@article{hasan_capolungo_zikry_2023, title={A machine learning microstructurally predictive framework for the failure of hydrided zirconium alloys}, volume={7}, ISSN={["2397-2106"]}, url={https://doi.org/10.1038/s41529-023-00344-7}, DOI={10.1038/s41529-023-00344-7}, abstractNote={AbstractHydride 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.}, number={1}, journal={NPJ MATERIALS DEGRADATION}, author={Hasan, Tamir and Capolungo, Laurent and Zikry, Mohammed}, year={2023}, month={Mar} }
 @article{hasan_capolungo_zikry_2023, title={How can machine learning be used for accurate representations and predictions of fracture nucleation in zirconium alloys with hydride populations?}, volume={11}, ISSN={["2166-532X"]}, url={https://doi.org/10.1063/5.0155679}, DOI={10.1063/5.0155679}, abstractNote={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.}, number={7}, journal={APL MATERIALS}, author={Hasan, T. and Capolungo, L. and Zikry, M. A.}, year={2023}, month={Jul} }
 @article{hasan_capolungo_zikry_2023, title={Predictive machine learning approaches for the microstructural behavior of multiphase zirconium alloys}, volume={13}, ISSN={["2045-2322"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85151742870&partnerID=MN8TOARS}, DOI={10.1038/s41598-023-32582-9}, abstractNote={AbstractZirconium alloys are widely used in harsh environments characterized by high temperatures, corrosivity, and radiation exposure. These alloys, which have a hexagonal closed packed (h.c.p.) structure thermo-mechanically degrade, when exposed to severe operating environments due to hydride formation. These hydrides have a different crystalline structure, than the matrix, which results in a multiphase alloy. To accurately model these materials at the relevant physical scale, it is necessary to fully characterize them based on a microstructural fingerprint, which is defined here as a combination of features that include hydride geometry, parent and hydride texture and crystalline structure of these multiphase alloys. Hence, this investigation will develop a reduced order modeling approach, where this microstructural fingerprint is used to predict critical fracture stress levels that are physically consistent with microstructural deformation and fracture modes. Machine Learning (ML) methodologies based on Gaussian Process Regression, random forests, and multilayer perceptrons (MLP) were used to predict material fracture critical stress states. MLPs, or neural networks, had the highest accuracy on held-out test sets across three predetermined strain levels of interest. Hydride orientation, grain orientation or texture, and hydride volume fraction had the greatest effect on critical fracture stress levels and had partial dependencies that were highly significant, and in comparison hydride length and hydride spacing have less effects on fracture stresses. Furthermore, these models were also used accurately predicted material response to nominal applied strains as a function of the microstructural fingerprint.}, number={1}, journal={SCIENTIFIC REPORTS}, author={Hasan, Tamir and Capolungo, Laurent and Zikry, Mohammed A. A.}, year={2023}, month={Apr} }
 @article{mohamed_hasan_zikry_2022, title={Thermomechanical Microstructural Predictions of Fracture Nucleation of Zircaloy-4 Alloys With delta and e Hydride Distributions}, volume={144}, ISSN={["1528-8889"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85127542533&partnerID=MN8TOARS}, DOI={10.1115/1.4051687}, abstractNote={Abstract
               A crystalline dislocation-density formulation that was incorporated with a nonlinear finite-element (FE) method was utilized to understand and to predict the thermomechanical 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 thermomechanical 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 into why circumferential hydrides are more fracture-resistant than radial hydrides for different volume fractions and thermomechanical loading conditions.}, number={1}, journal={JOURNAL OF ENGINEERING MATERIALS AND TECHNOLOGY-TRANSACTIONS OF THE ASME}, author={Mohamed, I. and Hasan, T. and Zikry, M. A.}, year={2022}, month={Jan} }
 @article{hasan_ziaei_zikry_2019, title={Microstructural Modeling of the Mechanical Behavior of Face-Centered Cubic Nanocrystalline-Twinned Systems}, volume={50A}, ISSN={["1543-1940"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85056852998&partnerID=MN8TOARS}, DOI={10.1007/s11661-018-5008-2}, number={2}, journal={METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE}, author={Hasan, Tamir S. and Ziaei, S. and Zikry, M. A.}, year={2019}, month={Feb}, pages={609–615} }
 @inproceedings{kribs_hutchins_reach_hasan_lyons_2014, title={Effects of hydrogen enrichment on the reattachment and hysteresis of lifted methane flames}, booktitle={Proceedings of the ASME Power Conference, 2013, vol 1}, author={Kribs, J. D. and Hutchins, A. R. and Reach, W. A. and Hasan, T. S. and Lyons, K. M.}, year={2014} }
 @article{kribs_moore_hasan_lyons_2013, title={Nitrogen-diluted methane flames in the near-and far-field}, volume={135}, number={4}, journal={Journal of Energy Resources Technology}, author={Kribs, J. and Moore, N. and Hasan, T. and Lyons, K.}, year={2013} }
 @inproceedings{hasan_kribs_lyons_2012, title={Influences of nitrogen dilution in the near flow field of transition regime lifted natural gas jet flames}, booktitle={Proceedings of the ASME International Mechanical Engineering Congress and Exposition, 2011, vol 1}, author={Hasan, T. S. and Kribs, J. D. and Lyons, K. M.}, year={2012}, pages={907–912} }