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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Materials Data on U(MoO4)2 by Materials Project

UMo2O8 crystallizes in the orthorhombic Pbca space group. The structure is three-dimensional. U4+ is bonded in a 8-coordinate geometry to eight O2- atoms. There are a spread of U–O bond distances ranging from 2.29–2.42 Å. There are two inequivalent Mo6+ sites. In the first Mo6+ site, Mo6+ is bonded in a tetrahedral geometry to four O2- atoms. There are a spread of Mo–O bond distances ranging from 1.79–1.83 Å. In the second Mo6+ site, Mo6+ is bonded in a tetrahedral geometry to four O2- atoms. There is three shorter (1.80 Å) and one longer (1.81 Å) Mo–O bond length. There are eight inequivalent O2- sites. In the first O2- site, O2- is bonded in a distorted bent 150 degrees geometry to one U4+ and one Mo6+ atom. In the second O2- site, O2- is bonded in a distorted bent 150 degrees geometry to one U4+ and one Mo6+ atom. In the third O2- site, O2- is bonded in a distorted bent 150 degrees geometry to one U4+ and one Mo6+ atom. In the fourth O2- site, O2- is bonded in a distorted linear geometry to one U4+ and one Mo6+ atom. In the fifth O2- site, O2- is bonded in a distorted bent 150 degrees geometry to one U4+ and one Mo6+ atom. In the sixth O2- site, O2- is bonded in a distorted bent 150 degrees geometry to one U4+ and one Mo6+ atom. In the seventh O2- site, O2- is bonded in a distorted bent 150 degrees geometry to one U4+ and one Mo6+ atom. In the eighth O2- site, O2- is bonded in a distorted bent 120 degrees geometry to one U4+ and one Mo6+ atom.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on U(FeB3)2 by Materials Project

UFe2B6 crystallizes in the orthorhombic Immm space group. The structure is three-dimensional. U6+ is bonded in a 12-coordinate geometry to sixteen B2- atoms. There are a spread of U–B bond distances ranging from 2.66–2.89 Å. Fe3+ is bonded in a 10-coordinate geometry to ten B2- atoms. There are a spread of Fe–B bond distances ranging from 2.02–2.18 Å. There are two inequivalent B2- sites. In the first B2- site, B2- is bonded in a 6-coordinate geometry to three equivalent U6+, three equivalent Fe3+, and three B2- atoms. There is one shorter (1.70 Å) and two longer (1.79 Å) B–B bond length. In the second B2- site, B2- is bonded in a 9-coordinate geometry to two equivalent U6+, four equivalent Fe3+, and three B2- atoms. The B–B bond length is 1.78 Å.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on U(SiAu)2 by Materials Project

UAu2Si2 crystallizes in the tetragonal P4/nmm space group. The structure is three-dimensional. U6+ is bonded in a 8-coordinate geometry to eight Si4- atoms. There are four shorter (3.27 Å) and four longer (3.32 Å) U–Si bond lengths. There are two inequivalent Au1+ sites. In the first Au1+ site, Au1+ is bonded to four equivalent Si4- atoms to form a mixture of edge and corner-sharing AuSi4 tetrahedra. All Au–Si bond lengths are 2.57 Å. In the second Au1+ site, Au1+ is bonded in a 5-coordinate geometry to five Si4- atoms. There are one shorter (2.41 Å) and four longer (2.51 Å) Au–Si bond lengths. There are two inequivalent Si4- sites. In the first Si4- site, Si4- is bonded to four equivalent U6+ and four equivalent Au1+ atoms to form a mixture of distorted face and edge-sharing SiU4Au4 tetrahedra. In the second Si4- site, Si4- is bonded in a 5-coordinate geometry to four equivalent U6+ and five Au1+ atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2026

U-net architected deep material network training with microstructure local field information

The Deep Material Network (DMN) has recently emerged as a powerful reduced-order modeling framework for simulating the mechanical response of heterogeneous materials such as composites. Unlike most data-driven approaches that directly learn a material’s response under prescribed loading, the DMN acts as a homogenization operator, learning the kinematic constraints and mechanical interactions of the underlying microstructure. However, traditional DMN training relies exclusively on homogenized effective properties derived from Direct Numerical Simulations (DNS), discarding the rich local field data that govern microstructural interactions. In this work, we extend the DMN framework to incorporate such local field information into the offline training process. Utilizing a U-Net architecture, we augment the DMN training objective to include the first and second statistical moments of the local stress fields obtained from linear DNS. This ensures that the learned network topology not only fits the effective stiffness but also accurately reflects the internal local stress and strain partitioning of the microstructure. The results confirm that supervising the localization process during training yields a superior surrogate model, reducing local prediction errors by an order of magnitude and significantly improving generalization to unseen nonlinear constitutive behaviors compared to traditional DMNs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A chromatography chemistry for purifying $\mathrm{Pa}$ from $\mathrm{U-Nb}$ metal alloys

Here we present a chromatography chemistry for purifying protactinium from uranium metal alloys containing weight-percent concentrations of niobium. Niobium is precipitated in 9 M HCl without the co-precipitation of Pa as demonstrated by gamma-ray spectrometry. Protactinium is further purified using Bio-Rad™ anion resin AG® 1-X8 and 9 M HCl + 0.0128 M HF. A Pa/Nb separation factor of 85,000 and a 90% Pa recovery is demonstrated, which is amenable to 231 Pa concentration determination by isotope dilution and 231 Pa/ 235 U model age calculation for nuclear forensics.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

A mesoscale 3D model of irradiated concrete informed via a 2.5 U-Net semantic segmentation

The concrete biological shield in light-water reactors is exposed to neutron and gamma irradiation, which deteriorates the concrete’s mechanical properties in the long term. To assess the irradiation-induced damage, predictive mechanical models are developed and used in parallel with the characterization of irradiated concrete samples. Realistic 3D simulation domains can drastically improve a model’s prediction. In this work, we utilized x-ray computed tomography (XCT) data of a concrete specimen to reconstruct its 3D microstructure. The XCT data shows low contrast between the concrete’s aggregates and cement paste, resulting in poor image segmentation when using traditional unsupervised techniques. To address this issue, we developed and trained a 2.5D U-Net model on only 24 pre-labeled XCT layers to segment 651 layers of the XCT data. The overall F1-score of the model is approximately 96%. Then, we created a 3D finite element (FE) mesh based on the stack of segmented images. The FE model contains radiation-induced expansion, damage, and creep. The constitutive equations are adapted to each phase (aggregates and cement paste). Here, we simulated the effects of neutron irradiation in the concrete specimen as well as the specimen’s mechanical response to uniaxial compression. Finally, model validation was performed using experimental data on similar concrete specimens in the literature.

2.5D U-Net↗
DOE OSTI2020

U-Pb zircon age constraints on the earliest eruptions of the Deccan Large Igneous Province, Malwa Plateau, India

Climate instability driven by emission of volatiles during emplacement of large igneous provinces (LIPs) is frequently invoked as a potential cause of mass extinctions. However, documenting this process in the geologic record requires a holistic understanding of eruption rates, the location of eruptive centers, and potential sources of climate-changing volatiles. We present new chemical abrasion–isotope dilution–thermal ionization mass spectrometry (CA-ID-TIMS) U-Pb zircon geochronology from Malwa Plateau basalts on the northern margin of the Deccan LIP, India. These basalts have been previously interpreted as either an extension of the province's main volcanic stratigraphy or as an independent eruptive center active up to millions of years prior to the main eruptive phase. Our data instead demonstrate that the lower Malwa Plateau basalts are temporally correlative with the first pulse of Deccan volcanism and provide new constraints on its initiation and duration. Paleomagnetic data further indicate that upper Malwa Plateau basalts may be age-equivalent to the second, third, and fourth pulses of Deccan volcanism. The relative thicknesses of age-equivalent packages of basalt are consistent with eruption of the Deccan LIP from a southward-migrating eruptive center. The first eruptive pulse is coeval with a ~200 kyr Late Maastrichtian warming event preserved globally in contemporaneous stratigraphic sections. We propose that the first pulse of Deccan magmatism was more voluminous in the north, where it erupted through organic-rich sedimentary rocks of the Narmada-Tapti rift basin. Thermal metamorphism of these sediments could have been a source of sufficient CO 2 to drive the Late Maastrichtian warming event, which if true would reconcile the apparent dampened warming signals associated with later Deccan eruptive pulses.

58 GEOSCIENCES↗
DOE OSTI2023

Fast model-based scenario optimization in NSTX-U enabled by analytic gradient computation

Model-based optimization offers a systematic approach to advanced scenario planning. In this case, the feedforward-control inputs (actuator trajectories) that are needed to attain and sustain a desired scenario are obtained by solving a nonlinear constrained optimization problem. This class of problems generally minimize a cost function that measures the difference between desired and actual plasma states. Several numerical optimization algorithms, such as sequential quadratic programming, require repeated calculation of the cost function gradients with respect to the input trajectories. Calculating these gradients numerically can be computationally intensive, increasing the time needed to solve the feedforward-control optimization problem. Here, this work introduces a method to analytically calculate these cost function gradients from the current profile evolution model. This can significantly reduce the computational time and allow for fast feedforward-control optimization, which would eventually enable optimal scenario planning between discharges. The performance of the feedforward optimizer with analytical gradients is compared to a traditional optimization algorithm based on numerical gradients for different NSTX-U scenarios. The plasma dynamics in the optimization algorithm are simulated using the Control Oriented Transport SIMulator (COTSIM). Results of the work show that analytical gradients consistently reduce the computation time while achieving trajectories that are comparable to those obtained by traditional optimization algorithms based on numerical gradients.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Analysis of fast-ion losses measured in MAST-U via infrared thermography and a Fast Ion Loss Detector

Fast-ion losses need to be monitored to avoid damage to plasma facing components. In existing experimental devices, the scintillator-based fast-ion loss detector (FILD) is the most advanced diagnostic for measuring fast-ion losses. However, FILDs provide only local information about the losses. Infrared (IR) thermography can be used as a complementary tool for more global monitoring of the deposition of fast-ion losses on the wall, at the expense of no velocity-space resolution. IR cameras measure the temperature of the plasma facing components. This measurement, determined by a combined effect of the thermal plasma, radiation, neutrons and fast-ion losses, can be decomposed to infer the fast-ion load on the tokamak wall. In this manuscript, a workflow to estimate fast-ion losses via IR thermography is applied to the MAST-U spherical tokamak, using a 1D approximation to extract the experimental heat flux on the FILD front face from IR data. To numerically estimate the different contributions to this total heat flux, the field-line tracing environment SMITER is used to calculate the thermal plasma contribution, the orbit-following Monte-Carlo code ASCOT to estimate the fast-ion losses, and bolometry measurements for the radiation. To validate the workflow, two discharges, L-mode plasmas with low MHD activity, were executed using on and off-axis beams, respectively. The experimentally and numerically estimated heat flux are of the same order of magnitude for the on-axis heated scenario, with a strong dependence of the estimated fast-ion losses contribution on the fit to the kinetic profiles used as input. This is also true for the off-axis heated scenario, where the total numerically estimated heat flux is 2.1 or 1.3 times higher than the maximum experimentally estimated heat flux, depending on the ASCOT input used.

FILD↗
DOE OSTI2022

Automated bubble analysis of high-speed subcooled flow boiling images using U-net transfer learning and global optical flow

Capturing and analyzing the bubble dynamics is crucial to improving the understanding of boiling heat transfer mechanisms and predicting boiling heat transfer coefficient and boiling crisis. High speed video (HSV) imaging has been used for decades towards this end. Still, there is no universal approach to quantitatively analyze bubble dynamics from HSV images. In this study, we propose a data-driven post-processing approach to segment, track, and identify wall-attached vapor bubbles from HSV images of the boiling process in subcooled flow conditions. Firstly, we employ a transfer learning framework with a U-Net-based convolution neural network (CNN) architecture to detect and segment bubbles in HSV images of diverse contrast and surface texture using very little data (e.g., 10 images) for training. Then, we evaluate the trained CNN model with 100 ground-truth images, and the validation results show that the model accuracy and precision in detecting the optical footprint of bubbles are higher than 90%. Finally, we suggest a criterion to identify a condensing bubble based on the divergence of the bubble displacement, which is calculated from sequential segmented bubble images using a global optical flow code. Using this combination of machine learning and optical flow, we can identify nucleation sites and track the growth of bubbles nucleating at each site to quantify nucleation site density, nucleation frequency, and other fundamental boiling parameters. The proposed system is validated using results obtained on a special heater, which enables both infrared (IR) thermometry and HSV imaging on a metallic surface. We compare the fundamental boiling parameters obtained by the two different diagnostics. The results show good agreement. In conclusion, the difference between the measurements of nucleation site density, averaged nucleation frequency, and averaged growth time performed with the two techniques is always within ± 20% and mostly ± 10% of the values measured with IR thermometry.

42 ENGINEERING↗
DOE OSTI2021

High temperature mechanical properties of fluorite crystal structured materials (CeO 2 , ThO 2 , and UO 2 ) and advanced accident tolerant fuels (U 3 Si 2 , UN, and UB 2 )

The mechanical interaction between the fuel and cladding that occurs during operation of a nuclear reactor is important to understand as it can lead to cladding failures and release of radioactive material into the coolant. Additionally, in order to develop better models of the pellet-clad mechanical interactions, the mechanical properties of the fuel at relevant operating temperatures, like the elastic moduli, are needed for current and advanced accident tolerant fuels (ATFs). In this work, elevated temperature nanoindentation and resonant ultrasound spectroscopy were used to measure the moduli and hardness of several fluorite materials (CeO 2 , ThO 2 , UO 2 ) and several ATF candidates (ATF) (U 3 Si 2 , UN, UB 2 ). In addition, a comparison of the two techniques was performed in this study to independently validate the mechanical properties.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Assessment of effective elastic constants of U-10Mo fuel: A multiscale modeling and homogenization study

The significant microstructural changes that U-Mo fuel undergoes during operation degrades its mechanical properties and structural integrity. Microstructural evolution entails the formation, evolution, and redistribution of porosity in conjunction with grain refinement. In the present paper, we employ numerical approaches to assess the impact of the various microstructural features—grains, nanoscale intragranular fission gas bubbles, and mesoscale intergranular voids—on the degradation of elastic constants. Phase-field microstructure models are combined with the asymptotic expansion homogenization technique in order to derive the effective elastic constants as a function of porosity and fission density. Here the results are verified and compared against theoretical bounds. Using this approach, elastic degradation in operating nuclear fuels can be quantified when the distributions of microstructural features are known.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Segmentation and Classification of Fission as Pores in Reactor Irradiated Annular U–10Zr Metallic Fuel Using Machine Learning Models

Metallic fuels, particularly U—10Zr, are promising candidates for next-generation sodium-cooled fast reactors. Irradiation of nuclear fuels in reactors can lead to the formation of solid and gas fission product which subsequently forms microstructural pores, deteriorating fuel performance. Due to the massive amount of pores and complex phases formed, a quantitative description of fission gas pores is not yet available, preventing the development of microstructure-informed fuel performance modeling for fuel qualification. This paper applied a pre-trained deep learning model to ~10,260 high magnification scanning electron microscopy images. This method increased the accuracy of fission gas pore segmentation and allows statistical features to be extracted which cannot be achieved manually. A pre-trained decision tree model worked on the segemenation results and further classified the pores into different categories to produce a correlation between the pores, movement of lanthanides, and temperature gradient during irradiation. Finally, this paper emphasizes the potentials of machine learning models to accelerate fuel research, development, and qualification for advanced reactors.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Characterization of Fuel Cladding Chemical Interaction on a High Burnup U-10Zr Metallic Fuel via Electron Energy Loss Spectroscopy Enhanced by Machine Learning

Fuel cladding chemical interaction (FCCI) is one of the main performance limiting factors for metallic nuclear fuels. The interaction destabilizes the martensitic microstructure and deteriorates mechanical properties of HT-9 cladding. The detection of low atomic number elements (Z<10) and overlapping of elemental peaks can be problematic in interpreting energy dispersive X-ray spectroscopy (EDS) data. Electron energy loss spectroscopy (EELS) provides precise elemental edge energy values and can detect elements with a low atomic number. This work utilizes EELS to study the distribution of lanthanides and light elements at the interaction region. The sample was prepared from the FCCI region of a U-10Zr (wt.%) solid fuel with HT-9 cladding, irradiated to a burnup of 13.2 at.%. Processing the EELS data included three major steps: 1) enhance the signal to noise ratio by denoising the spectrum with principal component analysis (PCA) method, removing background and performing deconvolution; 2) identify chemical elements with core energy loss edges; 3) confirm different phases using a popular machine learning method, K-means. This work presents qualitative assessment of lanthanides and light elements like carbon (C) and oxygen (O) enhanced by the application of machine learning algorithms. By comparing with EDS elemental maps, EELS provides higher resolution chemical maps, reveals the distribution of carbon at the interaction region supporting the formation of zirconium carbide, a rind-like microstructure feature that was proposed to mitigate the chemical interaction. Furthermore, the plasmon peak map was also found to indicate an energy shift associated with the formation of phases/compounds. K-means clustering method was used on the processed electron energy loss (EEL) spectrum to automatically reveal different phases. The resulting clustered maps from K-means clustering align well with elemental maps confirming certain phases, especially Fe-Ce and Zr-C, in the FCCI region.

EELS↗
DOE OSTI2024

Atom probe tomography of segregation at grain boundaries and gas bubbles in neutron irradiated U-10 wt% Mo fuel

During neutron irradiation to fission densities > 5.2 × 10 21 fiss/cm 3 , Xe agglomerates forming gas bubbles of varying size within the U-Mo fuel matrix. Herein, segregation of fission products to Xe bubbles and grain boundaries (GB) were studied using atom probe tomography (APT). Segregation behavior was found to vary among GBs, small bubbles (<10 nm), and larger bubbles (>10 nm). Solid fission products were enriched at GBs and larger bubbles, but not at small bubbles. Finally, a denuded zone was identified adjacent to a > 10 nm Xe gas bubble and a GB.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Measurement of the 239 Pu(n,f)/ 235 U(n,f) Cross-Section Ratio with the NIFFTE fission Time Projection Chamber

In this work, the 239 Pu(n,f)/ 235 U(n,f) cross-section ratio has been measured with the fission Time Projection Chamber (fissionTPC) from 100 keV to 100 MeV. The fissionTPC provides three-dimensional reconstruction of fission-fragment ionization profiles, allowing for a precise quantification of measurement uncertainties. The measurement was performed at the Los Alamos Neutron Science Center which provides a pulsed white source of neutrons. The data are recommended to be used as a cross-section ratio shape. A discussion of the status of the absolute normalization and comparisons to ENDF evaluations and previous measurements is included.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Stopping Force Analysis of 235 U Elemental Fission Product Yields for $E_n$ = 0.11–92.4 MeV

We report most evaluated elemental fission product yield distributions are not experimentally measured. Instead, the majority of evaluated distributions are based on analytic expressions of the Zp-model for relevant cumulative yields. Here we report independent elemental fission product yield distributions of a 235 U target for incident neutron energies ranging from 0.11 MeV through 92.4 MeV. Atomic numbers are calculated by an approach that combines a 2E analysis with a stopping force analysis method, developed within this paper. These analyses are applied to more than 6.1 × 10 6 fission fragment ionization tracks captured within the NIFFTE (Neutron Induced Fission Fragment Tracking Experiment) collaboration fission time projection chamber (fissionTPC). A 3-Z resolution was obtained with the fissionTPC spatial and energy resolutions. Tabulated results are presented for the atomic yield and experimentally derived Zp values as a function of pre-neutron-emission fragment masses for the complete range of incident neutron energies. The stopping-force-derived Zp values tend to support the unchanged charge distribution theory within uncertainty.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Remeasurement of the 239 Pu(n,f)/ 235 U(n,f) Cross-Section Ratio with the NIFFTE fission Time Projection Chamber Using Vapor-deposited Targets

The NIFFTE fission Time Projection Chamber (fissionTPC) has been used to measure the 239 Pu(n,f)/ 235 U(n,f) cross-section ratio for neutron-induced fission in the range of 0.1–100 MeV, with high precision. A white neutron source was provided by the Los Alamos Neutron Science Center, where the experiment was conducted as a remeasurement to evaluate a roughly 2% discrepancy of the previous fissionTPC results with ENDF/B-VIII.0. Further, a detailed accounting of measurement uncertainties was performed, based on the fissionTPC's novel ability to provide three-dimensional reconstruction of fission-fragment ionization profiles. Current results obtained using a vapor-deposited, highly uniform 239 Pu target, in comparison to the measurement published in 2021, where a 239 Pu electroplated target was used, are presented and discussed. The remeasurement presented here is in agreement with the previous fissionTPC result within measurement uncertainties.

235U↗