion. When exposed to water in high temperature environments, these alloys can thermo-mechanically degrade by forming hydrides that have a crystalline structure that is different from that of zirconium. Cracks can nucleate near these hydrides; hence, these hydrides are a direct link to fracture failure and overall large inelastic strain deformation modes. To fundamentally understand and predict these microstructural failure modes, we interrogated a finite-element database that was deterministically tailored and generated for large strain-dislocation-density crystalline plasticity and fracture modes. A database of 210 simulations was created to randomly sample from a group of microstructural fingerprints that encompass hydride volume fraction, hydride orientation, grain orientation, hydride length, and hydride spacing for a hydride that is physically representative of an aggregate of a hydride population. Machine learning approaches were then used to understand, identify, and characterize the dominant microstructural mechanisms and characteristics. We first used fat-tailed Cauchy distributions to determine the extreme events. A multilayer perceptron was used to learn the mechanistic characteristics of the material response to predefined strain levels and accurately determine the critical fracture stress response and the accumulated shear slips in critical regions. The predictions indicate that hydride volume fraction, a population-level parameter, had a significant effect on localized parameters, such as fracture stress distribution regions, and on the accumulated immobile dislocation densities both within the face centered cubic hydrides and the hexagonal cubic packed h.c.p. matrix.

With the increased utilization of multicomponent fuels, such as natural gas and biogas, in industrial applications, there is a need to be able to effectively model and predict the properties of jet flames for mixed fuels. In addition, the interaction of these diluted fuels with outside influences (such as differing levels of coflow air) is a primary consideration. Experiments were performed on methane jet flames under the influence of varying levels of nitrogen dilution, from low Reynolds number lifted regimes to blowout, observing the influence of the nitrogen on lifted flame height and flame chemiluminesence images. These findings were analyzed and compared with existing lifted jet flame relations, such as the flammable region approximation proposed by Tieszen et ai, as well as to undiluted flames. The influence of nitrogen dilution was seen to have an effect on the liftoff height of the flame, as well as the blowout velocity of the flame, but was seen to have a less pronounced effect compared with flames with coflowing air.

The purpose of this study is to observe the effects of hydrogen enrichment on the stability of lifted, partially premixed, methane flames. Due to the relatively large burning velocity of hydrogen-air flames when compared to that of typical hydrocarbon-air flames, hydrogen enriched hydrocarbon flames are able to create stable lifted flames at higher velocities. In order to assess the impact of hydrogen enrichment, a selection of studies in lifted and attached flames were initiated. Experiments were performed that focused on the amount of hydrogen needed to reattach a stable, lifted methane jet flame above the nozzle. Although high fuel velocities strain the flame and cause it to stabilize away from the nozzle, the high burning velocity of hydrogen is clearly a dominant factor, where as the lifted position of the flame increased, the amount of hydrogen needed to reattach the flame increased at the same rate. In addition, it was observed that as the amount of hydrogen in the central jet increased, the change in flame liftoff height increased and hysteresis became more pronounced. It was found that the hysteresis regime, where the flame could either be stabilized at the nozzle or in air, shifted considerably due to the presence of a small amount of hydrogen in the fuel stream. The effects of the hydrogen enrichment, however small the amount of hydrogen compared to the overall jet velocity, was the major factor in the flame stabilization, even showing discernible effects on the flame structure.

Hydride precipitation within zirconium alloys affects ductility and fracture behavior. The complex distribution of hydrides and their interaction with defects, such as dislocations, have a significant role in crack nucleation and failure. Hence, there is substantial variability in the microstructural behavior of hydrided zirconium. A deterministic fracture model coupled to a dislocation-density based crystalline plasticity approach was used to predict failure. Deterministic simulations were used to develop a database of crack initiation for representative microstructural characteristics, such as texture, crystalline structure, hydride orientations and spacing, and hydride geometry. The machine learning (ML) analysis is based on Extreme Value Theory (EVT) and a Bayesian based Gaussian Process Regression (GPR). Fracture probability is significantly influenced by hydride orientation and dislocation-density interactions. Furthermore, surrogate reduced order models (ROM) models were used to predict the likelihood of failure. This approach provides a ML framework to predict failure at different physical scales.