The effect of Al on alpha' precipitation in FeCrAl alloys was studied through thermal aging of several model binary FeCr and FeCrAl alloys with Cr content of 13 wt.%, 17 wt.%, and 25 wt.% with and without 5.5 wt.% Al. Aging was performed up to 1,000 h at varying temperatures from 400 to 500 degrees C. At 400 degrees C no age hardening was observed due to slow kinetics at this temperature. For both the 17Cr and 25Cr alloys, the addition of Al shows a lowering of the miscibility gap, consistent with other reports in literature. Interestingly, however, for the 25Cr alloys the addition of Al in the FeCrAl ternary alloy accelerated alpha' precipitation below the miscibility gap. Such enhanced precipitation has also been predicted by our atomistic kinetic Monte Carlo (AKMC) simulations. While previous literature has often focused on Al suppressing alpha', here we show that while Al can lower the miscibility gap in the Fe-Cr ferritic system thermodynamically, it may also enhance the kinetics of precipitation.

FeCrAl alloys are promising candidates for next generation nuclear fuel cladding applications due to their high resistance to hydrothermal corrosion, high temperature steam oxidation, fretting, and creep properties. However, a potential area of concern for FeCrAl alloys is the precipitation of Cr-enriched alpha' phase which could have an implication on the mechanical, oxidative degradation as well as electrochemical behavior. Studies in this area are limited since most of the past applications of FeCrAl alloys have been in high temperatures above the miscibility gap. In this work, we investigate the high temperature oxidation and electrochemical behavior of a modified APMT (Fe21Cr5Al3Mo) alloy in unaged condition in comparison with a thermally aged condition having alpha' phase segregation. The alpha' is shown to have no major role in steam oxidation resistance of FeCrAl alloys. The electrochemical behavior in chloride environments shows that the presence of alpha' slightly reduces the pitting potential but shows an overall increase in polarization resistance indicating that alpha' may enhance corrosion resistance in chloride environments by increasing passivation.

Given the focus on developing novel accident-tolerant materials for nuclear fuel cladding applications, FeCrAl alloys have been considered as one of the promising candidates. To optimize the composition of FeCrAl alloys, systematic studies are necessary to understand the elemental effect on oxidation behavior at normal and accident conditions. This work focuses on understanding how the presence of 3 wt.% and 8 wt.% Al in a Fe-21Cr alloy affects their steam oxidation mechanisms and oxide architecture in the steam environment at 400°C and 1200°C. Results show that increasing Al content has a beneficial effect on the oxidation resistance at both temperatures. At lower temperatures, the presence of Al promotes the formation of Cr2O3 and limits Fe oxidation resulting in the formation of thinner oxides with dual uniform layers rich in Cr2O3 on the outside and rich in Al2O3 on the inside. At 1200°C, increasing Al content from 3 to 8% reduces transient Fe and Cr oxidation and promotes a thinner protective α-Al2O3 oxide.

The coupling of artificial intelligence and materials characterizations has been a center piece of almost all materials discovery efforts since 1990. Furthermore, with the constant development in probabilistic machine learning tools (i.e. machine learning models that can efficiently propagate uncertainty from inputs to outputs), the synergistic interplay between materials expert knowledge and machine learning model accuracy has been a major driver for state-of-the-art materials research. However, there still exist major challenges facing the machine learning community today when it comes to materials discovery. Be it properly representing uncertainties in the data, or leveraging materials expert knowledge in the learning phase, or actively learning from the usually-sparse, and in many cases non-comprehensive, data sets available to the machine learning expert. These challenges need to be overcome to accelerate research endeavors in the domain of material discovery. To this end, General Electric Research (GER) has been developing and applying highly robust, industry-grade tools that can do tasks like uncertainty quantification (UQ), probabilistic calibration, meta-modelling, active learning, and many more. Application and demonstration of two such tools namely Bayesian Hybrid Modeling (GEBHM) and Intelligent Design and Analysis of Computer Experiments (IDACE) are presented in this paper in the context of materials modeling and discovery.

A primary challenge of using FeCrAl in high temperature industrial settings is the formation of α′-precipitates that causes brittleness in the alloy, resulting in failure through fracture. The precipitation causes hardness change during thermal aging which is sensitive to both alloy composition and experimental condition (i.e., temperature and time of heat treatment). A Gaussian Process Regression (GPR) model is built on the hardness data collected at GE Research. Subsequently, for the first time, SHapley Additive exPlanations (SHAP) built upon the GPR is used as an Explainable Artificial Intelligence (XAI) tool to understand the effect of feature values in driving the hardness change. SHAP analysis has confirmed that the primary chemical driver for α′ age hardening in the FeCrAl system is Cr as expected. However, the analysis also indicated that Al does not have a clear trend of only suppressing the formation of α′ which contradicts current literature. This lack of a trend on the effect of Al on age hardening may be due to Al ability to both suppress thermodynamically and enhance kinetically the formation of α′. Similarly, SHAP analysis points towards Mo having no clear trend towards either enhancing or suppressing α′. This study indicates that more in depth studies focusing on both the chemistry and different aging temperatures (to study kinetics) should be performed to better understand the aging of this system.

Traditionally, FeCrAl alloys played an important role in high-temperature applications due to their ability to form a passive Al oxide film at temperatures above ~ 800 °C. Recently, FeCrAl alloys became of interest for the application of accident tolerant nuclear fuel cladding. This study covers work done at GE Research for better understanding the role of Al, Cr, and Mo in oxidation kinetics and thermodynamics. Several models and commercial prototype alloys have been tested in hydrothermal corrosion autoclave loops, at low temperature steam exposure (~ 400 °C), high temperature steam exposure (~ 1000 °C or higher), and high temperature air exposures. The results provide insights on how chromium and aluminum play a significant role in both high temperature and low temperature oxidation of FeCrAl. Additionally, machine learning tools are used to gain further insights on both predicting future optimized chemistries for balancing the properties of hydrothermal corrosion, low and high temperature steam oxidation, and thermal aging (which is exacerbated due to radiation in a nuclear reactor environment). GE plans to use this framework to further optimize the FeCrAl alloy system for use in nuclear reactor environments. Graphical abstract

FeCrAl alloys are being investigated as candidate materials for light water reactor fuel claddings. The chemical composition of FeCrAl determines its resistance to corrosion under normal operating conditions. The purpose of this paper is to investigate the effect of 0, 1, 3 wt% Ni addition on the passivation characteristics of Fe17Cr5.5Al in oxygenated and hydrogenated simulated reactor waters for 3-month immersion. Current results show that Ni addition improves the passivation of FeCrAl under oxygenated condition and decreases the mass loss under hydrogenated conditions.

FeCrAl alloys are among the most promising candidates for accident-tolerant fuel cladding material in light water nuclear reactors. Despite their high-temperature oxidation resistance in corrosive environments coupled with their hydrothermal corrosion resistance, a key challenge remains in optimizing the composition of the alloy that can be achieved through statistical analysis. However, the current literature on FeCrAl alloy design lack studies for designing alloys based on oxidation resistance. This study addresses that gap by developing a predictive model for the oxidation of FeCrAl alloys based on an experimental dataset, which lays the groundwork for model-based optimization for alloy composition.

Fuel Cladding Materials for nuclear reactors need to have great fretting resistance, good mechanical properties under irradiation and good corrosion/oxidation resistance. Since the Nuclear Reactor Failure at Fukushima, concerted efforts have been taken to develop novel Fuel Cladding materials with improved resistance towards degradation under the Loss of Coolant Accident Conditions (LOCA). FeCrAl-based alloys are promising materials given their high-temperature steam oxidation resistance under the LOCA conditions. However, little efforts have been put into understanding the compositional effects on the performance of FeCrAl alloys at temperatures at or near Boiling Water Reactor (BWR) conditions during normal operations (~300 °C). To address that need, this work focuses on understanding the effect of Al content in three Fe-17Cr-xAl alloys: Fe-17Cr-2Al, Fe-17Cr-4Al and Fe-17Cr-9Al on oxidation resistance at 400 °C in steam environment. This study shows that increased Al content reduces the total oxide thickness, enhances the formation of a uniform Cr-oxide layer and with sufficient Al content allows for the formation of a Cr-oxide, Al-oxide bilayer. This Cr-oxide, Al-oxide bilayer is assumed to have benefits including faster passivation, reduced oxygen diffusion and Cr-depletion and may act as a tritium permeation barrier for nuclear fuel cladding applications.

The oxidation resistance of FeCrAl based on alloying composition and oxidizing conditions is predicted using a combinatorial experimental and artificial intelligence approach. A neural network (NN) classification model was trained on the experimental FeCrAl dataset produced at GE Research. Furthermore, using the SHapley Additive exPlanations (SHAP) explainable artificial intelligence (XAI) tool, we explore how the NN can showcase further material insights that are unavailable directly from a black-box model. We report that high Al and Cr content forms protective oxide layer, while Mo in FeCrAl creates thick unprotective oxide scale that is vulnerable to spallation due to thermal expansion. Graphical abstract

The effect of 0, 1, 3wt.% Mo addition on the corrosion behavior of Fe21Cr5.5Al was studied through steam tests at 400 °C and 1200 °C. At 400 °C, Mo addition leads to non-protective Fe-Mo oxide growth on the surface, compromising protective uniform Cr oxide growth and subsequent Al2O3 layers as in the Mo-free specimen. At 1200 °C, the presence of Mo causes the spallation of the surface oxide scale. However, a thin alumina surface film was present. Current results show that Mo addition is detrimental to the corrosion resistance of FeCrAl alloy under both operational and accidental temperature scenarios.

The ever-increasing interest in multicomponent alloys over the last decade is attributed to their intriguing structural properties. Nonetheless, these materials do hold potential where novel functional properties, such as high specific heat at extremely low temperatures, are required for applications, such as the cryogenic regenerator bed, that have been predominantly dependent on rare earth metals and lead. Given the enormous design space of alloy compositions, spanning over half a trillion possible compositions, and consisting of three to six principal elements, machine-learning and data-guided methods are the emergent techniques to address such materials discovery challenges. Here, we implement a gradient boost regressor to estimate the specific heat capacity of these alloys, but more importantly to understand the key material descriptors driving the predictions. The mean lattice constant and the valence electron concentration are the most impactful descriptors, since they directly influence vibrational frequencies and electron–phonon coupling, both of which are crucial material features for specific heat of multicomponent alloys. Additionally, our workflow also establishes the need for an extensive and homogeneous dataset to enable accurate ML-based predictions for material properties.

Abstract Traditionally, FeCrAl alloys played an important role in high-temperature applications due to their ability to form a passive Al oxide film at temperatures above ~800°C. Recently, FeCrAl alloys became of interest for the application of accident tolerant nuclear fuel cladding. This study covers work done at GE Research for better understanding the role of Al, Cr, and Mo in oxidation kinetics and thermodynamics. Several models and commercial prototype alloys have been tested in hydrothermal corrosion autoclave loops, at low temperature steam exposure (~400°C), high temperature steam exposure (~1000°C or higher), and high temperature air exposures. The results provide insights on how chromium and aluminum play a significant role in both high temperature and low temperature oxidation of FeCrAl. Additionally, machine learning tools are used to gain further insights on both predicting future optimized chemistries for balancing the properties of hydrothermal corrosion, low and high temperature steam oxidation, and thermal aging (which is exacerbated due to radiation in a nuclear reactor environment). GE plans to use this framework to further optimize the FeCrAl alloy system for use in nuclear reactor environments.

Understanding the complex oxidation behavior of alloys consisting of multiple principal elements is critical to realize their potential for high temperature structural applications. We examine the thermodynamic stability of primary and mixed oxides for one such extensively examined high-entropy alloy (HEA), AlCoCrFeNi, using first-principles calculations. Our predictions indicate that Cr 2 O 3 and Al 2 O 3 are the most stable oxides both at the ground state and elevated temperatures, with the relative ordering for oxide stability at higher temperatures being similar to that at 0 K. The presence of both Al with Cr are most likely to form protective oxide layers, while that produced by Ni and Cr are the least stable. Nevertheless, the gradient of formation energies for the stable mixed Cr 2 O 3 -type oxides with increasing concentration of other principal elements becomes steeper with increasing temperature.

Together with the thermodynamics and kinetics, the complex microstructure of high-entropy alloys (HEAs) exerts a significant influence on the associated oxidation mechanisms in these concentrated solid solutions. To describe the surface oxidation in AlCoCrFeNi HEA, we employed a stochastic cellular automata model that replicates the mesoscale structures that form. The model benefits from diffusion coefficients of the principal elements through the native oxides predicted by using molecular simulations. Through our examination of the oxidation behavior as a function of the alloy composition, we corroborated that the oxide scale growth is a function of the complex chemistry and resultant microstructures. The effect of heat treatment on these alloys is also simulated by using reconstructed experimental micrographs. When they are in a single-crystal structure, no segregation is noted for α-Al2O3 and Cr2O3, which are the primary scale-forming oxides. However, a coexistent separation between Al2O3 and Cr2O3 oxide scales with the Al-Ni- and Cr-Fe-rich regions is predicted when phase-separated microstructures are incorporated into the model.

Understanding the oxidation mechanisms in multi-principal elements and high-entropy alloys (HEAs) is critical for their potential applications in high-temperature oxidative environmnts. In addition to the compositional com-plexity, the counter-diffusion of cations and anions through the oxide contributes to the growth of the oxide scales in these materials. We examine the cationic and anionic diffusion through the stable chromium and aluminum oxides that form in a model HEA using atomistic simulations. In accord with experiments, we find that the tracer cations diffuse faster than the native cations through the oxide scales at high temperature (1000 K to 2000 K) and the dynamics are directly correlated to the respective migration energies of the diffusion pathways. The oxide scale growth is strongly influenced by the presence of tracer/impurity elements in alumina forming alloys relative to those that predominantly form chromia. A geometric analysis of the vacancy-induced diffusion paths for the cation migration relative to the location of the oxygen atoms reveals the influence of the latter on the preferential diffusion pathway, resulting in anisotropy. The predictions offer insights on the diffusion characteristics in the oxide scales formed in HEAs and aid in our understanding of the oxide growth kinetics.

We present results from a stochastic cellular automata (CA) model developed and employed for examining the oxidation kinetics of NiAl and NiAl+Hf alloys. The rules of the CA model are grounded in diffusion probabilities and basic principles of alloy oxidation. Using this approach, we can model the oxide scale thickness and morphology, specific mass change and oxidation kinetics as well as an approximate estimate of the stress and strains in the oxide scale. Furthermore, we also incorporate Hf in the grain boundaries and observe the “reactive element effect”, where doping with Hf results in a drastic reduction in the oxidation kinetics concomitant with the formation of thin, planar oxide scales. Interestingly, although we find that grain boundaries result in rapid oxidation of the undoped NiAl, they result in a slower-growing oxide and a planar oxide/metal interface when doped with Hf.

The rapid transmission of the highly contagious novel coronavirus has been represented through several data-guided approaches across targeted geographies, in an attempt to understand when the pandemic will be under control and imposed lockdown measures can be relaxed. However, these epidemiological models predominantly based on training data employing number of cases and fatalities are limited in that they do not account for the spatiotemporal population dynamics that principally contributes to the disease spread. Here, a stochastic cellular automata enabled predictive model is presented that is able to accurate describe the effect of demography-dependent population dynamics on disease transmission. Using the spread of coronavirus in the state of New York as a case study, results from the computational framework remarkably agree with the actual count for infected cases and deaths as reported across organizations. The predictions suggest that an extended lockdown in some form, for up to 180 days, can significantly reduce the risk of a second wave of the outbreak. In addition, increased availability of medical testing is able to reduce the number of infected patients, even when less stringent social distancing guidelines and imposed. Equipping this stochastic approach with demographic factors such as age ratio, pre-existing health conditions, robustifies the model to predict the transmittivity of future outbreaks before they transform into an epidemic.