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Results for “Phase transformations”

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.

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At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

EUTERPE: A global gyrokinetic code for stellarator geometry

The current state of the EUTERPE code is described with emphasis on the implemented models and their numerical implementation. The code solves the multi-species electromagnetic gyrokinetic equations in the full volume of a three-dimensional domain. Noise reduction of the particle-in-cell method is achieved by using a δf-method and Fourier filters. The field equations are discretized with B-splines and the resulting system of equations is solved iteratively. For linear simulations a phase-factor transformation is applied in order to strongly reduce the necessary grid resolution. Apart from the full gyrokinetic model, other numerically less expensive hybrid models are also implemented. They are mainly tailored for comparison with fluid theory and for studying the interaction of the bulk plasma with fast particles. The code is parallelized for CPUs by particle and domain decomposition. Good scalability up to several thousand nodes is demonstrated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A structured framework for predicting sustainable aviation fuel properties using liquid-phase FTIR and machine learning

Sustainable aviation fuels have the potential to improve efficiency, reduce emissions, and enhance energy security. To help identify viable sustainable aviation fuels and accelerate research, machine learning models have been developed to predict relevant physicochemical properties. However, many models have limited applicability, leverage data from complex analytical techniques with confined spectral ranges, or use feature decomposition methods that offer limited interpretability. Using liquid-phase Fourier Transform Infrared (FTIR) spectra, this study presents a structured method for creating accurate and interpretable property prediction models for neat molecules, aviation fuels, and blends. Liquid FTIR spectra can be collected quickly and consistently, offering high reliability, sensitivity, and component specificity using less than 2 ml of sample. The method first decomposes FTIR spectra into fundamental building blocks using non-negative matrix factorization (NMF) to enable scientific analysis of FTIR spectra attributes and fuel properties. The NMF features are then used to create five ensemble models for predicting final boiling point, flash point, freezing point, density at 15°C, and kinematic viscosity at -20°C. All models were trained using experimental property data from neat molecules, aviation fuels, and blends. The models accurately predict key properties across a broad range of neat molecules and representative fuels and blends, while enabling interpretation of relationships between compositional elements, such as functional groups or chemical classes, and their resulting properties. This demonstrates strong potential to support sustainable aviation fuel research and development. The models and data are available on an interactive web tool.

Fourier transform infrared spectroscopy↗
DOE OSTI2023

A tool and a methodology to use macros for abstracting variations in code for different computational demands

Scientific software used on high-performance computing platforms is in a phase of transformation because of the combined increase in the heterogeneity and complexity of models and hardware platforms. Having separate implementations for different platforms can easily lead to combinatorial explosions; therefore, the computational science community has been looking for mechanisms to express code through abstractions that can be specialized for different platforms. Most existing approaches use template meta-programming in C++, and are, therefore language specific. Here, we have developed a tool that uses customized expansion of macros to mimic some of C++ behavior in other languages. It enables unification of any code variants that may be necessary to run efficiently on different target architectures and different computational environments through use of macros with multiple alternative definitions and ability to arbitrate on definition selection for expansion. Combined with two other tools, a custom runtime, and a user specified recipe translator, our custom macroprocessor becomes a part of an overall performance portability solution that does not depend on any specific programming language. We also use macros as code-shorthand that lets code snippets become building blocks that allow variations in control flow to explore performance options. We demonstrate use of macros in Flash-X, a multiphysics multicomponent code with many Fortran legacy components derived from an earlier community code FLASH.

Heterogenous computing↗
DOE OSTI2021

Phase evolution during reactive flash sintering of Li 6.25 Al 0.25 La 3 Zr 2 O 12 starting from a chemically prepared powder

Here, reactive flash sintering (RFS) of a chemically prepared multiphase precursor powder was performed to fabricate Li 6.25 Al 0.25 La 3 Zr 2 O 12 (Al-LLZO) ceramics. This approach allowed for obtaining single-phase dense samples in a remarkably short processing time of 30 s, at a furnace temperature of 600 °C, with an electric field of 50 V cm -1 and a current limit of 150 mA mm -2 . The ceramics display high bulk conductivity of 0.18 mS cm -1 at room temperature. Furthermore, phase evolution is studied by in-situ X-ray diffraction during: i) conventional heating and ii) RFS under current rate mode. As expected, the intermediate phases progressively dissolved into the Al-LLZO matrix by conventional heating. On the other hand, RFS promoted the growth of the intermediate La 2 Zr 2 O 7 , an effect that was overcome by the thermally driven formation of Al-LLZO at higher temperatures. The observed different reaction pathway suggests that RFS can be used for stabilizing phases that are not thermodynamically favored upon conventional heating.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Reactive flash sintering of TiZrN and TiAlN ternary metal nitrides

Here, this study demonstrated the reactive flash sintering (RFS) for two powder mixtures: TiN-ZrN (both conducting) and AlN-TiN (TiN conducting but AlN insulating), targeting ternary metal nitrides (TMN) of Ti 0.5 Zr 0.5 N and Ti 0.5 Al 0.5 N, respectively. A constant volage and pressure (e.g., 8 V DC, ∼15 MPa) at room temperature triggered the flash (current density up to 27 A/mm 2 ) without pre-heating, and the entire RFS process finished in a few minutes. For TiZrN, the flash was instantaneous whereas for TiAlN, there was a long incubation before the flash followed by a quick and dramatic flash. Both conventional ex situ XRD and in situ synchrotron study had been carried out. They showed a uniform Ti 0.57 Zr 0.43 N solution formed in RFS and persisted upon cooling, while (Ti, Al) N solid solution formed at high temperature was not stable and likely went through a very quick phase separation in the cooling process. The final products from RFS had been characterized using SEM/EDS for microstructure. Both TiZrN and TiAlN were dense. Distribution of Ti, Zr, and N was uniform for TiZrN; for TiAlN, Ti and N distribution was uniform, while association of Al with oxygen was observed. TGA-DSC revealed the onset oxidation temperature for TiZrN was comparable to TiN and ZrN, while it was higher by ∼200 °C for TiAlN, likely due to the formation of Al 2 O 3 . In terms of mechanical properties such as hardness or fracture toughness, forming a single-phase solid solution (like TiZrN) does not offer obvious benefits. while large grain size from RFS seemed to be unfavorable. Future optimization of RFS condition and in-depth study by both experiments and simulation are needed to fully understand the composition-processing-structure-property relationships for such TMN from the reactive flash sintering process.

Flash Sintering↗
DOE OSTI2020

Thermophysical properties and reversible phase transitions in yttrium hydride

In this study, the thermophysical properties of yttrium hydrides were investigated as a function of temperature (room temperature [RT] to 700°C) and hydrogen concentration (H/Y ratio ranges from 1.52 to 1.93). The results indicate that at the temperatures below 300°C, the hydrogen content did not have a significant influence on the thermal expansion, while the specific heat capacity, the thermal diffusivity, and the calculated thermal conductivity were slightly higher for the larger H/Y ratio. Between 300°C and 700°C, a reversible second-order endothermic transition in all measured thermal properties was observed. It was also found that the onset temperatures of the observed transition varied, with the composition having inverse dependence on the hydrogen content. The phase transition from ( α - Y + δ -YH x ) mixed phase to the δ -YH x single phase was excluded from the main contributors to the observed transition. An attempt was made to explain the behavior of the thermophysical properties at higher temperatures by considering the order–disorder transition as a result of hydrogen redistribution.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Phase evolution of U-Zr system in a thermal cycling neutron diffraction experiment: as-cast U-35Zr and U-50Zr

Here, phase evolution of as-cast U-35 wt% Zr and U-50 wt% Zr alloys during thermal cycling (303–1073 K) was investigated using in-situ neutron diffraction. Analysis was performed using Rietveld crystal and microstructure refinements from time-of-flight neutron diffraction data, with a focus on evolution of lattice parameter, atom ordering, unit cell volume, and weight fractions during the thermal cycling. Disordered δ-UZr 2 and residual γ-(U,Zr) phase are retained in the field of δ of the U-Zr equilibrium phase diagram for the U-50Zr sample, and in the field between δ and α-U for the U-35Zr sample. The α phase that is present in the reported phase diagrams of U-Zr was not observed in the diffraction patterns. The evolutions of lattice parameter and unit cell volume of δ and γ are affected by both thermal expansion and chemistry.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Determination of single crystal thermal expansion in Uranium-6wt%Niobium shape memory alloy using in-situ diffraction and modeling of textured polycrystalline samples

In-situ neutron diffraction and ex-situ dilatometry experiments were performed on a chemically banded, quenched uranium-6wt% niobium shape memory alloy to study the impact of deformation-(detwinning-) induced texture on the evolution of thermally induced strains (and associated stresses). Thermal heating and cooling cycles between 5K and 473K were performed in-situ with neutron diffraction, and the lattice strain evolution is reported for the observed monoclinic α" phase. Comparisons between the measured diffraction strains and macroscopic dilatometry experiments reveal relationships between micro- and macro-level thermal expansion. Softening of the texture during heating suggests that twin boundary motion can accommodate the large internal thermal strains which are approximately 2% greater than that typically observed during the 175K heating interval used to age more randomly oriented polycrystalline material. Assuming weak constraint of neighboring grains in randomly textured polycrystals, the single crystal thermal expansion tensor is extracted from measurements of lattice strains over the range 5K to 473K. Predictions of polycrystalline thermal expansion, based upon this single crystal thermal expansion tensor, are shown to compare favorably with bulk thermal expansion observations of the as quenched microstructure. However, such a lower-bound estimate is insufficient to explain all aspects of the behavior of the textured material, where the matrix is not isotropic. In conclusion, it is hypothesized that relaxation processes which occur within the quenched microstructure during heating are responsible for the distinct thermal expansion behavior observed during the initial heating cycle compared to cooling and subsequent cycling.

36 MATERIALS SCIENCE↗
DOE OSTI2020

An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction

Micrograph quantification is an essential component of several materials science studies. Machine learning methods, in particular convolutional neural networks, have previously demonstrated performance in image recognition tasks across several disciplines (e.g. materials science, medical imaging, facial recognition). Here, we apply these well-established methods to develop an approach to microstructure quantification for kinetic modeling of a discontinuous precipitation reaction in a case study on the uranium-molybdenum system. Prediction of material processing history based on image data (classification), calculation of area fraction of phases present in the micrographs (segmentation), and kinetic modeling from segmentation results were performed. Results indicate that convolutional neural networks represent microstructure image data well, and segmentation using the k-means clustering algorithm yields results that agree well with manually annotated images. Classification accuracies of original and segmented images are both 94% for a 5-class classification problem. Kinetic modeling results agree well with previously reported data using manual thresholding. The image quantification and kinetic modeling approach developed and presented here aims to reduce researcher bias introduced into the characterization process, and allows for leveraging information in limited image data sets.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Austenitic parent grain reconstruction in martensitic steel using deep learning

In this work we develop a deep convolutional architecture to estimate the prior austenite structure from observed martensite electron backscatter diffraction micrographs. A novel data augmentation strategy randomizes the global reference coordinate system which makes it possible to train our model from only four micrographs. The model is much faster than algorithmic approaches and generalizes well when applied to micrographs of a different material. Empirical evidence suggests the efficacy of the model depends on the scale of the microstructure and receptive field of the vision model. Furthermore, this work demonstrates that modern computer vision approaches are well suited for capturing complex spatial-orientation patterns present in orientation imaging micrographs.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Predicting Geometric Influences in Metal Additive Manufacturing

Metal additive manufacturing (AM) has evolved from scientific curiosity to a potential paradigm shift in materials manufacturing owing to its ability to control microstructure and fabricate complex geometries. However, geometry, often ignored as a variable, can have a significant impact on microstructure evolution, thereby indirectly limiting the design flexibility offered by AM. Currently, the process of retaining microstructure with changing geometry relies on trial and error-based processing followed by microstructural evaluation that places significant demands on time and costs associated with experimentation and characterization. We demonstrate herein that lightweight thermal models can be effectively used to predict microstructural changes with geometry. These models can in turn be used as precursors to developing geometry independent scan strategies and achieve microstructure control in various alloy systems. Even if the thermal signatures predicted by the model are not accurate, scan strategies can be developed to mimic the thermal conditions during initial process development. We present a case study on one of the most studied AM alloys: Ti-6Al-4V.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Morphology and elemental composition of a new iron-rich ferrite phase in highly irradiated austenitic steel

Here, elemental composition and morphology of a previously unidentified radiation-induced ferrite phase were investigated in a 300-series steel irradiated by neutrons in-service up to 57.6 dpa. Specimens of 18Cr-10Ni-Ti stainless steel (AISI 321 analog) were cut from a hexagonal wrapper of a fuel assembly irradiated in the BN-350 sodium-cooled fast reactor. An Fe-rich bcc-phase was observed primarily on grain boundaries. In this phase, the concentration of Cr is ~8–12% (compared to ~19% in the matrix), the concentration of Ni is ~1.5–3% (~9% in the bulk material), and the concentration of Mn is ~0.23% (1.3% in the matrix). This Fe-rich phase is distinctly different from the retained-ferrite phase, commonly found in commercial austenitic steels. The extensive appearance of this Fe-rich ferrite on grain boundaries suggests that enhanced surface–intergranular corrosion may occur in water-cooled power reactors, arising from the low Ni, Mn, and Cr concentrations in this phase.

36 MATERIALS SCIENCE↗