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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Inter-ELM pedestal evolution and the role of edge fluctuations in the C-Mod and DIII-D tokamaks

Edge localized modes (ELMs) lead to a cyclical behaviour of the H-mode pedestal density, temperature and pressure. Substantial evidence exists associating the ELM crash with violation of ideal MHD stability, via current driven kink/peeling modes and/or pressure gradient driven ballooning modes. Recovery from ELM crashes and buildup to the next ELM crash is an active and critical area of edge physics research for ITER projections. In one set of theories, the pedestal pressure profile in H-mode discharges is predicted to be limited by micro-instabilities. One such micro-instability is the kinetic ballooning mode (KBM), which is hypothesized to provide a ‘soft’ limit that regulates the edge transport, thereby restricting the local pressure gradient. In this picture, e.g. in the EPED model, the pedestal width expands slowly until the ideal MHD stability limit is reached. Experiments were recently performed on the C-Mod and DIII-D devices to search for instabilities correlated with the pedestal evolution between ELMs. The results show correlations between the onset of quasi-coherent fluctuations between type I ELMs and a critical temperature gradient. Linear gyrokinetic calculations have shown that these fluctuations have characteristics similar to those expected for KBMs. These results provide additional data toward validation of the EPED model, increasing confidence in ITER projections.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Material transfers detection with seismic observations

We are exploring the use of data from a seismic network around a research nuclear reactor and isotope production facility at Oak Ridge National Laboratory to study activity patterns related to the transfer of nuclear material. A sensor network with eight seismometers was installed around the High Flux Isotope Reactor and the Radiochemical Engineering Development Center and started operating in October 2019. These data are used to detect and characterize operational events around facility. We are using those data to extract signals related to the movement of vehicles involved in the transport of nuclear materials (e.g., transportation of reactor fuel, targets, and product isotopes). Large vehicles produce mechanical energy that can be observed as seismic signals, either as the result of sound emanating from the vehicle engine or by the generation of surface waves as a response of the ground to the load of the vehicles. In particular, the station located near the entrance gate of the facility displays a clear seismic signature as vehicles cross a metal platform on the ground. This signal is characterized by sharp energy bursts that correspond to the number of axles on the vehicle. We developed and are testing an algorithm to identify sequences of energy bursts to count and characterize vehicles of different sizes. We are also exploring the use of seismic polarization analysis to measure the degree of polarization of the observed signals. These two techniques for detection and characterization of vehicles used for nuclear material transfers will be validated with seismic data from a targeted collection using different vehicles and routes similar to the ones used in real-life scenarios at the High Flux Isotope Reactor.

Marcillo, Omar↗
DOE OSTI2023

Nb3Sn Coating of Twin Axis Cavity and Other Complex SRF Cavity Structures

In the field of Accelerator Science, for the low cost and increased quality factor, thin films coated niobium cavities are used in the modern SRF research. Within the potential substances, Nb3Sn has shown higher critical temperature than niobium. Here the tin vapor diffusion method is used as the preferred technique to coat niobium cavities. So far, only elliptical cavities have been coated with Nb3Sn and this technique has not yet been applied to cavities with complex geometries, which are also helpful in the accelerator science field. The Half-wave resonator could provide us data across frequencies of interest for particle accelerators world wide. Similarly the Twin axis cavity proposed for Energy Recovary Linac (ERL) applications could benefit from Nb3Sn coating. But with their advanced geometries, increased area, more number of ports and hard to reach areas, a different coating approach is needed to provide a uniform coating of Nb3Sn. We used twin axis cavity to optimize the coating technique and followed a low temperature annealing to further improvement of the thin film quality by low temperature annealing as a post coating method. With these we could expand the current coating system at JLab to coat complex cavity models

Tiskumara, Jayendrika↗
DOE OSTI2023

A Summary of the Microstructural Characterization Results for the A709 Commercial Heats

To improve high temperature strength for advanced nuclear reactors, it is necessary to qualify additional structural materials for introduction into the American Society of Mechanical Engineers (AMSE) Boiler and Pressure Vessel Code (BPVC). Specifically, Alloy 709 was identified as a material for Sodium-cooled Fast Reactors to enhance time-dependent mechanical properties compared to 316H stainless steel, a code qualified high temperature alloy. Idaho National Laboratory (INL) and Oak Ridge National Laboratory (ORNL) performed initial microstructure and mechanical property testing on a third, commercial heat of Alloy 709 stainless steel. The Alloy 709 plate was produced by Allegheny Technology Inc. (ATI) and in the solution annealed condition. The chemical composition was within the limits for an American Society of Testing and Materials (ASTM) A213 specification for seamless tube for material UNS S31025. Grain size was measured as an average ASTM size of 6.5, which was larger than the ASTM 7 required according to ASTM A213 for seamless tube. The chemical composition was within the limits for an ASTM A213, and the room temperature tensile tests and hardness data met ASTM A213 specifications. Creep-fatigue results showed similar behavior to previous heats of Alloy 709.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Advancing earth system model calibration: a diffusion-based method

Understanding of climate impact on ecosystems globally requires site-specific model calibration. Here we introduce a novel diffusion-based uncertainty quantification (DBUQ) method for efficient model calibration. DBUQ is a score-based diffusion model that leverages Monte Carlo simulation to estimate the score function and evaluates a simple neural network to quickly generate samples for approximating parameter posterior distributions. DBUQ is stable, efficient, and can effectively calibrate the model given diverse observations, thereby enabling rapid and site-specific model calibration on a global scale. This capability significantly advances Earth system modeling and our understanding of climate impacts on Earth systems. We demonstrate DBUQ's capability in E3SM land model calibration at the Missouri Ozark AmeriFlux forest site. Both synthetic and real-data applications indicate that DBUQ produces accurate parameter posterior distributions similar to those generated by Markov Chain Monte Carlo sampling but with 30X less computing time. This efficiency marks a significant stride in model calibration, paving the way for more effective and timely climate impact analyses.

Liu, Yanfang↗
DOE OSTI2022

Geostationary Satellite Retrievals with Irradiances and Cloud Properties / Processed Data

The Geostationary Operational Environmental Satellite (GOES) records sky brightness temperatures and irradiances at several wavelengths. From these the cloud microphysical and optical properties can be routinely retrieved. These properties are derived using the technique proposed by Minnis et al. 2020, and is similar to the technique used for deriving cloud properties from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The derived GOES properties for the duration of the second Wind Forecasting Improvement Project (WFIP2) within the domain are reported here. Nominally the retrieved dataset has a 30- minute temporal and a 9 km spatial resolution.

17 WIND ENERGY↗
DOE OSTI2021

Converting tabular data into images for deep learning with convolutional neural networks

Abstract Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relationship between features, and thus are unsuitable for modeling using CNNs. To meet this challenge, we develop a novel algorithm, image generator for tabular data (IGTD), to transform tabular data into images by assigning features to pixel positions so that similar features are close to each other in the image. The algorithm searches for an optimized assignment by minimizing the difference between the ranking of distances between features and the ranking of distances between their assigned pixels in the image. We apply IGTD to transform gene expression profiles of cancer cell lines (CCLs) and molecular descriptors of drugs into their respective image representations. Compared with existing transformation methods, IGTD generates compact image representations with better preservation of feature neighborhood structure. Evaluated on benchmark drug screening datasets, CNNs trained on IGTD image representations of CCLs and drugs exhibit a better performance of predicting anti-cancer drug response than both CNNs trained on alternative image representations and prediction models trained on the original tabular data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

The Atacama Cosmology Telescope: DR6 power spectra, likelihoods and ΛCDM parameters

We present power spectra of the cosmic microwave background (CMB) anisotropy in temperature and polarization, measured from the Data Release 6 maps made from Atacama Cosmology Telescope (ACT) data. These cover 19,000 deg 2 of sky in bands centered at 98, 150 and 220 GHz, with white noise levels three times lower than Planck in polarization. We find that the ACT angular power spectra estimated over 10,000 deg 2 , and measured to arcminute scales in TT, TE and EE, are well fit by the sum of CMB and foregrounds, where the CMB spectra are described by the ΛCDM model. Combining ACT with larger-scale Planck data, the joint P-ACT dataset provides tight limits on the ingredients, expansion rate, and initial conditions of the universe. We find similar constraining power, and consistent results, from either the Planck power spectra or from ACT combined with WMAP data, as well as from either temperature or polarization in the joint P-ACT dataset. When combined with CMB lensing from ACT and Planck, and baryon acoustic oscillation data from the Dark Energy Spectroscopic Instrument (DESI DR1), we measure a baryon density of Ω b h 2 = 0.0226 ± 0.0001, a cold dark matter density of Ω c h 2 = 0.118 ± 0.001, a Hubble constant of H 0 = 68.22 ± 0.36 km/s/Mpc, a spectral index of n s = 0.974 ± 0.003, and an amplitude of density fluctuations of σ 8 = 0.813 ± 0.005. Including the DESI DR2 data tightens the Hubble constant to H 0 = 68.43 ± 0.27 km/s/Mpc; ΛCDM parameters agree between the P-ACT and DESI DR2 data at the 1.6σ level. We find no evidence for excess lensing in the power spectrum, and no departure from spatial flatness. The contribution from Sunyaev-Zel'dovich (SZ) anisotropy is detected at high significance; we find evidence for a tilt with suppressed small-scale power compared to our baseline SZ template spectrum, consistent with hydrodynamical simulations with feedback.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Scalable and Energy-Efficient Methods for Interactive Exploration of Scientific Data

The main scientific contributions of this project are the following novel concepts for multidimensional arrays: shape-based similarity join (SIGMOD 2016), incremental view maintenance (SIGMOD 2017), user-defined stencil functions (HPDC 2017), and distributed caching for in-situ processing (SSDBM 2018). Building on our collaboration with the astrophysics group at LBNL, we applied these techniques to the data generated in the Palomar Transient Factory (PTF) astronomical survey. They played a pivotal role in the first-ever observation of a neutron star merger, which produces gravitational waves and turns out to be the origin of heavy elements, including gold. This has lead to a Science magazine article that has received extensive media coverage on ACM TechNews, Slashdot, FiveThirtyEight, and Quanta Magazine, among others. Additionally, two other articles detailing related aspects of the same discovery have been published in the Astrophysical Journal Letters journal. These publications have more than 3,000 citations according to Google Scholar (as of February 2022). This cross-disciplinary collaboration provided very good opportunities to apply database techniques to real-life scientific problems. The fact that they facilitated major discoveries in astrophysics proves the importance of our research. In addition to the work on multidimensional array databases, this project has also developed stochastic gradient descent (SGD) optimization algorithms for training large scale machine learning models, methods for querying in-situ data, and a database query optimizer based on sketch synopses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 2 Sensor Data v2-1

This is the version v2-1 Level 2 (L2) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. Level 2 (L2) data consist of sensor observations from the COMPASS-FME synoptic sites, TEMPEST, and DELUGE. Compared to the L1 data, these are more consistent (always 15-minute timestamps for the entire year); better QA/QC’d (out of bounds, out of service, and extreme outlier values are removed); and more complete, with a gap-filled time series available alongside the main observations, and additional derived (calculated) variables. L2 data are intended to be rapidly and easily usable in analyses and simulations. However, algorithmic outlier identification always carries the risk of removing valid data, and Level 1 data may be more suitable for analyses that focus on variability or extreme events. This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific Parquet (a high performance, space efficient format; see https://parquet.apache.org) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are reported every 15 minutes. Please see v2-1 TEMPEST L2 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. Data files are in Apache Parquet, a high performance, space efficient format for tabular data. These files can be read using R's `arrow` package (https://arrow.apache.org/docs/r/), with similar tools available in other languages. The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods. * Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021 * TEMPEST 1: June 22, 2022 * TEMPEST 2: June 6-7, 2023 * TEMPEST 3: June 11-13, 2024

EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATU↗
DOE OSTI2020

Medium amplitude parallel superposition (MAPS) rheology. Part 2: Experimental protocols and data analysis

An experimental protocol is developed to directly measure the new material functions revealed by medium amplitude parallel superposition (MAPS) rheology. This protocol measures the medium amplitude response of a material to a simple shear deformation composed of three sine waves at different frequencies, revealing a rich dataset consisting of up to 19 measurements of the third-order complex modulus at distinct three-frequency coordinates. We discuss how the choice of input frequencies influences the features of the MAPS domain studied by the experiment. A polynomial interpolation method for reducing the bias of measured values from spectral leakage and reducing variance due to noise is discussed, including a derivation of the optimal range of amplitudes for the input signal. This leads to the conclusion that conducting the experiment in a stress-controlled fashion possesses a distinct advantage to the strain-controlled mode. The experimental protocol is demonstrated through measurements of the MAPS response of a model complex fluid: a surfactant solution of wormlike micelles. The resulting dataset is indeed large and feature-rich, while still acquired in a time comparable to similar medium amplitude oscillatory shear (MAOS) experiments. We demonstrate that the data represent measurements of an intrinsic material function by studying its internal consistency, compatibility with low-frequency predictions for Coleman–Noll simple fluids, and agreement with data obtained via MAOS amplitude sweeps. Finally, the data are compared to predictions from the corotational Maxwell model to demonstrate the power of MAPS rheology in determining whether a constitutive model is consistent with a material’s time-dependent response.

Lennon, Kyle R. (ORCID:0000000212515461)↗
DOE OSTI2021

Statistical tools for a better optical model

Background: Modern statistical tools provide the ability to compare the information content of observables and provide a path to explore which experiments would be most useful to give insight into and constrain theoretical models. Purpose: Here we study three such tools in the context of nuclear reactions with the goal of constraining the optical potential. Method: The three statistical tools examined are (i) the principal component analysis, (ii) the sensitivity analysis based on derivatives, and (iii) the Bayesian evidence. We first apply these tools to a toy-model case, comparing the form of the imaginary part of the optical potential. Then we consider two different reaction observables, elastic angular distributions and polarization data for reactions on 48 Ca and 208 Pb at two different beam energies. Results: For the toy-model case, we find significant discrimination power in the sensitivities and the Bayesian evidence, showing clearly that the volume imaginary term is more useful to describe scattering at higher energies. When comparing between elastic cross sections and polarization data using realistic optical models, sensitivity studies indicate that both observables are roughly equally sensitive but the variability of the optical model parameters is strongly angle dependent. The Bayesian evidence shows some variability between the two observables, but the Bayes factor obtained is not sufficient to discriminate between angular distributions and polarization. Conclusions: From the cases considered, we conclude that, in general, elastic scattering angular distributions have similar impact in constraining the optical potential parameters compared with the polarization data. The angular ranges for the optimum experimental constraints can vary significantly with the observable considered.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Quantifying the Solar Energy Resource for Puerto Rico

After Hurricane Maria, multiple U.S. Department of Energy laboratories studied the state of the electric grid in Puerto Rico and analyzed grid resilience and grid integration of renewable energy. As part of the work done at the National Renewable Energy Laboratory, researchers created new solar resource data, conducted a technical potential and supply curve analysis, and studied the interannual variability of the solar resource. A new methodology was developed to downscale solar resource data from the National Solar Radiation Data Base (NSRDB) from a 4-km x 4-km spatial and 30-minute temporal resolution to a 2 km x 2 km and 5-minute resolution. This methodology primarily used simple physical principles to develop high-resolution cloud properties which were then used to compute solar radiation. The high-resolution datasets were validated against ground measurements and the error metrics were found to be similar to the original lower resolution dataset. Using 20 years of downscaled data from the NSRDB multi-year capacity factors for photovoltaics (PV) were developed for both single-axis tracking and fixed latitude-tilt configurations. Use of the multi-year data provides the ability to understand variability in capacity factors due to variability in weather over a long period of time. For Puerto Rico the coastal regions were found to have significant higher capacity factors than inland. Using land-use and terrain information a technical potential analysis was conducted for Puerto Rico. This analysis restricted single PV plant development to a maximum of 100 MW nameplate capacity. The nameplate capacity for each municipality were then determined. Based on our assumptions, 56 of the 78 total municipalities of Puerto Rico contain some level of solar capacity. Most of the interior municipalities did not have any capacity because of the geographic exclusions used in this study. The lowest capacity for a PV plant observed in a municipality was 10 MW. The maximum capacity within a county was 2,000 MW. Further a supply curve analysis was conducted by taking the results of the technical potential and quantifying system and transmission costs. The levelized cost of energy (LCOE) was calculated for each theoretical PV plant site, and the levelized cost of transmission was added to the LCOE to produce a total cost estimate for each site. The results of the supply curve analysis allow for a relative comparison of the cost for integrating new PV capacity into the grid. This analysis indicates that cheaper total LCOE sites tend to be larger in capacity. The total capacity in this study was found to be far beyond the maximum peak load for the island. However, this study does not consider the economic and market potential for development. The cumulative capacity presented in this study assumes that the best locations are developed first and ignores the complex decision paths for new power plant development. Therefore, this analysis can only be treated as illustrative. Finally this study investigates the impact of inter-annual variability of resource using a variety of metrices including probability of exceedance and variation in capacity factor and LCOE. This study demonstrates that the capacity factor or LCOE could vary by over 10% year to year. This clearly indicates the risks involved in using any particular year of data and clearly points to the use of multi-year data to reduce some of the risks related to variability in weather.

14 SOLAR ENERGY↗
DOE OSTI2022

Overexpression of phospholipid: diacylglycerol acyltransferase in Brassica napus results in changes in lipid metabolism and oil accumulation

The regulation of lipid metabolism in oil seeds is still not fully understood and increasing our knowledge in this regard is of great economic, as well as intellectual, importance. Oilseed rape (Brassica napus) is a major global oil crop where increases in triacylglycerol (TAG) accumulation have been achieved by overexpression of relevant biosynthetic enzymes. In this study, we expressed Arabidopsis phospholipid: diacylglycerol acyltransferase (PDAT1), one of the two major TAG-forming plant enzymes in B. napus DH12075 to evaluate its effect on lipid metabolism in developing seeds and to estimate its flux control coefficient. Despite several-fold increase in PDAT activity, seeds of three independently generated PDAT transgenic events showed a small but consistent decrease in seed oil content and had altered fatty acid composition of phosphoglycerides and TAG, towards less unsaturation. Mass spectrometry imaging of seed sections confirmed the shift in lipid compositions and indicated that PDAT overexpression altered the distinct heterogeneous distributions of phosphatidylcholine (PC) molecular species. Similar, but less pronounced, changes in TAG molecular species distributions were observed. Our data indicate that PDAT exerts a small, negative, flux control on TAG biosynthesis and could have under-appreciated effects in fine-tuning of B. napus seed lipid composition in a tissue-specific manner. This has important implications for efforts to increase oil accumulation in similar crops.

Biochemistry & Molecular Biology↗
DOE OSTI2023

Data-Driven Validation of NOvA's Convolutional Neural Network for Electron (Anti)Neutrino Selection

NOvA is a long-baseline neutrino oscillation experiment, designed to make measurements of several oscillation parameters using muon neutrino disappearance and electron neutrino appearance. It consists of two functionally equivalent detectors and utilizes the Fermilab NuMI neutrino beam. NOvA uses a convolutional neural network for particle identification of electron neutrino events with a validation process that includes several data-driven techniques. These Muon Removed studies ensure that our classifier performs the same on data as it does on simulation. In particular, Muon Removed Electron-Added studies involve selecting muon neutrino charged current events from both data and simulation and replacing the muon with a simulated electron of similar energy. For Muon Removed Bremsstrahlung and Muon Removed Decay-in-Flight studies, we remove muonic hits from cosmic muons that have either experienced Bremsstrahlung radiation or decayed in flight, producing samples of pure electromagnetic showers. Each of these electron neutrino-like samples are then evaluated by our classifier to obtain selection efficiencies. Our most recent analysis showed good agreement in the electron selection efficiency between data and simulation using these techniques. Furthermore, these cross-checks can be extended to corrections to our predicted electron neutrino signal. The impact of one such set of corrections on the overall analysis results were also evaluated in thesis.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Nickel-rich, volatile depleted iron meteorites: Relationships and formation processes

Ungrouped iron meteorites Tishomingo, Willow Grove, and Chinga, and group IVB iron meteorites, are Ni-rich. Similarities include enrichments of 10–100 × CI for some refractory siderophile elements, and equivalent depletions in more volatile siderophile elements. Superimposed on the overall enrichment/depletion trend, certain siderophile elements (P, W, Fe, Mo) are depleted relative to elements of similar volatility. All three ungrouped irons derive from parent bodies formed in the early Solar System. Willow Grove and Chinga are characterized by cosmic ray exposure corrected 182 W/ 184 W consistent with metal-silicate segregation on their parent bodies within 1–3 Myr of Solar System formation, within the age range determined for segregation of magmatic iron meteorite parent bodies, including group IVB irons. Tishomingo is characterized by a younger model age 4–5 Myr subsequent to Solar System formation, reflecting either late stage melting resulting from 26 Al decay, or an impact resetting. The discovery of stishovite in Tishomingo, indicating exposure to a minimum shock pressure of 8–9 GPa, is consistent with the latter. Stishovite in Tishomingo and chromite included in troilite-daubréelite in IVB irons allows oxygen isotopic composition comparison between these meteorites. Different mass independent oxygen isotopic compositions of IVB irons and Tishomingo indicate genetically distinct parent bodies. By contrast, mass independent Mo isotopic compositions overlap within analytical uncertainties, indicating a similar, carbonaceous chondrite (CC) type genetic heritage. Molybdenum and 183 W isotopic data for Chinga and Willow Grove indicate derivation from CC type parent bodies. Willow Grove shares Mo and 183 W isotopic compositions with the Ni-rich South Byron Trio (SBT) grouplet and the Milton pallasite. These Ni-rich meteorites likely formed in the same general nebular environment as other CC planetesimals, likely the outer Solar System. Highly siderophile element (HSE) abundances of Willow Grove and Tishomingo are similar to some IVB meteorites, consistent with formation by moderate degrees of fractional crystallization from initial metallic melts with low S and P, and modestly fractionated HSE. The comparable HSE abundances of Tishomingo and Willow Grove to some IVB irons, yet substantially higher Ni concentrations, indicate formation on parent bodies with lower bulk HSE abundances or HSE concentration in proportionally smaller volumes of metal. HSE abundances in Chinga are considerably lower than in IVB irons, highly fractionated, and processes responsible for these remain elusive. For IVB irons and these ungrouped irons, high temperature condensation likely dominated the enrichment and depletion of the refractory and volatile siderophile elements, respectively. Parent body degassing may have also played a role. Relative depletion of volatile siderophile elements is not, however, a universal feature of high-Ni meteorites. The SBT and Milton pallasite are Ni-rich, but less depleted in the more volatile siderophile elements. Nickel enrichment was likely driven by oxidation of Fe metal during parent body accretion or core segregation. Oxidation of the Tishomingo and Willow Grove parent bodies may have occurred at ~ IW + 1, indicated by relative Mo and W depletions due to metal/water reaction during differentiation. Late-stage reduction, indicated by the presence of Cr-bearing sulfides in Tishomingo and IVB irons, may have resulted from exhaustion of the oxidant.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Mass Detection for Heavy-Duty Vehicles using Gaussian Belief Propagation

Predicting vehicle mass is critical to accurately estimate energy use and emissions of commercial trucks. However, data from vehicle telematics is often not at sufficient temporal resolution or accuracy for use in model-based detection methods. In this work, a new statistical mass prediction technique is described for heavy-duty vehicles that incorporates the use Gaussian Belief Propagation (GBP) for probabilistic inference. Similar to Bayesian inference models, the GBP model typically requires less labeled training data than other contemporary machine learning techniques. First, a factor graph is constructed, and a set of Gaussian belief nodes with associated means and variances are fitted to the training data. To better handle noisy input data, the GBP mass prediction model utilizes a k-nearest factors (kNF) algorithm for probabilistic inference on unseen testing data. The proposed method is compared with a classical weighted k-nearest neighbors (kNN) regressor. This statistical kNF-GBP model works even with low-quantity, low-quality initial training data, while being capable of realtime mass estimation. Unlike the kNN regressor, the GBP model produces a measure of uncertainty with its predictions. The proposed method is validated using curve-sampled driving data collected from multiple cloud-connected Class 8 regional haul diesel trucks. Both the kNN regressor and the kNF-GBP mass prediction model were able to predict payload mass with coefficients of determination above 0.97 with minimal data preprocessing.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Frameworks, Algorithms, and Scalable Technologies for Mathematics (FASTMath) SciDAC Institute

As computational models scale to larger computers, the rate at which they produce data has far outstripped the same computers ability to write that data and further the file systems ability to store that data. Almost all of the SciDAC applications, but especially those related to fusion solve very large scale PDEs whose scientific output his impacted by this problem. To gain access to dynamics in an exascale simulation that are not identifiable a priori and to make that dynamical data available to machine learning requires fundamental research in the area of in situ data data analytics. Here data analytics includes compression, visualization, uncertainty quantification, and machine learning. This in situ data analytics will enable on-the-fly spatial and temporal compression of solution dynamics, expose that space-time compressed field to machine learning algorithms that have been specialized to work with dynamically evolving data (existing machine learning algorithms treat data sets as static), greatly improving the opportunity for machine learning to provide feedback to the compression, all within an ongoing simulation, without the need to write data to files. The same concepts are also being applied to uncertainty quantification and multi-fidelity modeling which have similar needs for spatial and temporal compression of the ongoing exascale simulation to perform either without the typical, unacceptable writing of data to files.

97 MATHEMATICS AND COMPUTING↗