Search NASA⌕ Search

SEARCH · Search NASA

Results for “steel microstructure”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 397 records · Page 22

Deuterium trapping mechanisms in reduced activation ferritic martensitic steels and their correlation with mechanical strengthening

Development of high-strength materials often involves introduction of additional strengthening microstructures that also serve as tritium trapping sites. Such additions in fusion material development could degrade the fuel efficiency in fusion reactors and raise radiological concerns. The contribution of individual microstructure features in hydrogen trapping must be evaluated to ensure fuel efficiency and radiological safety. This study explores the mechanistic origins of deuterium trapping in reduced-activation ferritic–martensitic steels and its correlation to mechanical strengthening. A series of model alloys and engineering steels were fabricated and subjected to different heat treatments to control deuterium trapping site density. Deuterium retention was evaluated using D 2 gas charging and thermal desorption spectroscopy, focusing on the role of grain boundary, dislocation, M 23 C 6 precipitates, and TiC precipitates. Multiscale microstructure characterization and synchrotron X-ray diffraction were performed to characterize microstructure, which was correlated to the deuterium retention property. Results show that TiC precipitates exhibit the highest deuterium trapping capacity, followed by M 23 C 6 precipitates. Dislocation and grain boundary demonstrate the lowest and similar efficiencies. Furthermore, the relationship of trapping quantity and mechanical strengthening of these microstructure features was quantified, demonstrating that TiC precipitates offer highest deuterium trapping per unit of mechanical strengthening.

Retention↗

Cold spray manufacturing of oxide-dispersion strengthened (ODS) steels using gas-atomized and ball-milled 14YWT powders

The cold spray deposition process has been investigated for the manufacture of 14YWT oxide-dispersion strengthened (ODS) steel, a nanostructured ferritic alloy (NFA), using gas-atomized and ball-milled feedstock powders. Cold spraying of the gas-atomized powder resulted in a thick dense deposit, but annealing them above 900 °C induced grain growth and heterogeneous precipitation of nanoparticles, thus resulting in lower hardness (~ 170 HV) compared to 14YWT ODS steel manufactured by the conventional method. The ball-milled powder (Fe-14Cr-3W-0.4Ti-0.2Y-0.125O) was cryomilled in liquid N 2 to achieve small particle sizes followed by annealing in a H 2 /Ar environment at 1000 °C and 1100 °C to reduce the hardness from ~ 8 GPa to ~ 5 GPa. Both smaller particle sizes and lower hardness of annealed powders made them amenable to the cold spray deposition process, resulting in thick and dense deposits, while retaining the favorable fully solutionized microstructure of the ball-milled powders. The microstructure of the as-deposited ODS exhibited very fine grain sizes (50–250 nm) with a uniform dispersion of Y-Ti-O nanoprecipitates in the ferritic steel matrix. The hardness values of the cold spray deposits using the powders annealed at 1000 °C and 1100 °C was 770 HV and 496 HV, respectively. Post-heat treatment of the as-deposited material up to 1100 °C showed a high-density microstructure with uniformly dispersed nanoscale oxide particles. The hardness of the annealed deposits up to 1000 °C had a notably higher hardness than bulk 14YWT ODS steel manufactured by conventional methods. The study demonstrates that cold spray deposition of the microstructurally-tailored feedstock powders can be used to successfully manufacture ODS steel with microstructure and properties comparable to those produced by the conventional methods, while providing for an attractive option for the more rapid and cost-effective manufacturing of ODS steel cladding tubes.

14YWT↗

Data-driven high-fidelity 2D microstructure reconstruction via non-local patch-based image inpainting

Microstructure reconstruction problems are usually limited to the representation with finitely many number of phases, e.g. binary and ternary. However, images of microstructure obtained through experimental, for example, using microscope, are often represented as a RGB or grayscale image. Because the phase-based representation is discrete, more rigid, and provides less flexibility in modeling the microstructure, as compared to RGB or grayscale image, there is a loss of information in the conversion. In this paper, a microstructure reconstruction method, which produces images at the fidelity of experimental microscopy, i.e. RGB or grayscale image, is proposed without introducing any physics-based microstructure descriptor. Furthermore, the image texture is preserved and the microstructure image is represented with continuous variables (as in RGB or grayscale images), instead of binary or categorical variables, which results in a high-fidelity image of microstructure reconstruction. The advantage of the proposed method is its quality of reconstruction, which can be applied to any other binary or multiphase 2D microstructure. The proposed method can be thought of as a subsampling approach to expand the microstructure dataset, while preserving its image texture. Moreover, the size of the reconstructed image is more flexible, compared to other machine learning microstructure reconstruction method, where the size must be fixed beforehand. In addition, the proposed method is capable of joining the microstructure images taken at different locations to reconstruct a larger microstructure image. A significant advantage of the proposed method is to remedy the data scarcity problem in materials science, where experimental data is scare and hard to obtain. The proposed method can also be applied to generate statistically equivalent microstructures, which has a strong implication in microstructure-related uncertainty quantification applications. The proposed microstructure reconstruction method is demonstrated with the UltraHigh Carbon Steel micrograph DataBase (UHCSDB).

97 MATHEMATICS AND COMPUTING↗

Automated high-throughput tensile testing reveals stochastic process parameter sensitivity

The mechanical properties of additively manufactured metals tend to show high variability, due largely to the stochastic nature of defect formation during the printing process. This study seeks to understand how automated high throughput testing can be utilized to understand the variable nature of additively manufactured metals at different print conditions, and to allow for statistically meaningful analysis. This is demonstrated by analyzing how different processing parameters, including laser power, scan velocity, and scan pattern, influence the tensile behavior of additively manufactured stainless steel 316L utilizing a newly developed automated test methodology. Microstructural characterization through computed tomography and electron backscatter diffraction is used to understand some of the observed trends in mechanical behavior. Specifically, grain size and morphology are shown to depend on processing parameters and influence the observed mechanical behavior. In the current study, laser-powder bed fusion, also known as selective laser melting or direct metal laser sintering, is shown to produce 316L over a wide processing range without substantial detrimental effect on the tensile properties. Ultimate tensile strengths above 600 MPa, which are greater than that for typical wrought annealed 316L with similar grain sizes, and elongations to failure greater than 40% were observed. As a result, it is demonstrated that this process has little sensitivity to minor intentional or unintentional variations in laser velocity and power.

36 MATERIALS SCIENCE↗

NEUP Final Report: Multilayer Composite Fuel Cladding and Core Internals for LWR Performance Enhancement and Severe Accident Tolerance

Accident tolerance of LWR fuels and structures is of paramount importance, as highlighted by the accident at the Fukushima Daiichi nuclear power station. The ability of fuel cladding and core internals to resist runaway oxidation during a beyond design basis accident (BDBA), as well as to minimize corrosion during steady state operation and design basis accidents (DBAs), determines its degree of accident tolerance. A gap was identified in the accident tolerant fuel (ATF) concepts under consideration, as most consisted either of coatings which may not hold their integrity under prolonged operation, or different materials altogether which incur significant neutronic penalties. A compromise was therefore sought – by minimizing the amount of coating material while ensuring a strong microstructural bond, it was thought that a Zircaloy-steel layered composite would achieve the best of both approaches.The goal of this NEUP project was to develop a multi-metallic layered composite (MMLC) tailored to addressing accident tolerance of LWR fuel cladding and core internal structures using an innovative fabrication technology. The MMLC developed in this program is expected to enhance the accident tolerance of LWRs, thereby reducing the cost by recovering lost operating margins and/or increasing operating windows of peak cladding temperature, peak linear power, reducing steady-state corrosion, and enhancing severe accident tolerance.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Additive Manufacture of Multi-Functional, Large-Scale Components

The poster summarizes the main results for a LDRD focused on developing a arc-based additive manufacturing (AM) system capable of fabricating large components with a composition gradient otherwise known as functionally graded materials (FGMs). FGMs are capable of addressing local extreme conditions in an optimal and cost-effective manner. The AM system (referred to as multi-Wire Arc Additive Manufacturing or m-WAAM) consisted of modifying a standard automated gas tungsten arc welding system to deliver multiple welding wires to the weld pool at controlled rates to deposit desired alloy compositions at critical locations. Numerous shapes and composition gradients were fabricated with the system and the effect of various deposition parameters on the nature of the composition profile were evaluated. Additionally, heat treatment of the FGM was performed to develop graded microstructures which should enhance dynamic impact performance of inexpensive steels. The project highlights the potential of m-WAAM to efficiently fabricate FGMs. However, this project only explored the science behind the technique and additional development work will be required to develop a fully-functional system that can build more complex components.

36 MATERIALS SCIENCE↗

Understanding radiation effects in friction stir welded MA956 using ion irradiation and a rate theory model

We report an outstanding challenge in the manufacturing and joining of oxide dispersion strengthened steels is retaining the nanofeatures in the alloy throughout the fabrication and welding process. MA956 was friction stir welded with two different sets of welding parameters, resulting in a medium and high heat input. After welding, 5 MeV Fe ++ ion irradiations were performed at doses ranging from 50 to 200 dpa in the temperature range of 400 to 500°C. Post-irradiation characterization was performed with scanning transmission electron microscopy and energy-dispersive x-ray spectroscopy to investigate the Y-Al-O dispersoids, voids, and dislocations. After welding, the dispersoid microstructure coarsened, resulting in fewer and larger dispersoids regardless of heat input. After irradiation, the dispersoid behavior in the welded material was sensitive to temperature, exhibiting growth behavior attributed to Ostwald coarsening at 500 °C but a mixture of nucleation and more muted growth at 400 and 450 °C, attributed to competing mechanisms of radiation-enhanced diffusion and Ostwald coarsening. Void swelling correlated to heat input; being more prevalent in the welded conditions occurring at lower doses and in higher values relative to the base material. The low values of swelling despite microstructure coarsening caused by welding demonstrate the excellent swelling resistance of MA956, even after welding with the highest swelling values of 0.5% noted in the stir zone high heat input condition at 450 °C, 200 dpa. The dislocation behavior was inconsistent: the strongest trend was that network density was higher for welded versus base material, and an increase in loop diameter with temperature was observed. A rate theory model based on the observed microstructure suggests at high temperature interstitial loss to sinks was more likely to be dominant compared to mutual annihilation via point defect recombination, because of an increase of the radiation diffusion coefficient with temperature regardless of initial welded microstructure.

36 MATERIALS SCIENCE↗

Consistency of fatigue crack growth behavior of pipeline and low-alloy pressure vessel steels in gaseous hydrogen

Here, this study investigates the fatigue crack growth rate (FCGR) behavior of pipeline and low-alloy pressure vessel steels in high-pressure gaseous hydrogen. Despite a broad range of yield strengths and microstructures ranging from ferrite/pearlite, acicular ferrite, bainite, and martensite, the FCGR in gaseous hydrogen remained consistent (falling within a factor of 2–3). Steels with higher fractions of pearlite, typical of older vintage pipeline steels, exhibited modestly lower crack growth rates in gaseous hydrogen compared to steels with lower fractions of pearlite. Crack growth rates in these materials exhibit a systematic dependence on stress ratio and partial pressure of hydrogen, as captured in the recently published fatigue design curves in ASME B31 code case 220 for pipeline steels and ASME BPVC code case 2938 for pressure-vessel steels.

08 HYDROGEN↗

Corrosion testing needs and considerations for additively manufactured materials in nuclear reactors

Metal additive manufacturing holds significant promise as an enabling technology for the 21st century nuclear energy industry. Metal additive manufacturing (MAM) can allow the fabrication of novel materials and innovative component designs that are not achievable through conventional manufacturing. Due to its very different fabrication methods, as-fabricated MAM components are characteristically different from conventionally manufactured components. MAM materials exhibit very different microstructures from conventional cast or wrought materials. For example, austenitic stainless steels fabricated by laser powder bed fusion exhibit columnar grain structures, dislocation cell structures, and melt pool fingerprints. In addition, MAM fabrication may result in defects such as porosity, incomplete processing of the feedstock (e.g., lack of fusion in melt-based methods), and oxide inclusions. Heat treatments may further evolve the microstructure, microsegregation, and stresses within the component. Furthermore, MAM components have a rough surface with feature sizes on the order of the feedstock material, as opposed to smooth surfaces resulting from conventional machining and forming operations. As a result, the corrosion behavior of MAM components will likely be significantly different from that of conventionally formed components. Corrosive environments for structural materials within advanced reactor environments include molten fluoride and chloride salts, liquid sodium and lead-bismuth, and high-temperature helium. The Advanced Materials and Manufacturing Technologies program within the Department of Nuclear Energy in the United States Department of Energy is assessing the unique concerns of MAM component corrosion and testing methodologies in advanced nuclear reactor environments. We discuss these concerns and testing strategies in this paper.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Manufacturing ODS Steels from GARS Powders by Friction Consolidation and Extrusion

Previous research suggested that friction-based processing is a promising method for fabricating oxide dispersion-strengthened (ODS) steel. In this study, we combined friction consolidation and friction extrusion to successfully manufacture ODS steel rods using precursor powder made with gas atomization reaction synthesis. We examined the microstructure evolution from the initial powder to the final extruded rod, which revealed the dispersion process of Y. Additionally, by comparing the microstructures of three rods extruded at different temperatures, we showed that low-temperature friction extrusion effectively enhanced microstructure uniformity and prevented grain coarsening, leading to improved mechanical properties. Furthermore, our findings provide practical guidelines for adjusting processing parameters in the production of ODS steel using friction-based processing.

36 MATERIALS SCIENCE↗

Characterization and Quantification of Radiation-Induced Clusters/Precipitates in RPV Steels Using STEM-EDS and Machine Learning

Over the operational lifespan of a nuclear reactor, reactor pressure vessel (RPV) steels are subjected to significant neutron irradiation, resulting in complex microstructural changes and the consequent degradation of mechanical properties. Various physically motivated correlation models have been developed to predict neutron irradiation-induced embrittlement of RPVs under different irradiation conditions. However, the efficient and accurate characterizations and quantification of radiation-induced clusters in RPVs are still challenging, which will affect the precision of the predictive models for embrittlement of RPV components. In the DOE Visiting Faculty Program (VFP) research work at Oak Ridge National Lab (ORNL), I integrate machine learning to aid Scanning Transmission Electron Microscopy – Energy Dispersive X-ray Spectroscopy (STEM-EDS) analyses, which improve the characterization and quantification of radiation-induced clusters in RPV steels, thereby enabling more accurate predictions of material behavior under irradiation. The surveillance base- and welded- RPV steels were annealed at various temperatures of 340 °C, 450 °C and 500 °C for up to 168 hours, respectively. Afterwards, I have characterized radiation-induced clusters using advanced STEM-EDS techniques and subsequently applying machine learning algorithms to analyze and refine STEM-EDS datasets, enhancing the quantification of clusters compositions and distributions. In the end, an efficient workflow for integrating STEM-EDS data analysis with machine learning to address challenges including noise reduction has been developed. The completion of this VFP work will support bridge critical gaps in the accurate quantification of radiation-induced clusters in RPV steels using STEM-EDS and support the development of more precise models for predicting RPV embrittlement in the Light Water Reactor Sustainability program supported by Department of Energy and enhancing the collaboration between ORNL and Alred University. The outcome of the VFP project will leverage a few research papers submission to peer-reviewed journals in the relevant scientific field and a few oral presentations at national and international conferences.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Postweld Heat Treatment on Toughness of Electric- Resistance Welded X70 Steel

In this report the effect of postweld heat treatment (PWHT) on Charpy V-notch impact toughness of a highfrequency electric-resistance welded (HF-ERW) grade X70 pipeline steel is investigated. PWHT thermal cycles are simulated using the Gleeble on the as-welded specimens. Microstructure, along with crystallographic texture and microhardness, is characterized in specimens that are impact tested at –5°C (23°F), –30°C (–22°F), and –45°C (–49°F). The impact toughness values show a wide scatter band and decrease with the increasing peak temperature of the PWHT. For higher peak temperatures, the microstructure of the heat-affected zone (HAZ) gradually changed from equiaxed ferrite to bainitic ferrite. Furthermore, the prior austenite grain size (PAGS) increases with the increasing peak temperature. The density of high-angle grain boundaries decreases, and the fraction of cleavage planes {100} parallel to the impact fracture plane increases for higher peak temperatures of the PWHT

36 MATERIALS SCIENCE↗

Long-term thermal aging behavior and strength reduction in a laser powder bed fusion 316H stainless steel

The long-term thermal stability of structural alloys is essential for ensuring the safe and reliable operation of nuclear reactors and other power plants. While extensive research has explored the effects of thermal aging on conventional stainless steels, the behavior of additively manufactured (AM) alloys remains less understood. This study examines the thermal aging response of laser powder bed fusion (LPBF) 316H stainless steel (SS) at temperatures ranging from 550 °C to 750 °C over durations of up to 10,000 h (approximately 1.14 years). Advanced characterization techniques, including electron microscopy and synchrotron X-ray diffraction, were used to investigate dislocation recovery and phase evolution. Based on these findings, a time-temperature-precipitation (TTP) diagram was developed for LPBF 316H SS, revealing a 10- to100-fold acceleration in precipitation kinetics compared to wrought 316H SS. A physics-informed model was calibrated using the short-term experimental data, enabling predictions of average precipitate sizes, volume fractions of M 23 C 6 and Laves phases, and changes in molybdenum solute concentration for aging up to 1 × 10⁶ h (114 years). These microstructural insights were further utilized to estimate yield strength and extrapolate strength reduction factors over the extended aging period. Despite the accelerated aging kinetics, LPBF 316H SS demonstrated superior yield strength retention compared to its wrought counterpart. In conclusion, this study establishes a framework for evaluating long-term performance using short-term experimental data and supports the accelerated qualification of AM materials for high-temperature structural applications.

Laser powder bed fusion↗

Technological aspects in blanket design: Effects of micro-alloying and thermo-mechanical treatments of EUROFER97 type steels after neutron irradiation

Presently available data on neutron irradiation damage raise doubts on the feasibility of using EUROFER97 steel for a water-cooled starter blanket in a DEMO reactor, since the ductile-to-brittle transition temperature (DBTT) increases significantly for irradiation temperatures below 350°C. The additional DBTT shift caused by H and He transmutation can only be estimated based on very few results with isotopically tailored EUROFER97 steel. Conservative calculations show that the DBTT of EUROFER97 steel could exceed the operating temperature in water-cooled starter blankets within a relatively short time period. This paper presents results from a EUROfusion funded irradiation campaign that was performed in the High Flux Isotope Reactor at Oak Ridge National Laboratory. The paper compares ten newly developed reduced activation ferritic-martensitic (RAFM) steels irradiated to a nominal dose of 2.5 dpa at 300°C. The post-irradiation experiments using Small Specimen Test Technology included hardness, tensile, and fracture mechanics tests combined with fractography and microstructure analysis are presented. Results show that micro-alloying EUROFER97-type steels influenced the mechanical properties but a dominating impact on irradiation damage resistance could not be identified. In contrast, specific thermo-mechanical treatments lead to better DBTT behavior. Discussion about irradiation response to heat treatment conditions is also given. Here, despite requiring data also at high dpa values, the results indicate that with these modified materials an increased lifetime and potentially also an increased operating temperature window can be achieved compared to EUROFER97.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Deformation Behavior of a Double Soaked Medium Manganese Steel with Varied Martensite Strength

The effects of athermal martensite on yielding behavior and strain partitioning during deformation is explored using in situ neutron diffraction for a 0.14C–7.14Mn medium manganese steel. Utilizing a novel heat treatment, termed double soaking, samples with similar microstructural composition and varied athermal martensite strength and microstructural characteristics, which composed the bulk of the matrix phase, were characterized. It was found that the addition of either as-quenched or tempered athermal martensite led to an improvement in mechanical properties as compared to a ferrite plus austenite medium manganese steel, although the yielding and work hardening behavior were highly dependent upon the martensite characteristics. Specifically, athermal martensite was found to promote continuous yielding and improve the work hardening rate during deformation. The results of this study are particularly relevant when considering the effect of post-processing thermal heat treatments, such as tempering or elevated temperature service environments, on the mechanical properties of medium manganese steels containing athermal martensite.

36 MATERIALS SCIENCE↗

Coupling physics in machine learning to predict properties of high-temperatures alloys

Abstract High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex high-temperature alloys with an example of the 9–12 wt% Cr steels yield strength. We have incorporated synthetic alloy features that capture microstructure and phase transformations into the dataset. Identified high impact features that affect yield strength of 9Cr from correlation analysis agree well with the generally accepted strengthening mechanism. As a part of the verification process, the consistency of sub-datasets has been extensively evaluated with respect to temperature and then refined for the boundary conditions of trained ML models. The predicted yield strength of 9Cr steels using the ML models is in excellent agreement with experiments. The current approach introduces physically meaningful constraints in interrogating the trained ML models to predict properties of hypothetical alloys when applied to data-driven materials.

36 MATERIALS SCIENCE↗

Machine-Learning Microstructure for Inverse Material Design

Metallurgy and material design have thousands of years’ history and have played a critical role in the civilization process of humankind. The traditional trial-and-error method has been unprecedentedly challenged in the modern era when the number of components and phases in novel alloys keeps increasing, with high-entropy alloys as the representative. New opportunities emerge for alloy design in the artificial intelligence era. Here a successful machine-learning (ML) method is developed to identify the microstructure images with eye-challenging morphology for a number of martensitic and ferritic steels. Assisted by it, a new neural-network method is proposed for the inverse design of alloys with 20 components, which can accelerate the design process based on microstructure. The method is also readily applied to other material systems given sufficient microstructure images. This work lays the foundation for inverse alloy design based on microstructure images with extremely similar features.

36 MATERIALS SCIENCE↗