Search NASA⌕ Search

SEARCH · Search NASA

Results for “data ordering”

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

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

At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The Triple Component Interface of Ni–Co–Ce: Growth, Chemical State, and Stability of NiCo Bimetallic Particles on Reducible CeO 2 (111) Thin Films

The growth of NiCo particles at low coverages over reducible CeO 2 (111) thin films producing a triple interface between Ni-Co-Ce was investigated by scanning tunneling microscopy (STM) and X-ray photoelectron spectroscopy (XPS), which was compared to that of monometallic Ni and Co particles. XPS data show that deposition of either Ni or Co on CeO 2 at 300 K causes a partial reduction of Ce 4+ cations to Ce 3+ ions. At 0.3 monolayer (ML), XPS detects Co 2+ on CeO 2 . However, both Ni 0 and Ni 2+ are present as major species at 300 K and annealing causes a significant increase of Ni 2+ in Ni particles. Deposition of 0.3 ML Co over 0.3 ML Ni on CeO 2 at 300 K induces reduction of Ni 2+ to metallic Ni and Ni 0 was found as predominant species. Unlike for Co/CeO 2 , metallic Co was also present over the Co-Ni/CeO 2 surface in addition to Co 2+ . Further, this behavior indicates the formation of NiCo bimetallic particles with the possibility of Co diffusion to the interface of Ni/ceria. With heating, the intermixing of Ni and Co atoms in bimetallic particles on CeO 2 was facilitated. Furthermore, oxidation of both metals and ceria occurred as a result of the diffusion of lattice oxygen from the bulk of ceria to the surface. A slight increase in Ni 2+ was observed after heating Co-Ni/CeO 2 to 500 K or higher. Co became Co 2+ with heating to 800 K. Our STM results confirm the formation of NiCo bimetallic particles on CeO 2 at 300 K and further suggest that the addition of Co can help inhibit the sintering of Ni particles at higher temperatures. Bimetallic particles were also obtained by depositing Ni over existing Co particles on CeO 2 . However, our XPS data demonstrate that the deposition order of Co and Ni plays a role in the chemical state of these two metals in bimetallic particles, likely attributed to the difference in their compositions at the bimetallic particle surface as well as the metal-support interface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Optimizing Earthquake Nowcasting With Machine Learning: The Role of Strain Hardening in the Earthquake Cycle

Abstract Nowcasting is a term originating from economics, finance, and meteorology. It refers to the process of determining the uncertain state of the economy, markets or the weather at the current time by indirect means. In this paper, we describe a simple two‐parameter data analysis that reveals hidden order in otherwise seemingly chaotic earthquake seismicity. One of these parameters relates to a mechanism of seismic quiescence arising from the physics of strain‐hardening of the crust prior to major events. We observe an earthquake cycle associated with major earthquakes in California, similar to what has long been postulated. An estimate of the earthquake hazard revealed by this state variable time series can be optimized by the use of machine learning in the form of the Receiver Operating Characteristic skill score. The ROC skill is used here as a loss function in a supervised learning mode. Our analysis is conducted in the region of 5° × 5° in latitude‐longitude centered on Los Angeles, a region which we used in previous papers to build similar time series using more involved methods (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020EA001097 ; Rundle, Donnellan et al., 2021, https://doi.org/10.1029/2021EA001757 ; Rundle, Stein et al., 2021, https://doi.org/10.1088/1361-6633/abf893 ). Here we show that not only does the state variable time series have forecast skill, the associated spatial probability densities have skill as well. In addition, use of the standard ROC and Precision (PPV) metrics allow probabilities of current earthquake hazard to be defined in a simple, straightforward, and rigorous way.

58 GEOSCIENCES↗
DOE OSTI2021

Photoelectric absorption cross section of silicon near the bandgap from room temperature to sub-Kelvin temperature

The use of cryogenic silicon as a detector medium for dark matter searches is gaining popularity. Many of these searches are highly dependent on the value of the photoelectric absorption cross section of silicon at low temperatures, particularly near the silicon bandgap energy, where the searches are most sensitive to low mass dark matter candidates. While such cross section data have been lacking from the literature, previous dark matter search experiments have attempted to estimate this parameter by extrapolating it from higher temperature data. However, discrepancies in the high temperature data have led to order-of-magnitude differences in the extrapolations. In this paper, we resolve these discrepancies by using a novel technique to make a direct, low temperature measurement of the photoelectric absorption cross section of silicon at energies near the bandgap (1.2 eV–2.8 eV).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Robust analysis of space-, time-, and energy-resolved soft x-ray measurements of magnetically confined fusion plasmas (invited)

A novel compact multi-energy soft x-ray (ME-SXR) diagnostic based on the PILATUS3 100K x-ray detector has been developed in collaboration between the Princeton Plasma Physics Laboratory and the University of Wisconsin-Madison and tested on the Madison Symmetric Torus (MST) reversed-field pinch. This solid-state photon-counting detector consists of a two-dimensional array of ~100 000 pixels for which the lower photon absorption cutoff energy can be independently set, allowing it to be configured for a unique combination of simultaneous spatial, spectral, and temporal resolution of ~1 cm, 100 eV, and 500 Hz, respectively. The diagnostic is highly versatile and can be readily adapted to diverse plasma operating conditions and scientific needs without any required downtime. New results from improved-confinement and quasi-single helicity plasmas in the MST demonstrate how the detector can be applied to study multiple aspects of the evolution of magnetically confined fusion-grade plasmas. These include observing the evolution of thermal emissivity, characterizing the energy of mid-Z excitation lines, extracting the T e profile, and observing the evolution of non-thermal populations. A technique for integrating the ME-SXR diagnostic into an integrated data analysis framework based on Bayesian inference is also presented. As a result, this allows ME-SXR measurements to be combined with data for complementary diagnostics in order to simultaneously infer Te and nZ from all available information.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

EWALD: A macromolecular diffractometer for the second target station

Revealing the positions of all the atoms in large macromolecules is powerful but only possible with neutron macromolecular crystallography (NMC). Neutrons provide a sensitive and gentle probe for the direct detection of protonation states at near-physiological temperatures and clean of artifacts caused by x rays or electrons. Currently, NMC use is restricted by the requirement for large crystal volumes even at state-of-the-art instruments such as the macromolecular neutron diffractometer at the Spallation Neutron Source. EWALD’s design will break the crystal volume barrier and, thus, open the door for new types of experiments, the study of grand challenge systems, and the more routine use of NMC in biology. EWALD is a single crystal diffractometer capable of collecting data from macromolecular crystals on orders of magnitude smaller than what is currently feasible. The construction of EWALD at the Second Target Station will cause a revolution in NMC by enabling key discoveries in the biological, biomedical, and bioenergy sciences.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Calibration of energy density functionals with deformed nuclei

Nuclear density functional theory is the prevalent theoretical framework for accurately describing nuclear properties at the scale of the entire chart of nuclides. Given an energy functional and a many-body scheme (e.g., single- or multireference level), the predictive power of the theory depends strongly on how the parameters of the energy functionals have been calibrated with experimental data. Expanded algorithms and computing power have enabled recent optimization protocols to include data in deformed nuclei in order to optimize the coupling constants of the energy functional. The primary motivation of this work is to test the robustness of such protocols with respect to some of the technical and numerical details of the underlying calculations, especially when the calibration explores a large parameter space. To this end, we quantify the effect of these uncertainties on both the optimization and statistical emulation of composite objective functions. We also emphasize that Bayesian calibration can provide better estimates of the theoretical errors used to define objective functions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Advances in prediction of tokamak experiments with theory-based models

The successful validation of theory-based models of transport, MHD stability, heating and current drive, with tokamak measurements over the last 20 years, has laid the foundation for a new era where these models can be routinely used in a "predict first" approach to design and predict the outcomes of experiments on tokamaks today. The capability to predict the plasma confinement and core profiles with a quantified uncertainty, based on a multi-machine, international, database of experience, will provide confidence that a proposed discharge will remain within the operational limits of the tokamak. Developing this predictive capability for the first generation of burning plasma devices, beginning with ITER, and progressing to tokamak demonstration reactors, is a critical mission of fusion energy research. Major advances have been made implementing this predict first methodology on today's tokamaks. In this work, an overview of several of these recent advances will be presented, providing the integrated modeling foundations of the experimental successes. The first steps to include boundary plasmas, and tokamak control systems, have been made. A commitment to predicting experiments as part of the planning process is needed in order to collect predictive accuracy data and evolve the models and software into a robust whole discharge pulse design simulator.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Sensitivity Analysis of Genome-Scale Metabolic Flux Prediction

TRIMER, Transcription Regulation Integrated with MEtabolic Regulation, is a genome-scale modeling pipeline targeting at metabolic engineering applications. Using TRIMER, regulated metabolic reactions can be effectively predicted by integrative modeling of metabolic reactions with a Transcription Factor (TF)-gene regulatory network (TRN), which is modeled via a Bayesian network (BN). In this paper, we focus on sensitivity analysis of metabolic flux prediction for uncertainty quantification of BN structures for TRN modeling in TRIMER. We propose a computational strategy to construct the uncertainty class of TRN models based on the inferred regulatory order uncertainty given transcriptomic expression data. With that, we analyze the prediction sensitivity of the TRIMER pipeline for the metabolite yields of interest. The obtained sensitivity analyses can guide Optimal Experimental Design (OED) to help acquire new data that can enhance TRN modeling and achieve specific metabolic engineering objectives, including metabolite yield alterations. Here we have performed small- and large-scale simulated experiments, demonstrating the effectiveness of our developed sensitivity analysis strategy for BN structure learning to quantify the edge importance in terms of metabolic flux prediction uncertainty reduction and its potential to effectively guide OED.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Dynamics and activity of an ammonia-oxidizing archaea bloom in South San Francisco Bay

Abstract Transient or recurring blooms of ammonia-oxidizing archaea (AOA) have been reported in several estuarine and coastal environments, including recent observations of AOA blooms in South San Francisco Bay. Here, we measured nitrification rates, quantified AOA abundance, and analyzed both metagenomic and metatranscriptomic data to examine the dynamics and activity of nitrifying microorganisms over the course of an AOA bloom in South San Francisco Bay during the autumn of 2018 and seasonally throughout 2019. Nitrification rates were correlated with AOA abundance in quantitative polymerase chain reaction (PCR) data, and both increased several orders of magnitude between the autumn AOA bloom and spring and summer seasons. From bloom samples, we recovered an extremely abundant, high-quality Candidatus Nitrosomarinus catalina-like AOA metagenome-assembled genome that had high transcript abundance during the bloom and expressed >80% of genes in its genome. We also recovered a putative nitrite-oxidizing bacteria metagenome-assembled genome from within the Nitrospinaceae that was of much lower abundance and had lower transcript abundance than AOA. During the AOA bloom, we observed increased transcript abundance for nitrogen uptake and oxidative stress genes in non-nitrifier metagenome-assembled genomes. This study confirms AOA are not only abundant but also highly active during blooms oxidizing large amounts of ammonia to nitrite—a key intermediate in the microbial nitrogen cycle—and producing reactive compounds that may impact other members of the microbial community.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Radon Kinetics in a Basement Space Measured with Different Devices

Although indoor monitoring of radon and benchmarking of radon measurement devices remain important research topics, few intercomparisons of active radon measurement devices have been performed under realistic conditions, let alone dynamic ones enabling comparison of their transient behavior. Five different radon monitors were therefore placed in a poorly ventilated basement space under three different conditions: 24 h under a steady, elevated radon level, 24 h with fans turned on to produce a radon washout transient, and 9 d with fans turned off for a radon buildup transient. Resulting radon concentrations varied between ~200 and ~2,000 Bq m -3 . Accuracy of the devices were evaluated using root-mean-square error, and ventilation data were fit to first order linear compartmental models. To more accurately model behaviors such as cyclic diurnal variations, the source term corresponding to entry of radon from soil into the basement was considered to be non-constant, as it is likely to vary drastically with both the indoor-outdoor pressure differential and soil concentration variations. Here, the improved radon washout model fit very well with the measurements. Despite a wide variety in list prices, all devices performed similarly during transients and at different radon concentrations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Machine learning approach to pattern recognition in nuclear dynamics from the ab initio symmetry-adapted no-core shell model

A novel machine learning approach is used to provide further insight into atomic nuclei and to detect orderly patterns amid a vast data of large-scale calculations. The method utilizes a neural network that is trained on ab initio results from the symmetry-adapted no-core shell model (SA-NCSM) for light nuclei. We show that the SA-NCSM, which expands ab initio applications up to medium-mass nuclei by using dominant symmetries of nuclear dynamics, can reach heavier nuclei when coupled with the machine learning approach. In particular, we find that a neural network trained on probability amplitudes for s- and p-shell nuclear wave functions not only predicts dominant configurations for heavier nuclei but in addition, when tested for the 20 Ne ground state, accurately reproduces the probability distribution. The non-negligible configurations predicted by the network provide an important input to the SA-NCSM for reducing ultralarge model spaces to manageable sizes that can be, in turn, utilized in SA-NCSM calculations to obtain accurate observables. The neural network is capable of describing nuclear deformation and is used to track the shape evolution along the 20-42 Mg isotopic chain, suggesting a shape coexistence that is more pronounced toward the very neutron-rich isotopes. We provide first descriptions of the structure and deformation of 24 Si and 40 Mg of interest to x-ray burst nucleosynthesis, and even of the extremely heavy nuclei such as 166,168 Er and 236 U, that build on first-principles considerations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Solving the Puzzles of the Decay of the Heaviest Known Proton-Emitting Nucleus 185 Bi

Two long-standing puzzles in the decay of 185 Bi, the heaviest known proton-emitting nucleus are revisited. These are the non-observation of the 9/2 – state, which is the ground state of all heavier odd-A Bi isotopes, and the hindered nature of proton and α decays of its presumed 60-μs 1/2 + ground state. The 185 Bi nucleus has now been studied with the 95 Mo( 93 Nb; 3n) reaction in complementary experiments using the Fragment Mass Analyzer and Argonne Gas-Filled Analyzer at Argonne National Laboratory’s ATLAS facility. The experiments have established the existence of two states in 185 Bi; the short-lived T 1/2 = $2.8$$^{+2.3}_{–1.0}$ μs, proton- and α-decaying ground state, and a 58(2)–μs γ-decaying isomer, the half-life of which was previously attributed to the ground state. The reassignment of the ground-state lifetime results in a proton-decay spectroscopic factor close to unity and represents the only known example of a ground-state proton decay to a daughter nucleus ( 184 Pb) with a major shell closure. Furthermore, the data also demonstrate that the ordering of low- and high-spin states in 185 Bi is reversed relative to the heavier odd-A Bi isotopes, with the intruder-based 1/2 + configuration becoming the ground state, similar to the lightest At nuclides.

150 ≤ A ≤ 189↗
DOE OSTI2023

Topological features in the ferromagnetic Weyl semimetal CeAlSi: Role of domain walls

In the ferromagnetic (FM) Weyl semimetal CeAlSi both space-inversion and time-reversal symmetries are broken. Our quantum oscillation (QO) data indicate that the FM ordering modifies the Fermi-surface topology and also leads to an unusual drop in the QO amplitude. In the FM phase, we find a pressure-induced suppression of the anomalous and the loop Hall effects. This cannot be explained based on the electronic band structure or magnetic structure, both of which are nearly pressure independent. Instead, we show that a simplified model describing the scattering of Weyl fermions off FM domain walls can potentially explain the observed topological features. Our study highlights the importance of domain walls for understanding transport in FM Weyl semimetals.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Robust Optimization for the Day-Ahead Scheduling of Cascaded Hydroelectric Systems

Uncertain electricity prices resulting from the re-structuring of electricity market have brought new opportu-nities and challenges for hydroelectric producers. This paper presents a data-driven robust optimization approach for the day-ahead scheduling of cascaded hydroelectric systems (CHS) with electricity price uncertainty. In this paper, a minimum volume enclosing ellipsoid (MVEE) is adopted to construct an ellipsoidal uncertainty set that fully identifies and exploits the historical characteristics data. Here, a second-order conic optimization formulation of the robust counterpart is derived for efficient computation. A real-world case study is conducted to demonstrate the capability of the proposed optimization approach compared with the traditional robust optimization approach.

13 HYDRO ENERGY↗
DOE OSTI2022

Physics-Informed Sparse Gaussian Process for Probabilistic Stability Analysis of Large-Scale Power System with Dynamic PVs and Loads

This work proposes a physics-informed sparse Gaussian process (SGP) for probabilistic stability assessment of large-scale power systems in the presence of uncertain dynamic PVs and loads. The differential and algebraic equations considering uncertainties from dynamic PVs and loads are reformulated to a nonlinear mapping relationship that allows the application of SGP. Thanks to the nonparametric characteristic of Gaussian process, the proposed framework does not require distributions of uncertain inputs and this distinguishes it from existing approaches. As the original Gaussian process is not scalable to large-scale systems with high dimensional uncertain inputs, this paper develops the SGP with a stochastic variational inference technique. It leads to approximately two orders of complex reduction. A data pre-processing step is also introduced to tackle the coexistence of stable and unstable cases by sample clustering and constructing separate SGPs. The probabilistic transient stability index is analyzed to assess system stability under different uncertain dynamics loads and PVs. Comparisons are performed with the sampling-based, the polynomial chaos expansion-based, and traditional Gaussian process-based methods on the modified IEEE 118-bus and Texas 2000-bus systems under various scenarios, including different levels of uncertainties and the existence of nonlinear correlations among dynamic PVs. The impacts of data quality and quantity issues are also investigated. It is shown that the proposed SGP achieves significantly improved computational efficiency while maintaining high accuracy with a limited number of data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Status of weak scale supersymmetry after LHC Run 2 and ton-scale noble liquid WIMP searches

After completion of LHC Run 2, the ATLAS and CMS experiments had collected of order 139 fb –1 of data at √s = 13 TeV. While discovering a very Standard Model-like Higgs boson of mass m h ≃ 125 GeV, no solid signal for physics beyond the Standard Model has emerged so far at LHC. In addition, no WIMP signals have emerged so far at ton-scale noble liquid WIMP search experiments. For the case of weak scale supersymmetry (SUSY), which is touted as a simple and elegant solution to the gauge hierarchy problem and likely low energy limit of compactified string theory, LHC has found rather generally that gluinos are beyond about 2.2 TeV whilst top squark must lie beyond 1.1 TeV. These limits contradict older simplistic notions of naturalness that emerged in the 1980s–1990s, leading to the rather pessimistic view that SUSY is now excluded except for perhaps some remaining narrow corners of parameter space. Yet, this picture ignores several important developments in SUSY/string theory that emerged in the 21st century: 1. the emergence of the string theory landscape and its solution to the cosmological constant problem, 2. a more nuanced view of naturalness including the notion of “stringy naturalness”, 3. the emergence of anomaly-free discrete R-symmetries and their connection to R-parity, Peccei-Quinn symmetry, the SUSY μ problem and proton decay and 4. the importance of including a solution to the strong CP problem. Rather general considerations from the string theory landscape favor large values of soft terms, subject to the vacuum selection criteria that electroweak symmetry is properly broken (no charge and/or color breaking (CCB) minima) and the resulting magnitude of the weak scale is not too far from our measured value. Then stringy naturalness predicts a Higgs mass m h ~ 125 GeV whilst sparticle masses are typically lifted beyond present LHC bounds. Lastly, in light of these refinements in theory perspective confronted by LHC and dark matter search results, we review the most likely LHC, ILC and dark matter signatures that are expected to arise from weak scale SUSY as we understand it today.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Machine learning-based analysis of COVID-19 pandemic impact on US research networks

Here in this study we explore how fallout from the changing public health policy around COVID-19 has changed how researchers access and process their science experiments. Using a combination of techniques from statistical analysis and machine learning, we conduct a retrospective analysis of historical network data for a period around the stay-at-home orders that took place in March 2020. Our analysis takes data from the entire ESnet infrastructure to explore DOE high-performance computing (HPC) resources at OLCF, ALCF, and NERSC, as well as User sites such as PNNL and JLAB. We look at detecting and quantifying changes in site activity using a combination of t-Distributed Stochastic Neighbor Embedding (t-SNE) and decision tree analysis. Our findings bring insights into the working patterns and impact on data volume movements, particularly during late-night hours and weekends.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

SEFOR Core I-E

This repository contains the VTB model developed for the Southwest Experimental Fast Oxide Reactor (SEFOR) core configuration I-E using the NEAMS tools. The MOOSE Reactor Module was employed to set up the geometry mesh, MC2-3 was applied to generate multigroup cross sections at various temperatures, and Griffin was used to calculate the k-eff values of Core I-E across these temperature conditions. To validate the deterministic results from Griffin, reference solutions were obtained with the Monte Carlo code Shift. The reactivity feedback was then derived and compared against SEFOR experimental data from isothermal tests in order to assess the performance of Griffin. Reproducing these results requires licensed access to several code systems, including Shift, Griffin, and MC2-3. Instructions for obtaining and accessing these codes can be found at the following locations: https://www.ornl.gov/scale/releases for SCALE/Shift, https://mooseframework.inl.gov/ncrc/applications/ncrc_root_griffin.html for Griffin, and https://www.anl.gov/nse/mc23 for MC2-3.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗