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

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At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Indra: a public computationally accessible suite of cosmological N -body simulations

ABSTRACT Indra is a suite of large-volume cosmological N-body simulations with the goal of providing excellent statistics of the large-scale features of the distribution of dark matter. Each of the 384 simulations is computed with the same cosmological parameters and different initial phases, with 10243 dark matter particles in a box of length 1 h−1 Gpc, 64 snapshots of particle data and halo catalogues, and 505 time-steps of the Fourier modes of the density field, amounting to almost a petabyte of data. All of the Indra data are immediately available for analysis via the SciServer science platform, which provides interactive and batch computing modes, personal data storage, and other hosted data sets such as the Millennium simulations and many astronomical surveys. We present the Indra simulations, describe the data products and how to access them, and measure ensemble averages, variances, and covariances of the matter power spectrum, the matter correlation function, and the halo mass function to demonstrate the types of computations that Indra enables. We hope that Indra will be both a resource for large-scale structure research and a demonstration of how to make very large data sets public and computationally accessible.

Falck, Bridget↗
DOE OSTI2022

Development of ultra-pure gadolinium sulfate for the Super-Kamiokande gadolinium project

This paper reports the development and detailed properties of about 13 metric tons of gadolinium sulfate octahydrate, Gd 2 (SO 4 ) 3 • 8H 2 O⁠, which has been dissolved into Super-Kamiokande (SK) in the summer of 2020. We evaluate the impact of radioactive impurities in Gd 2 (SO 4 ) 3 • 8H 2 O on diffuse supernova neutrino background searches and solar neutrino observation and confirm the need to reduce radioactive and fluorescent impurities by about three orders of magnitude from commercially available high-purity Gd 2 (SO 4 ) 3 • 8H 2 O⁠. In order to produce ultra-high-purity Gd 2 (SO 4 ) 3 • 8H 2 O, we have developed a method to remove impurities from gadolinium oxide, Gd 2 O 3 , consisting of acid dissolution, solvent extraction, and pH control processes, followed by a high-purity sulfation process. All of the produced ultra-high-purity Gd 2 (SO 4 ) 3 • 8H 2 O is assayed by inductively coupled plasma mass spectrometry and high-purity germanium detectors to evaluate its quality. Because of the long measurement time of high-purity germanium detectors, we have employed several underground laboratories for making parallel measurements including the Laboratorio Subterráneo de Canfranc in Spain, Boulby in the UK, and Kamioka in Japan. In the first half of production, the measured batch purities were found to be consistent with the specifications. However, in the latter half, the Gd 2 (SO 4 ) 3 • 8H 2 O contained one order of magnitude more 228Ra than the budgeted mean contamination. This was correlated with the corresponding characteristics of the raw material Gd 2 O 3 , in which an intrinsically large contamination was present. Based on their modest impact on SK physics, they were nevertheless introduced into the detector. To reduce 228 Ra for the next stage of gadolinium loading to SK, a new process has been successfully established.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Effects of Electrical Stimulation on hiPSC-CM Responses to Classic Ion Channel Blockers

Abstract Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) hold great potential for personalized cardiac safety prediction, particularly for that of drug-induced proarrhythmia. However, hiPSC-CMs fire spontaneously and the variable beat rates of cardiomyocytes can be a confounding factor that interferes with data interpretation. Controlling beat rates with pacing may reduce batch and assay variations, enable evaluation of rate-dependent drug effects, and facilitate the comparison of results obtained from hiPSC-CMs with those from adult human cardiomyocytes. As electrical stimulation (E-pacing) of hiPSC-CMs has not been validated with high-throughput assays, herein, we compared the responses of hiPSC-CMs exposed with classic cardiac ion channel blockers under spontaneous beating and E-pacing conditions utilizing microelectrode array technology. We found that compared with spontaneously beating hiPSC-CMs, E-pacing: (1) reduced overall assay variabilities, (2) showed limited changes of field potential duration to pacemaker channel block, (3) revealed reverse rate dependence of multiple ion channel blockers on field potential duration, and (4) eliminated the effects of sodium channel block on depolarization spike amplitude and spike slope due to a software error in acquiring depolarization spike at cardiac pacing mode. Microelectrode array optogenetic pacing and current clamp recordings at various stimulation frequencies demonstrated rate-dependent block of sodium channels in hiPSC-CMs as reported in adult cardiomyocytes. In conclusion, pacing enabled more accurate rate- and concentration-dependent drug effect evaluations. Analyzing responses of hiPSC-CMs under both spontaneously beating and rate-controlled conditions may help better assess the effects of test compounds on cardiac electrophysiology and evaluate the value of the hiPSC-CM model.

Wei, Feng↗
DOE OSTI2022

Radon-222 Charcoal Canister Steady State Model Calibrations Performed in a Highly Controlled Environmental Chamber and a Natural Indoor Environment

Charcoal canisters are a common method of 222 Rn screening. The calibrations of different batches of activated charcoal used in different canister designs are typically performed in large volume controlled environmental chambers with known and controlled radon concentration, temperature, and humidity. Radon screening could be facilitated in locations without ready access to environmental chambers if canisters could be accurately calibrated and undergo quality control in less controlled environments. This study compares charcoal canister calibrations from a highly controlled radon chamber with calibrations from a basement storage area experiencing temporally varying radon. In addition, the impacts of exposure time and spectral region of interest selection on calibration accuracy are investigated by comparing calibrations calculated using three different choices. Approximately 30 mo after calibrations were completed, groups of canisters were exposed for different durations of time to investigate measurement accuracy and calibration validity over an extended time. A digital simulation of charcoal canister kinetics was also performed to establish limits on how stable radon must be in a space for equilibrium-based calibration to be performed there. Overall, the accuracy of measurements using calibrations from each space differed by less than 10% after 2 d exposure time, showing that carefully controlled conditions are not necessary for the accurate calibration of charcoal canisters. Furthermore, measurement accuracy differed by less than 2% for different spectral region selections. Accuracy improved slightly with canister exposure duration. Simulations suggest that radon instability is most tolerable near the beginning of canister calibration exposures, but this merits further experimental study.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

Online learning of quadratic manifolds from streaming data for nonlinear dimensionality reduction and nonlinear model reduction

Here, this work introduces an online greedy method for constructing quadratic manifolds from streaming data, designed to enable in situ analysis of numerical simulation data on the Petabyte scale. Unlike traditional batch methods, which require all data to be available upfront and take multiple passes over the data, the proposed online greedy method incrementally updates quadratic manifolds in one pass as data points are received, eliminating the need for expensive disk input/output operations as well as storing and loading data points once they have been processed. A range of numerical examples demonstrate that the online greedy method learns accurate quadratic manifold embeddings while being capable of processing data that far exceed common disk input/output capabilities and volumes as well as main-memory sizes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Transformer-based operator learning framework for self-energy in strongly correlated systems

We introduce Σ-Attention, a transformer-based operator-learning framework for approximating the self-energy operator of strongly correlated electronic systems. By creating a batched dataset that combines results from three complementary approaches, i.e., many-body perturbation theory, strong-coupling expansion, and exact diagonalization, each effective in specific parameter regimes, Σ-Attention is applied to learn an accurate approximation for the self-energy operator that is valid across a wide range of parameter regimes. This hybrid strategy leverages the strengths of existing methods while relying on the transformer's ability to generalize beyond individual limitations. More importantly, the scalability of the transformer architecture allows the learned self-energy to be extended to systems with larger sizes, leading to much improved computational scaling. Using the one-dimensional Hubbard model, we demonstrate that Σ-Attention can accurately predict the Matsubara Green's function of large systems with a wide range of coupling strength. Our framework offers a promising and scalable pathway for studying strongly correlated systems with many possible generalizations.

Zhu, Yuanran↗
DOE OSTI2024

Emulating ab initio computations of infinite nucleonic matter

We construct efficient emulators for the computation of the infinite nuclear matter equation of state. These emulators are based on the subspace-projected coupled-cluster method for which we here develop a new algorithm called small-batch voting to eliminate spurious states that might appear when emulating quantum many-body methods based on a non-Hermitian Hamiltonian. The efficiency and accuracy of these emulators facilitate a rigorous statistical analysis within which we explore nuclear matter predictions for > 10 6 different parametrizations of a chiral interaction model with explicit Δ -isobars at next-to-next-to leading order. Constrained by nucleon-nucleon scattering phase shifts and bound-state observables of light nuclei up to He 4 , we use history matching to identify nonimplausible domains for the low-energy coupling constants of the chiral interaction. Within these domains we perform a Bayesian analysis using sampling and importance resampling with different likelihood calibrations and study correlations between interaction parameters, calibration observables in light nuclei, and nuclear matter saturation properties. Published by the American Physical Society 2024

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

First direct 7 Be electron-capture $\mathrm{Q}$-value measurement toward high-precision searches for neutrino physics beyond the Standard Model

Here, we report the first direct measurement of the nuclear electron-capture (EC) decay Q value of 7 Be → 7 Li via high-precision Penning trap mass spectrometry (PTMS). This was performed using the LEBIT Penning trap located at the National Superconducting Cyclotron Laboratory/Facility for Rare Isotope Beams (NSCL/FRIB) using the newly commissioned Batch-Mode Ion-Source (BMIS) to deliver the unstable 7 Be + samples. With a measured value of Q EC = 861.963(23) keV, this result is three times more precise than any previous determination of this quantity. This improved precision and accuracy of the 7 Be EC decay Q value is critical for ongoing experiments that measure the recoiling nucleus in this system as a signature to search for beyond the Standard Model (BSM) neutrino physics using 7 Be-doped superconducting sensors. This experiment has extended LEBIT capabilities, using the first low-energy beam delivered by BMIS at FRIB for PTMS, as well as measuring the lightest-mass isotopes so far with LEBIT.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Cosmogenic production of 37 Ar in the context of the LUX-ZEPLIN experiment

We estimate the amount of 37 Ar produced in natural xenon via cosmic-ray-induced spallation, an inevitable consequence of the transportation and storage of xenon on the Earth’s surface. We then calculate the resulting 37 Ar concentration in a 10-tonne payload (similar to that of the LUX-ZEPLIN experiment) assuming a representative schedule of xenon purification, storage, and delivery to the underground facility. Using the spallation model by Silberberg and Tsao, the sea-level production rate of 37 Ar in natural xenon is estimated to be 0.024 atoms/kg/day. Assuming the xenon is successively purified to remove radioactive contaminants in 1-tonne batches at a rate of 1 tonne/month, the average 37 Ar activity after 10 tons are purified and transported underground is 0.058 − 0.090 𝜇⁢Bq/kg, depending on the degree of argon removal during above-ground purification. Such cosmogenic 37 Ar will appear as a noticeable background in the early science data, while decaying with a 35-day half-life. This newly noticed production mechanism of 37 Ar should be considered when planning for future liquid-xenon-based experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Graph neural network for neutrino physics event reconstruction

Liquid argon time projection chamber (LArTPC) detector technology offers a wealth of high-resolution information on particle interactions, and leveraging that information to its full potential requires sophisticated automated reconstruction techniques. Here, this article describes NUGRAPH 2, a graph neural network for low-level reconstruction of simulated neutrino interactions in a LArTPC detector. Simulated neutrino interactions in the MicroBooNE detector geometry are described as heterogeneous graphs, with energy depositions on each detector plane forming nodes on planar subgraphs. The network utilizes a multihead attention message-passing mechanism to perform background filtering and semantic labeling on these graph nodes, identifying those associated with the primary physics interaction with 98.0% efficiency and labeling them according to particle type with 94.9% efficiency. The network operates directly on detector observables across multiple two-dimensional representations but utilizes a three-dimensional-context-aware mechanism to encourage consistency between these representations. Model inference takes 0.12 s / event on a CPU and 0.005 s / event batched on a GPU. This architecture is designed to be a general-purpose solution for particle reconstruction in neutrino physics, with the potential for deployment across a broad range of detector technologies, and offers a core convolution engine that can be leveraged for a variety of tasks beyond the two described in this paper.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

SENSEI: Direct-Detection Results on sub-GeV Dark Matter from a New Skipper CCD

In this work, we present the first direct-detection search for sub-GeV dark matter using a new ~2-gram high-resistivity Skipper CCD from a dedicated fabrication batch that was optimized for dark matter searches. Using 24 days of data acquired in the MINOS cavern at the Fermi National Accelerator Laboratory, we measure the lowest rates in silicon detectors of events containing one, two, three, or four electrons, and achieve world-leading sensitivity for a large range of sub-GeV dark matter masses. Data taken with different thicknesses of the detector shield suggest a correlation between the rate of high-energy tracks and the rate of single-electron events previously classified as “dark current.” We detail key characteristics of the new Skipper CCDs, which augur well for the planned construction of the ~100-gram SENSEI experiment at SNOLAB.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Magnetosynthesis effect on magnetic order, phonons, and magnons in single-crystal Sr 2 ⁢ IrO 4

It was shown earlier that applying a magnetic field during the growth of Sr 2 IrO 4 , also known as “field-alteration”, induces significant changes to its structural, magnetic, and transport properties. However, the microscopic nature of these changes is enigmatic. Here, in this study, we employed resonant elastic and inelastic x-ray scattering, as well as Raman scattering, to investigate samples from two batches of Sr 2 IrO 4 grown in magnetic fields of different strengths. Our findings reveal that samples grown in a weaker magnetic field have similar magnetic order to non-altered samples, whereas those grown in a stronger field show a different stacking of weak in-plane ferromagnetic moments. Additionally, we observed significant softening and broadening of select Raman-active phonons in the field altered samples, with a stronger effect in the samples grown in the stronger field. We discuss insights that our results provide into the microscopic nature of field-alteration in Sr 2 IrO 4 .

36 MATERIALS SCIENCE↗
DOE OSTI2023

Revisiting the structures and phase transitions of Ba 2 NaNb 5 O 15

The room-temperature and low-temperature structure(s) of Ba 2 NaNb 5 O 15 (BNN) have been debated since the structure was proposed in the 1960s. This work revisits the structures and phase transitions of BNN, combining high-resolution X-ray and neutron powder diffraction with density functional theory calculations. Temperature-dependent high-resolution X-ray powder diffraction patterns are collected from 4 to 918 K, and sequential batch Rietveld refinement using a symmetry mode approach to describe the structure is used to extract the main structural changes as a function of temperature. The data show that the average structure of BNN is best described by the Ama2 space group, and no other structural phase transitions were observed below the ferroelastic transition. The symmetry mode analysis, combining results from diffraction and density functional theory, shows significant octahedral tilting and corrugations of both the A1 and A2 sites along the c direction. A strong correlation between the spontaneous strain and the octahedral tilting was observed, and a potential connection with emerging microstructure at low temperatures is proposed, all enabled by the symmetry mode approach used in this work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Solution scattering at the Life Science X-ray Scattering (LiX) beamline

This work reports the instrumentation and software implementation at the Life Science X-ray Scattering (LiX) beamline at NSLS-II in support of biomolecular solution scattering. For automated static measurements, samples are stored in PCR tubes and grouped in 18-position sample holders. Unattended operations are enabled using a six-axis robot that exchanges sample holders between a storage box and a sample handler, transporting samples from the PCR tubes to the X-ray beam for scattering measurements. The storage box has a capacity of 20 sample holders. At full capacity, the measurements on all samples last for ∼9 h. For in-line size-exclusion chromatography, the beamline-control software coordinates with a commercial high-performance liquid chromatography (HPLC) system to measure multiple samples in batch mode. The beamline can switch between static and HPLC measurements instantaneously. In all measurements, the scattering data span a wide q -range of typically 0.006–3.2 Å −1 . Functionalities in the Python package py4xs have been developed to support automated data processing, including azimuthal averaging, merging data from multiple detectors, buffer scattering subtraction, data storage in HDF5 format and exporting the final data in a three-column text format that is acceptable by most data analysis tools. These functionalities have been integrated into graphical user interfaces that run in Jupyter notebooks, with hooks for external data analysis software.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Advanced Health Information Technology Analytic Framework and Application to Hazard Detection

Health Information Technology (HIT) aims to improve healthcare outcomes by organizing and analyzing various health-related data. With data accumulating at a staggering rate, the importance of real-time analytics has been increasing dramatically, shifting the focus of informatics from batch processing to streaming analytics. HIT is also facing unprecedented challenges in adapting to this new requirement and leveraging advanced IT technologies. This paper introduces a HIT data and compute platform that supports multi-granularity real-time analytics from heterogeneous data sources. The paper first identifies functional requirements and proposes a framework that satisfies the requirements using state-of-the-art big data technologies including Apache Kafka, Spark Structured Streaming Engine, and Delta Lake. To demonstrate its capability to support data analytics in multiple time granularities analytics, a statistical process control-based hazard detection algorithm has been implemented on top of the framework to detect unexpected hazards from order cancellation data of the Department of US Veterans Affairs (VA) in near real-time.

Kumar, Mohit↗
DOE OSTI2022

Real-time Multi-granular Analytics Framework for HIT Systems

Streaming analytics is the process of ingesting and digesting live data from multiple data sources. In the healthcare domain, as the importance of extracting immediate insights while data are streaming into the system grows, the focus is shifting from batch processing to streaming analytics. With data increasing dramatically at high speeds, many informatics designs have been proposed to adapt healthcare domain into this new environment. In our previous work, we introduced a prototype of health informatics technology (HIT) framework that aims to address challenges in adopting state-of-the-art technologies to enable advanced healthcare analytic tasks in new streaming environments. We recently made major updates to the framework so that anomaly from multiple streaming data sources at different granularity levels can be detected in near real-time. In this paper, we detail the implementation and deployment of the framework in Kubernetes clusters and report its performances when tested on electronic health record (EHR) data of Veterans Affairs.

Park, Byung↗
DOE OSTI2024

An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry

Structure determination workloads in neutron diffractometry are computationally expensive and routinely require several hours to many days to determine the structure of a material from its neutron diffraction patterns. The potential for machine learning models trained on simulated neutron scattering patterns to significantly speed up these tasks have been reported recently. However, the amount of simulated data needed to train these models grows exponentially with the number of structural parameters to be predicted and poses a significant computational challenge. To overcome this challenge, we introduce a novel batch-mode active learning (AL) policy that uses uncertainty sampling to simulate training data drawn from a probability distribution that prefers labelled examples about which the model is least certain. We confirm its efficacy in training the same models with ∼ 75% less training data while improving the accuracy. We then discuss the design of an efficient stream-based training workflow that uses this AL policy and present a performance study on two heterogeneous platforms to demonstrate that, compared with a conventional training workflow, the streaming workflow delivers ∼ 20% shorter training time without any loss of accuracy.

Wang, Tianle [Brookhaven National Laboratory (BNL)↗