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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A consistent dataset for the net income distribution for 190 countries and aggregated to 32 geographical regions from 1958 to 2015

Abstract. Data on income distributions within and across countries are becoming increasingly important for informing analysis of income inequality and understanding the distributional consequences of climate change. While datasets on income distribution collected from household surveys are available for multiple countries, these datasets often do not represent the same concept of inequality (or income concept) and therefore make comparisons across countries, over time and across datasets difficult. Here, we present a consistent dataset of income distributions across 190 countries from 1958 to 2015 measured in terms of net income. We complement the observed values in this dataset with values imputed from a summary measure of the income distribution, specifically the Gini coefficient. For the imputation, we use a recently developed nonparametric principal-component-based approach that shows an excellent fit to data on income distributions compared to other approaches. We also present another version of this dataset aggregated from the country level to 32 geographical regions. Our dataset is developed for the purpose of calibrating models such as integrated human–Earth system models with detailed data on income distributions. This dataset will enable more robust analysis of income distribution at multiple scales. The latest version of our data are available on Zenodo: https://doi.org/10.5281/zenodo.7093997 (Narayan et al., 2022b).

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Spectroscopic Determination of the Physical Conditions in Hot Optically Thin Sources

Astrophysical Plasma Emission Database - APED consists of atomic data, primarily theoretical, needed to calculate X-ray through optical emission spectra of hot thermal plasmas. These data are supplemented by experimental values, such as laboratory plasma wave- lengths, and are validated by experiment only rarely. Thus the comparison of predicted spectra to astrophysical spectra serves as invaluable feedback on the quality of the models. Following up on unfavorable comparisons, we have communicated atomic data needs to atomic theorists and experimental plasma physicists so that improvements can be made to the database. The database is now reasonably complete from approx. 1 to 30 A, and from - 90 to 180 A, with significant gaps remaining in the range 30 to 90 A. Astrophysical Plasma Emission Code - APEC uses the APED data as inputs and computes level populations and line emissivities using a rate matrix solver. Tabulated models for a fine grid of temperatures and densities can be applied to observational data. The current public version of APEC output uses an input ionization balance model, assuming CIE; however, we have a working version to calculate the ionization state of the gas. We will begin testing non-equilibrium ionization (NEI) models over the next year - we are now finalizing the atomic database needed to perform the ionization state calculations. At that time, the code will be made public. We have developed optimized error codes, additional testing protocols and substantial documentation, in preparation for this public release. Secondarily, we have begun to investigate the use of APED and APEC for X-ray photoionized plasma, beginning with opacity modeling (e.g. warm absorbers). Application to bright AGN Chandra grating spectra suggests that the accurate wavelengths in APED are of great importance to fitting the data. We are exploring additional subroutines to APEC to extend its usefulness for X-ray photoionized plasma, e.g. to include collisional or photo-excitation of metastable levels for absorption.

Oliversen, Ronald J.↗
NASA NTRS2021

On the Cycling of 231 Pa and 230 Th in Benthic Nepheloid Layers

The naturally-occurring radionuclides protactinium-231 ( 231 Pa) and thorium-230 ( 230 Th) are produced at approximately uniform rates in the ocean and thought to be removed from the water column through a reversible exchange with settling particles. Recent measurements along the U.S. GEOTRACES North Atlantic transect (GA03) revealed two features which are at odds with current understanding about 231 Pa and 230 Th cycling in the ocean: (i) a sharp decrease in dissolved 231 Pa ( 231 Pa d ) and 230 Th ( 230 Th d ) activities with depth below 2000-4000 m and (ii) very high particulate 231 Pa ( 231 Pa p ) and 230 Th ( 230 Th p ) activities near the bottom, at a number of stations between the New England continental shelf and Bermuda. Concomitant measurements of light attenuation from beam transmissometry showed that both features occur in benthic nepheloid layers (BNLs), which suggests that these features may stem, at least partly, from the presence of resuspended sediment in the deep water column. Here we explore the behaviour of 231 Pa and 230 Th in BNLs by using (i) radionuclide, optical, and hydrographic data from the western segment of GA03 (west of Bermuda) and (ii) a simplified model of particle and radionuclide cycling that includes a lateral particle source. First, the BNLs observed at GA03 stations are characterized from measurements of the beam attenuation coefficient converted to particle concentrations. At all stations, particle concentrations below the clear water minimum were the highest in the bottom mixed layer, whose thickness ranged from 95 to 320 m, and decreased generally with height above the bottom. The thickness of strong BNLs varied from 482 to 1358 m and the vertical integral of particle concentration in excess to that at the clear water minimum varied from 1 x 10 4 to 2 x 10 6 mg m −2 , among different stations. Second, the particle-radionuclide model is fitted to data from stations GT11-04 (New England continental rise) and GT11-08 (Hatteras abyssal plain), where samples for radionuclide analyses were collected in the BNL. The model can reproduce simultaneously the increase of particle concentration with depth, the low 231 Pa d and 230 Th d in the BNLs, and the high 231 Pa p and 230 Th p near the bottom. According to the model, at heights less than about 300 m above the seafloor, the dissolved phase was set primarily by a balance between adsorption and desorption, with vertical turbulent mixing playing a secondary role, whilst the particulate phase behaved largely as a non-reactive constituent supplied laterally and transported vertically by particle settling and turbulent mixing. Sensitivity tests with the model suggest that lateral particle sources near continental slopes and similar reliefs can produce significant biases both in the 230 Th normalization method and in the interpretation of sediment 231 Pa/ 230 Th records. Our findings yield insights into the influence of sediment resuspension and transport on 231 Pa and 230 Th in the deep ocean and highlight the need for considering these processes in paleoceanographic applications.

Nepheloid layer↗
DOE OSTI2025

Constrained or unconstrained? Neural-network-based equation discovery from data

Throughout many fields, practitioners often rely on differential equations to model systems. Yet, for many applications, the theoretical derivation of such equations and/or the accurate resolution of their solutions may be intractable. Instead, recently developed methods, including those based on parameter estimation, operator subset selection, and neural networks, allow for the data-driven discovery of both ordinary and partial differential equations (PDEs), on a spectrum of interpretability. The success of these strategies is often contingent upon the correct identification of representative equations from noisy observations of state variables and, as importantly and intertwined with that, the mathematical strategies utilized to enforce those equations. Specifically, the latter has been commonly addressed via unconstrained optimization strategies. Representing the PDE as a neural network, we propose to discover the PDE (or the associated operator) by solving a constrained optimization problem and using an intermediate state representation similar to a physics-informed neural network (PINN). The objective function of this constrained optimization problem promotes matching the data, while the constraints require that the discovered PDE is satisfied at a number of spatial collocation points. We present a penalty method and a widely used trust-region barrier method to solve this constrained optimization problem, and we compare these methods on numerical examples. Our results on several example problems demonstrate that the latter constrained method outperforms the penalty method, particularly for higher noise levels or fewer collocation points. This work motivates further exploration into using sophisticated constrained optimization methods in scientific machine learning, as opposed to their commonly used, penalty-method or unconstrained counterparts. For both of these methods, we solve these discovered neural network PDEs with classical methods, such as finite difference methods, as opposed to PINNs-type methods relying on automatic differentiation. Here, we briefly highlight how simultaneously fitting the data while discovering the PDE improves the robustness to noise and other small, yet crucial, implementation details.

Data-driven discovery↗
NASA NTRS1986

Unsteady design-point flow phenomena in transonic compressors

High-frequency response probes which had previously been used exclusively in the MIT Blowndown Facility were successfully employed in two conventional steady state axial flow compressor facilities to investigate the unsteady flowfields of highly loaded transonic compressors at design point operation. Laser anemometry measurements taken simultaneously with the high response data were also analyzed. The time averaged high response data of static and total pressure agreed quite well with the conventional steady state instrumentation except for flow angle which showed a large spread in values at all radii regardless of the type of instrumentation used. In addition, the time resolved measurements confirmed earlier test results obtained in the MIT Blowdown Facility for the same compressor. The results of these tests have further revealed that the flowfields of highly loaded transonic compressors are heavily influenced by unsteady flow phenomena. The high response measurements exhibited large variations in the blade to blade flow and in the blade passage flow. The observed unsteadiness in the blade wakes is explained in terms of the rotor blades' shed vorticity in periodic vortex streets. The wakes were modeled as two-dimensional vortex streets with finite size cores. The model fit the data quite well as it was able to reproduce the average wake shape and bi-modal probability density distributions seen in the laser anemometry data. The presence of vortex streets in the blade wakes also explains the large blade to blade fluctuations seen by the high response probes which is simply due to the intermittent sampling of the vortex street as it is swept past a stationary probe.

Gertz, J. B.↗
NASA NTRS2019

The Type II superluminous SN 2008es at late times: near-infrared excess and circumstellar interaction

SN 2008es is one of the rare cases of a Type II superluminous supernova (SLSN), showing no narrow features in its early-time spectra, and therefore, its powering mechanism is under debate between circumstellar interaction (CSI) and magnetar spin-down. Late-time data are required for better constraints. We present optical and near-infrared (NIR) photometry obtained from Gemini, Keck, and Palomar Observatories from 192 to 554 d after explosion. Only broad H α emission is detected in a Gemini spectrum at 288 d. The line profile exhibits red-wing attenuation relative to the early-time spectrum. In addition to the cooling SN photosphere, an NIR excess with blackbody temperature ∼1500 K and radius ∼1016 cm is observed. This evidence supports dust condensation in the cool dense shell being responsible for the spectral evolution and NIR excess. We favour CSI, with ∼2–3 Mꙩ of circumstellar material (CSM) and ∼10–20 Mꙩ of ejecta, as the powering mechanism, which still dominates at our late-time epochs. Both models of uniform density and steady wind fit the data equally well, with an effective CSM radius ∼1015 cm, supporting the efficient conversion of shock energy to radiation by CSI. A low amount (≲0.4 Mꙩ) of 56Ni is possible but cannot be verified yet, since the light curve is dominated by CSI. The magnetar spin-down powering mechanism cannot be ruled out, but is less favoured because it overpredicts the late-time fluxes and may be inconsistent with the presence of dust.

Kornpob Bhirombhakdi↗
DOE OSTI2023

Chelation Modeling of a Plutonium-238 Inhalation Incident Treated with Delayed DTPA

This work describes an analysis, using a previously established chelation model, of the bioassay data collected from a worker who received delayed chelation therapy following a plutonium-238 inhalation. The details of the case have already been described in two publications. The individual was treated with Ca-DTPA via multiple intravenous injections and then nebulizations beginning several months after the intake and continuing for four years. The exact date and circumstances of the intake are unknown. However, interviews with the worker suggested that the intake occurred via inhalation of a soluble plutonium compound. The worker provided daily urine and fecal bioassay samples throughout the chelation treatment protocol, including samples collected before, during, and after the administration of Ca-DTPA. Unlike the previous two publications presenting this case, the current analysis explicitly models the combined biokinetics of the plutonium-DTPA chelate. Further, using the previously established chelation model, it was possible to fit the data through optimizing only the intake (day and magnitude), solubility, and absorbed fraction of nebulized Ca-DTPA. This work supports the hypothesis that the efficacy of the delayed chelation treatment observed in this case results mainly from chelation of cell-internalized plutonium by Ca-DTPA (intracellular chelation). It also demonstrates the validity of the previously established chelation model. As the bioassay data were modified to ensure data anonymization, the calculation of the “true” committed effective dose was not possible. However, the treatment-induced dose inhibition (in percentage) was calculated.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2022

INGENIOUS - Great Basin Regional Dataset Compilation

This is the regional dataset compilation for the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS) project. The primary goal of this project is to accelerate discoveries of new, commercially viable hidden geothermal systems while reducing the exploration and development risks for all geothermal resources. These datasets will be used in INGENIOUS as input features for predicting geothermal favorability throughout the Great Basin study area. Datasets consist of shapefiles, geotiffs, tabular spreadsheets, and metadata that describe: 2-meter temperature probe surveys, quaternary faults and volcanic features, geodetic shear and dilation models, heat flow, magnetotellurics (conductance), magnetics, gravity, paleogeothermal features (such as sinter and tufa deposits), seismicity, spring and well temperatures, spring and well aqueous geochemistry analyses, thermal conductivity, and fault slip and dilation tendency. For additional project information, see the INGENIOUS project site linked in the submission. Terms of use: These datasets are provided "as is", and the contributors assume no responsibility for any errors or omissions. The user assumes the entire risk associated with their use of these data and bears all responsibility in determining whether these data are fit for their intended use. These datasets may be redistributed with attribution (see citation information below). Please refer to the license information on this page for full licensing terms and conditions.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Oxygen Defect Configurations in Single-Phase UO 2.15

Hyperstoichiometric UO 2.15 was characterized by neutron total scattering at high temperature in the single-phase UO 2+x region of the U/O phase diagram. The diffraction data confirmed a single-phase fluorite structure at high temperature. Analysis of the short-range data showed that the same structural model does not fit the pair distribution functions well. Instead, structural models containing specific configurations of oxygen defect clusters best represent the local atomic arrangement. In conclusion, prevalent defect clusters previously proposed were fit to the experimental data, and moderately distorted oxygen cuboctahedra hypothesized by recent molecular dynamics simulations fit the data most accurately.

neutron pair-distribution function analysis↗
NASA NTRS2013

The W-W02 Oxygen Fugacity Buffer at High Pressures and Temperatures: Implications for f02 Buffering and Metal-silicate Partitioning

Oxygen fugacity (fO2) controls multivalent phase equilibria and partitioning of redox-sensitive elements, and it is important to understand this thermodynamic parameter in experimental and natural systems. The coexistence of a metal and its oxide at equilibrium constitutes an oxygen buffer which can be used to control or calculate fO2 in high pressure experiments. Application of 1-bar buffers to high pressure conditions can lead to inaccuracies in fO2 calculations because of unconstrained pressure dependencies. Extending fO2 buffers to pressures and temperatures corresponding to the Earth's deep interior requires precise determinations of the difference in volume (Delta) V) between the buffer phases. Synchrotron x-ray diffraction data were obtained using diamond anvil cells (DAC) and a multi anvil press (MAP) to measure unit cell volumes of W and WO2 at pressures and temperatures up to 70 GPa and 2300 K. These data were fitted to Birch-Murnaghan 3rd-order thermal equations of state using a thermal pressure approach; parameters for W are KT = 306 GPa, KT' = 4.06, and αKT = 0.00417 GPa K-1. Two structural phase transitions were observed for WO2 at 4 and 32 GPa with structures in P21/c, Pnma and C2/c space groups. Equations of state were fitted for these phases over their respective pressure ranges yielding the parameters KT = 190, 213, 300 GPa, KT' = 4.24, 5.17, 4 (fixed), and αKT = 0.00506, 0.00419, 0.00467 GPa K-1 for the P21/c, Pnma and C2/c phases, respectively. The W-WO2 buffer (WWO) was extended to high pressure by inverting the W and WO2 equations of state to obtain phase volumes at discrete pressures (1-bar to 100 GPa, 1 GPa increments) along isotherms (300 to 3000K, 100 K increments). The slope of the absolute fO2 of the WWO buffer is positive with increasing temperature up to approximately 70 GPa and is negative above this pressure. The slope is positive along isotherms from 1000 to 3000K with increasing pressure up to at least 100 GPa. The WWO buffer is at a higher fO2 than the IW buffer at pressures lower than 40 GPa, and the magnitude of this difference decreases at higher pressures. This qualitatively indicates an increasingly lithophile character for W at higher pressures. The WWO buffer was quantitatively applied to W metal-silicate partitioning by using the WWO-IW buffer difference in combination with literature data on W metal-silicate partitioning to model the exchange coefficient (KD) for the Fe-W exchange reaction. This approach captures the pressure dependence of W metal-silicate partitioning using the WWO-IW buffer difference and models the activities of the components in the silicate and metallic phases using an expression of the Gibbs excess energy of mixing. Calculation of KD along a peridotite liquidus predicts a decrease in W siderophility at higher pressures that supports the qualitative behavior predicted by the WWO-IW buffer difference, and agrees with findings of others. Comparing the competing effects of temperature and pressure on W metal-silicate partitioning, our results indicate that pressure exerts a greater effect.

Shofner, G. A.↗
DOE OSTI2023

Machine-Learning Assisted Identification of Battery Life Models

Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.

battery↗
DOE OSTI2022

Learning protein fitness models from evolutionary and assay-labeled data

Machine learning-based models of protein fitness typically learn from either unlabeled, evolutionarily related sequences or variant sequences with experimentally measured labels. For regimes where only limited experimental data are available, recent work has suggested methods for combining both sources of information. Toward that goal, we propose a simple combination approach that is competitive with, and on average outperforms more sophisticated methods. Our approach uses ridge regression on site-specific amino acid features combined with one probability density feature from modeling the evolutionary data. Within this approach, we find that a variational autoencoder-based probability density model showed the best overall performance, although any evolutionary density model can be used. Moreover, our analysis highlights the importance of systematic evaluations and sufficient baselines.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Strain Effects on Rashba Spin–Orbit Coupling of 2D Hole Gases in GeSn/Ge Heterostructures

A demonstration of 2D hole gases in GeSn/Ge heterostructures with a mobility as high as 20 000 cm 2 V –1 s –1 is given. Both the Shubnikov–de Haas oscillations and integer quantum Hall effect are observed, indicating high sample quality. The Rashba spin-orbit coupling (SOC) is investigated via magneto-transport. Further, a transition from weak localization to weak anti-localization is observed, which shows the tunability of the SOC strength by gating. The magneto-transport data are fitted to the Hikami–Larkin–Nagaoka formula. The phase-coherence and spin-relaxation times, as well as spin-splitting energy and Rashba coefficient of the k-cubic term, are extracted. Furthermore, the analysis reveals that the effects of strain and confinement potential at a high fraction of Sn suppress the Rashba SOC caused by the GeSn/Ge heterostructures.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Bemnifosbuvir: An HCV NS5B Inhibitor With Multiple Modes of Action

Bemnifosbuvir (BEM) is a potent, pan-genotypic inhibitor targeting the hepatitis C virus (HCV) NS5B polymerase. Its antiviral activity was evaluated in an ascending dose phase I clinical trial involving 30 patients treated once a day for 7 days. After treatment initiation, plasma HCV RNA declined in a biphasic manner with a mean reduction of 2.3 log 10 IU/mL after 24 hours and 4.4 log10 IU/mL by day 7 for the highest dose. Alanine aminotransferase (ALT) also normalized in most patients. We employed a multiscale mathematical model fitted to the HCV RNA and ALT dynamics to quantify the antiviral activity and evaluate the modes of action of BEM. We found that models in which BEM only acted as a typical HCV RNA polymerase inhibitor and reduced the intracellular production of HCV RNA did not fit the data as well as models in which BEM had multiple modes of action, including suppressing viral assembly and secretion and enhancing intracellular HCV RNA degradation. BEM's effectiveness in inhibiting intracellular HCV RNA production increased with dose (150 mg/day: 88.2%, 300 mg/day: 98.8%, 600 mg/day: 99.5%), while inhibition of viral assembly and release was ~95% effective regardless of dose. We observed a dose-dependent enhancement in the degradation of intracellular HCV RNA, with degradation rates 1.5-fold higher in patients receiving 300 mg/day and 2.7-fold higher in those receiving 600 mg/day than in patients receiving 150 mg/day. No significant differences in antiviral activity were detected between HCV genotypes 1b and 3 or between patients with and without compensated cirrhosis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Compressional wave velocity and effective stress in unsaturated soil: Potential application for monitoring moisture conditions in vadose zone sediments

Compressional seismic (P-wave) velocities were measured experimentally at different water potentials and confining pressure states using a variety of sediments collected from the US Department of Energy Hanford Site in southeastern Washington State. P-wave velocity was measured in variably saturated sediments using ultrasonic piezoelectric transducers and water potential was measured with a heat dissipation sensor. We propose a model to relate changes in P-wave velocity to soil stress conditions using an effective stress formulation that includes capillary stress as well as adsorptive stress. We show that compressional wave velocities can be related to water potential and confining stress using a model with five fitted parameters. A single set of parameters provides an excellent fit to data acquired from all of the sediment samples. Under very dry conditions, corresponding to low water potentials, water flow occurs in adsorptive thin films associated with very low unsaturated hydraulic conductivities. The results presented here suggest the utility of seismic methods for establishing low water potential and flow conditions within variably saturated Hanford formation sediments and providing feedback on the performance of mitigation efforts such as soil desiccation and surface infiltration barriers. Seismic methods can provide minimally invasive, three-dimensional information on relevant flow conditions in vadose zone sediments as well as changes that occur over time.

Linneman, Dorothy C.↗
DOE OSTI2021

High-pressure crystal structure and equation of state of ferromagnesian jeffbenite: implications for stability in the transition zone and uppermost lower mantle

Jeffbenite, ideally Mg 3 Al 2 Si 3 O 12 , has been identified as inclusions in super-deep diamonds originating from depths that exceed 300 km. Although Mg-end member jeffbenite has limited stability at upper-mantle conditions, iron-bearing jeffbenite may have broader P–T stability that extends to the transition zone or uppermost lower mantle, incorporating significant amounts of ferric iron. Using synchrotron-based, single-crystal X-ray diffraction (XRD) and synchrotron Mössbauer spectroscopy (SMS) at pressures up to 29 GPa, we report the crystal structure, compressibility, and likely spin transition of iron in ferromagnesian jeffbenite (Mg 2.32 Al 0.03 Fe 2+ 1.28 Fe 3+ 1.77 Si 2.85 O 12 ). High-pressure structure refinements reveal that Fe 3+ substitution for Si in the T2 site, which shares edges with the M2 octahedron, likely stabilizes jeffbenite at high pressure, because it increases the cation-to-cation distance between these sites. Although ferromagnesian jeffbenite does not undergo a structural phase transition below 30 GPa, SMS hyperfine parameters suggest the onset of an electronic spin transition of iron from high-spin (HS) to low-spin (LS) at around 22 GPa, which may increase its stability at high pressures. Pressure–volume data were fit to a third order Birch–Murnaghan equation of state, resulting in V o = 816.54(9), K To = 181.54(1.39), and K' T0 = 2.76(14). These equation of state parameters are applicable to evaluating the encapsulation pressures of super-deep diamonds. The density and bulk modulus of ferromagnesian jeffbenite are similar to or higher than pyrope–almandine, pyrope–majorite, and skiagite–majorite solid solution garnets, further suggesting that jeffbenite may be an important ferric–iron silicate in the deeper parts of the mantle transition zone and uppermost lower mantle. Furthermore, future studies on the influence of temperature and oxidation state on the stability and equations of state of iron-bearing jeffbennite are still needed to determine what role, if any, jeffbenite plays in transition-zone mineralogy.

58 GEOSCIENCES↗
DOE OSTI2024

Stochastic average model methods

We consider the solution of finite-sum minimization problems, such as those appearing in nonlinear least-squares or general empirical risk minimization problems. We are motivated by problems in which the summand functions are computationally expensive and evaluating all summands on every iteration of an optimization method may be undesirable. Here we present the idea of stochastic average model (SAM) methods, inspired by stochastic average gradient methods. SAM methods sample component functions on each iteration of a trust-region method according to a discrete probability distribution on component functions; the distribution is designed to minimize an upper bound on the variance of the resulting stochastic model. We present promising numerical results concerning an implemented variant extending the derivative-free model-based trust-region solver POUNDERS, which we name SAM-POUNDERS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

In-situ irradiation-induced studies of grain growth kinetics of nanocrystalline UO 2

The thermal conductivity of UO 2 fuel needs to be high enough to dissipate the heat generated from the fission reaction. Since grain size affects thermal conductivity and grain size can evolve with irradiation, it is critical to understand in-reactor UO 2 grain growth. Most studies of grain growth in UO 2 are based on thermally driven processes at elevated temperatures. However, studies have shown that grain growth can occur even at cryogenic temperatures by ballistic processes. Such irradiation-induced grain growth in UO 2 is yet to be studied. Advanced in-situ Kr ion irradiation and transmission electron microscopy were systematically performed on nanocrystalline UO 2 thin films at temperatures ranging from 50 K to 1073 K; grain growth was observed at all temperatures. A combination of manual and machine learning techniques was used to measure and plot grain size evolution against irradiation fluence at various irradiation temperatures. The machine learning method has significantly improved the analysis efficiency and reduced human labors. The grain diameter data were fitted using classical grain growth and thermal spike models to describe grain growth kinetics with and without irradiation effect. Grain growth during low temperature irradiation (≤ 475 K) can be well described by the thermal spike model. Above 475 K, there were additional thermally assisted processes that further accelerate the grain growth. At the highest irradiation temperature about 1075 K, both irradiation-induced dislocation loops and cavities/bubbles were observed to form in the UO 2 . In this report, the effects of irradiation-induced defects on grain growth kinetics are discussed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗