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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Utah FORGE: Fiber Optic Cumulative Strain Change and Strain Change Rate Data From Well 16A Stimulation at Well 16B

This dataset includes Rayleigh Frequency Shift (RFS) Distributed Strain Sensing (DSS) cumulative strain change and change rate data. The data was acquired during the stimulation of Utah FORGE Well 16A(78)-32 in April 2024 via fiber installed in Well 16B(78)-32. The fiber optic data was acquired using Neubrex SR7000 RFS DSS Distributed Strain sensing instruments and is saved here in the format of HDF5 files (.h5 extension). The spatial sampling on the full wellbore profiles is 0.20 centimeters. The data is the far field strain change response from a baseline profile made down the 16B well on April 3, 2024, so each strain value represents the strain change or strain change rate at each depth relative to the baseline reference profile. The data arrays for each type share the same dimensions (number of channels and time stamps).

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Accelerating Markov Chain Monte Carlo sampling with diffusion models

Global fits of physics models require efficient methods for exploring high-dimensional and/or multimodal posterior functions. We introduce a novel method for accelerating Markov Chain Monte Carlo (MCMC) sampling by pairing a Metropolis-Hastings algorithm with a diffusion model that can draw global samples with the aim of approximating the posterior. We briefly review diffusion models in the context of image synthesis before providing a streamlined diffusion model tailored towards low-dimensional data arrays. We then present our adapted Metropolis-Hastings algorithm which combines local proposals with global proposals taken from a diffusion model that is regularly trained on the samples produced during the MCMC run. Our approach leads to a significant reduction in the number of likelihood evaluations required to obtain an accurate representation of the Bayesian posterior across several analytic functions, as well as for a physical example based on a global fit of parton distribution functions. Our method is extensible to other MCMC techniques, and we briefly compare our method to similar approaches based on normalising flows. A code implementation can be found at https://github.com/NickHunt-Smith/MCMC-diffusion.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

A parallel and performance portable implementation of a full-field crystal plasticity model

We have developed a parallel implementation of an Elasto-Viscoplastic Fast Fourier Transform-based (EVPFFT) micromechanical solver to enable computationally efficient crystal plasticity modeling for polycrystalline materials. Our primary focus lies in achieving performance portability, allowing a single EVPFFT implementation to run optimally on various homogeneous architectures, including multi-core Central Processing Units (CPUs), as well as on heterogeneous computer architectures comprising multi-core CPUs and Graphics Processing Units (GPUs) from different vendors. To accomplish this goal, we have leveraged MATAR, a C++ software library that simplifies the creation and utilization of multidimensional dense or sparse matrix and array data structures. These data structures are designed to be portable across diverse architectures through the use of Kokkos, a performance-portable library. Additionally, we have employed the Message Passing Interface (MPI) to efficiently distribute the computational workload among processors. The heFFTe (Highly Efficient FFT for Exascale) library is used to facilitate the performance portability of the fast Fourier transforms (FFTs) computation. The computational performance of EVPFFT is evaluated and presented in terms of parallel scalability and simulation runtime on different high-performance computing (HPC) architectures. As a result, the utility of the developed framework to efficiently simulate the micro-mechanical fields in polycrystalline microstructures in engineering applications is discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Investigation of methane and ethane pyrolysis with highly active and durable iron-alumina catalyst to produce hydrogen and valuable nano carbons: Continuous fluidized bed tests and reaction rate analysis

The goal of this research work was to evaluate a novel iron-alumina catalyst for methane and ethane pyrolysis with high conversion rates to H 2 with a long life time to address two major issues reported with previous catalysts. Fluidized bed tests demonstrated continuous hydrogen production while collecting carbon containing fines. During the 230 h fluid bed test the methane conversion rate to hydrogen at 700 °C remained above 80% for 160 h while creating valuable nano carbons. Similarly high catalyst lifetimes with high methane conversions have not been reported before. Ethane was more active than methane for pyrolysis with 100% conversion to H 2 . Transmission electron microscopy and Raman analysis of the carbon showed single and multiwall nanotubes and conglomerate nano-fibers. Ethane did not impact the allotrope of carbon formed. TGA was used to create a data array that was used for kinetic analysis and reaction model parametric regressions. Ethane had a higher rate of pyrolysis that was 2.3–2.8 times that of methane. Ethane pyrolysis was found to be more sensitive to temperature with a higher activation energy of 62 kJ/mol as compared to methane at 43 kJ/mol. The frequency factor, A i , was significantly higher for ethane at 0.73 [[kmol/m 3 -sec]/[N/m2] n ] compared to 0.03 for methane on the same basis. Both gases had a functional partial pressure order dependence of ~0.6 based on the regression analysis.

08 HYDROGEN↗
DOE OSTI2022

Database-wide hazard modelling of the onset of DIII-D tearing modes with field features

The rate of onset (hazard) of tearing modes is modelled probabilistically using statistical learning algorithms. Axisymmetric energy-density equilibrium fields are taken as raw high-dimensional input features which are reduced with principal component analysis. Signal processing of non-axisymmetric magnetics fluctuation array data provides the target information from which to learn. Model selection, visualization and calibration assessment procedures are detailed. Here, the analysis is deployed at large scale across the DIII-D tokamak database. Standard model selection criteria suggest that the energy-density post-processed feature is a better choice for modelling the onset rate compared to the non-processed equilibrium reconstruction solution. Two example applications of the learned rate function are demonstrated: (i) proximity-to-onset discharge monitoring and (ii) database analysis showing an (expected) observational global trend that the general hazard increases as a plasma performance metric increases. An important connection between the hazard function and its use as a conditional probability generator is reviewed in the Appendix.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Microseismicity Modulation Due To Changes in Geothermal Production at San Emidio, Nevada, USA

Brief cessations of geothermal production can induce seismicity, a phenomenon that has drawn increasing attention in recent years. Such observations are rare, and the underlying mechanism requires careful analysis. In April 2022, a dense seismic and hydrologic monitoring system was deployed at the San Emidio geothermal field, Nevada, to accompany a planned power plant shutdown. Using the dense seismic array data, we detected and located ∼1,800 microseismic events (MSEs) and developed a high-resolution tomographic P-wave velocity model. We observed substantially increased microseismicity during shutdown. Most MSEs occurred on pre-existing normal faults, which are contained within extremely low-velocity zones that are likely damaged, fluid-filled, and hydraulically connected to nearby production wells. Hydrologic data show rapid fluid pressure increases of <60 kPa following the shutdown. We suggest that the cessation of production rapidly increased fluid pressures along pre-existing fault zones, activating critically stressed fault patches and fractures and producing microseismicity.

Guo, Hao [University of Wisconsin-Madison, WI (Uni↗
DOE OSTI2021

Macroecological distributions of gene variants highlight the functional organization of soil microbial systems

Abstract The recent application of macroecological tools and concepts has made it possible to identify consistent patterns in the distribution of microbial biodiversity, which greatly improved our understanding of the microbial world at large scales. However, the distribution of microbial functions remains largely uncharted from the macroecological point of view. Here, we used macroecological models to examine how the genes encoding the functional capabilities of microorganisms are distributed within and across soil systems. Models built using functional gene array data from 818 soil microbial communities showed that the occupancy-frequency distributions of genes were bimodal in every studied site, and that their rank-abundance distributions were best described by a lognormal model. In addition, the relationships between gene occupancy and abundance were positive in all sites. This allowed us to identify genes with high abundance and ubiquitous distribution (core) and genes with low abundance and limited spatial distribution (satellites), and to show that they encode different sets of microbial traits. Common genes encode microbial traits related to the main biogeochemical cycles (C, N, P and S) while rare genes encode traits related to adaptation to environmental stresses, such as nutrient limitation, resistance to heavy metals and degradation of xenobiotics. Overall, this study characterized for the first time the distribution of microbial functional genes within soil systems, and highlight the interest of macroecological models for understanding the functional organization of microbial systems across spatial scales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Neural network-based classification and regression of magnetohydrodynamic modes in tokamaks

We present a machine learning-based magnetohydrodynamic (MHD) classifier and regressor that utilizes real or complex-valued 3D magnetic sensor array data to determine neoclassical tearing mode (NTM) onset times in tokamaks with millisecond accuracy. The input dataset consists of poloidal profiles of complex Fourier amplitudes with an n = 1 toroidal mode number from 144 human-labeled ITER Baseline Scenario discharges in the DIII-D tokamak, spanning both tearing-dominated and sawtooth-dominated regimes. Since m, n = 2,1 NTMs frequently emerge alongside sawteeth at the same frequency in this scenario, the focus is on isolating the m = 1 and m = 2 components of the n = 1 MHD mode near the tearing onset. To improve model regularization and prediction stability, singular value decomposition was applied to balance the sawtooth and tearing datasets. The enriched datasets facilitated training neural networks that learn the key distinguishing features of sawtooth and tearing modes in the poloidal profiles of their magnetic amplitude and phase. When the modes occur independently, the networks achieve perfect classification due to the modes’ distinct characteristics and low measurement noise. In the more experimentally relevant case where both modes coexist, the networks maintain exceptional performance across key metrics. Tests on synthetic data with known ground truth demonstrate the superior accuracy of the neural network trained on complex-valued input compared to models using real amplitude, phase, or pseudo-complex data, achieving both a mean time delay and standard deviation below 1 ms. Notably, standard linear regression methods fitting the dominant singular modes to the data closely match the neural network’s performance. Applying these methods across a broad range of H-mode scenarios will enable future studies to systematically identify dominant NTM triggers as scenario-specific variables, paving the way for more effective tearing mode avoidance strategies in future fusion reactor designs.

machine learning↗
DOE OSTI2023

A near magnetic-to-kinetic energy equipartition flare from the relativistic jet in AO 0235 + 164 during 2013–2019

ABSTRACT We present the multiwavelength flaring activity of the blazar AO 0235 + 164 during its recent active period from 2013 to 2019. From a discrete correlation function analysis, we find a significant ($\gt 95~{{\ \rm per\ cent}}$) correlation between radio and gamma-ray light curves with flares at longer wavelengths following flares at shorter wavelengths. We identify a new jet component in 43 GHz Very Long Baseline Array data that was ejected from the radio core on MJD $57246^{+26}_{-30}$ (2015 August 12), during the peak of the 2015 radio flare. From the analysis of the jet component, we derived a Doppler factor of δvar = 28.5 ± 8.4, a bulk Lorentz factor of $\Gamma =16.8^{+3.6}_{-3.1}$, and an intrinsic viewing angle of $\theta _{\rm v}=1.42^{+1.07}_{-0.52}\textrm {~degrees}$. Investigation of the quasi-simultaneous radio data revealed a partially absorbed spectrum with the turnover frequency varying in the range of 10−70 GHz and the peak flux density varying in the range of 0.7−4 Jy. We find the synchrotron self-absorption magnetic field strength to be $B_{\rm SSA}=15.3^{+12.6}_{-14.0}\textrm {~mG}$ at the peak of the 2015 radio flare, which is comparable to the equipartition magnetic field strength of $B_{\rm EQ}=43.6^{+10.6}_{-10.4}\textrm {~mG}$ calculated for the same epoch. Additional analysis of the radio emission region in the relativistic jet of AO 0235 + 164 suggests that it did not significantly deviate from equipartition during its recent flaring activity.

Astronomy & Astrophysics↗
DOE OSTI2021

Searching for gravitational waves with strongly lensed repeating fast radio bursts

We report that since their serendipitous discovery, fast radio bursts (FRBs) have garnered a great deal of attention from both observers and theorists. A new class of radio telescopes with wide fields of view have enabled a rapid accumulation of FRB observations, confirming that FRBs originate from cosmological distances. The high occurrence rate of FRBs and the development of new instruments to observe them create opportunities for FRBs to be utilized for a host of astrophysical and cosmological studies. We focus on the rare, and as yet undetected, subset of FRBs that undergo repeated bursts and are strongly gravitationally lensed by intervening structure. An extremely precise timing of burst arrival times is possible for strongly lensed repeating FRBs, and we show how this timing precision enables the search for long-wavelength gravitational waves, including those sourced by supermassive black hole binary systems. The timing of burst arrival for strongly lensed repeating FRBs is sensitive to gravitational-wave sources near the FRB host galaxy, which may lie at cosmological distances and would therefore be extremely challenging to detect by other means. Timing of strongly lensed FRBs can also be combined with pulsar timing array data to search for correlated time delays characteristic of gravitational waves passing through the Earth.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Toward improved urban earthquake monitoring through deep-learning-based noise suppression

Earthquake monitoring in urban settings is essential but challenging, due to the strong anthropogenic noise inherent to urban seismic recordings. Here, we develop a deep-learning-based denoising algorithm, UrbanDenoiser, to filter out urban seismological noise. UrbanDenoiser strongly suppresses noise relative to the signals, because it was trained using waveform datasets containing rich noise sources from the urban Long Beach dense array and high signal-to-noise ratio (SNR) earthquake signals from the rural San Jacinto dense array. Application to the dense array data and an earthquake sequence in an urban area shows that UrbanDenoiser can increase signal quality and recover signals at an SNR level down to ~0 dB. Earthquake location using our denoised Long Beach data does not support the presence of mantle seismicity beneath Los Angeles but suggests a fault model featuring shallow creep, intermediate locking, and localized stress concentration at the base of the seismogenic zone.

58 GEOSCIENCES↗
DOE OSTI2021

FasTensor (FT) v0.0.1

The FasTensor implements the native array data programming mode namely SLOPE for modern data analysis tasks. It is designed to work in parallel on supercomputer and can handle terabyte data analysis tasks. It is thousand times faster than other data analysis systems such as Apache Spark and 38% faster than TensorFlow in dealing with large scale scientific data analysis. It has wide applications in image data analysis and mesh data analysis tasks in physical simulations and other fields.

Wu, Kesheng↗
DOE OSTI2024

Lightning and Radar Measures of Mixed-Phase Updraft Variability in Tracked Storms during the TRACER Field Campaign in Houston, Texas

Properties of 7488 thunderstorms are summarized for June–September 2022 during the Tracking Aerosol Convection Interactions Experiment (TRACER) field campaign Houston, Texas, using polarimetric weather radar and VHF 3D Lightning Mapping Array data. Automated tracking of storms linked each instrument’s measurements to a data-defined, time-evolving storm footprint. Within each storm, the depth and magnitude of episodic columns of radar differential reflectivity and specific differential phase quantified the prevalence of updrafts that activated mixed-phase precipitation pathways. Lightning measurements further distinguished the degree of rimed precipitation formation: the fraction of tracks with lightning varied from day to day and cells with lightning had stronger polarimetric columns. Track-level correlation of the lightning flash rate with radar polarimetric measures had substantial spread, showing that lightning provides an additional signal of mixed-phase precipitation processes that can complement future studies of thermodynamic and aerosol controls on cloud microphysics in the Houston region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

BASS XXXII: Studying the Nuclear Millimeter-wave Continuum Emission of AGNs with ALMA at Scales ≲100–200 pc

To understand the origin of nuclear (≲100 pc) millimeter-wave (mm-wave) continuum emission in active galactic nuclei (AGNs), we systematically analyzed subarcsecond resolution Band-6 (211–275 GHz) Atacama Large Millimeter/submillimeter Array data of 98 nearby AGNs (z < 0.05) from the 70 month Swift/BAT catalog. The sample, almost unbiased for obscured systems, provides the largest number of AGNs to date with high mm-wave spatial resolution sampling (~1–200 pc), and spans broad ranges of 14–150 keV luminosity {$40\lt \mathrm{log}[{L}_{14-150}/(\mathrm{erg}\,{{\rm{s}}}^{-1})]\lt 45$}, black hole mass $[5\lt \mathrm{log}({M}_{\mathrm{BH}}/{M}_{\odot })\lt 10$], and Eddington ratio ($-4\lt \mathrm{log}{\lambda }_{\mathrm{Edd}}\lt 2$). We find a significant correlation between 1.3 mm (230 GHz) and 14–150 keV luminosities. Its scatter is ≈0.36 dex, and the mm-wave emission may serve as a good proxy of the AGN luminosity, free of dust extinction up to N H ~ 10 26 cm –2 . While the mm-wave emission could be self-absorbed synchrotron radiation around the X-ray corona according to past works, we also discuss different possible origins of the mm-wave emission: AGN-related dust emission, outflow-driven shocks, and a small-scale (<200 pc) jet. The dust emission is unlikely to be dominant, as the mm-wave slope is generally flatter than expected. Also, due to no increase in the mm-wave luminosity with the Eddington ratio, a radiation-driven outflow model is possibly not the common mechanism. Furthermore, we find independence of the mm-wave luminosity on indicators of the inclination angle from the polar axis of the nuclear structure, which is inconsistent with a jet model whose luminosity depends only on the angle.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Redshift Evolution of the Feedback–Cooling Equilibrium in the Core of 48 SPT Galaxy Clusters: A Joint Chandra–SPT–ATCA Analysis

We analyze the cooling and feedback properties of 48 galaxy clusters at redshifts 0.4 < z < 1.3 selected from the South Pole Telescope (SPT) catalogs to evolve like the progenitors of massive and well-studied systems at z ~ 0. We estimate the radio power at the brightest cluster galaxy (BCG) location of each cluster from an analysis of Australia Telescope Compact Array data. Assuming that the scaling relation between the radio power and active galactic nucleus (AGN) cavity power P cav observed at low redshift does not evolve with redshift, we use these measurements in order to estimate the expected AGN cavity power in the core of each system. We estimate the X-ray luminosity within the cooling radius L cool of each cluster from a joint analysis of the available Chandra X-ray and SPT Sunyaev–Zel'dovich (SZ) data. This allows us to characterize the redshift evolution of the P cav /L cool ratio. When combined with low-redshift results, these constraints enable investigations of the properties of the feedback–cooling cycle across 9 Gyr of cluster growth. We model the redshift evolution of this ratio measured for cool-core clusters by a log-normal distribution Log-N (α + βz, σ 2 ) and constrain the slope of the mean evolution to β = -0.05 ± 0.47. This analysis improves the constraints on the slope of this relation by a factor of two. We find no evidence of redshift evolution of the feedback–cooling equilibrium in these clusters, which suggests that the onset of radio-mode feedback took place at an early stage of cluster formation. High values of P cav /L cool are found at the BCG location of noncool-core clusters, which might suggest that the timescales of the AGN feedback cycle and the cool core–noncool core transition are different. This work demonstrates that the joint analysis of radio, SZ, and X-ray data solidifies the investigation of AGN feedback at high redshifts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Hybrid power system control and operating strategy based on power system state vector calculation

Controlling a hybrid power system includes calculating a power system state vector based on energy demand and a stored data array including a matrix defined by a power system hardware configuration. The control further includes producing a power request based on the power system state vector, and varying a flow of energy amongst energy devices using drive linkages in the hybrid power system based on the power request. Related apparatus, control logic and controller structure is disclosed.

Guo, Fang↗
DOE OSTI2025

Glimpsing Physics of Nano-Hz Gravitational Waves in Neutrinos from Core-Collapse Supernovae

The growing evidence for nano-hertz gravitational waves, from NANOGrav and other observations, may be pointing to a cosmological first-order phase transition at temperatures of $\mathcal{O}(10-100)\;\mathrm{MeV}$. Such an interpretation requires dynamics beyond the Standard Model in this energy range. If so, it may well be the case that core-collapse supernova explosions would recreate the first-order phase transition leaving a unique imprint on the spectrum of neutrinos emitted in the initial few seconds. This scenario is also suggestive of a low-mass seesaw mechanism to explain neutrino masses. We outline the prospects for future observations of Galactic supernovae to uncover the signals of this scenario, which could get further confirmation with additional pulsar timing array data establishing the primordial origin of the observed nano-hertz gravitational waves.

Davoudiasl, Hooman [Brookhaven] (ORCID:00000003348↗
DOE OSTI2024

h5bench: A unified benchmark suite for evaluating HDF5 I/O performance on pre‐exascale platforms

Summary Parallel I/O is a critical technique for moving data between compute and storage subsystems of supercomputers. With massive amounts of data produced or consumed by compute nodes, high‐performant parallel I/O is essential. I/O benchmarks play an important role in this process; however, there is a scarcity of I/O benchmarks representative of current workloads on HPC systems. Toward creating representative I/O kernels from real‐world applications, we have created h5bench , a set of I/O kernels that exercise hierarchical data format version 5 (HDF5) I/O on parallel file systems in numerous dimensions. Our focus on HDF5 is due to the parallel I/O library's heavy usage in various scientific applications running on supercomputing systems. The various tests benchmarked in the h5bench suite include I/O operations (read and write), data locality (arrays of basic data types and arrays of structures), array dimensionality (one‐dimensional arrays, two‐dimensional meshes, three‐dimensional cubes), I/O modes (synchronous and asynchronous). In this paper, we present the observed performance of h5bench executed along several of these dimensions on existing supercomputers (Cori and Summit) and pre‐exascale platforms (Perlmutter, Theta, and Polaris). h5bench measurements can be used to identify performance bottlenecks and their root causes and evaluate I/O optimizations. As the I/O patterns of h5bench are diverse and capture the I/O behaviors of various HPC applications, this study will be helpful to the broader supercomputing and I/O community.

97 MATHEMATICS AND COMPUTING↗