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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 OSTI2022

The MeerKAT Galaxy Cluster Legacy Survey I. Survey Overview and Highlights

MeerKAT’s large number (64) of 13.5 m diameter antennas, spanning 8 km with a densely packed 1 km core, create a powerful instrument for wide-area surveys, with high sensitivity over a wide range of angular scales. The MeerKAT Galaxy Cluster Legacy Survey (MGCLS) is a programme of long-track MeerKAT L-band (900–1670 MHz) observations of 115 galaxy clusters, observed for ~6–10 h each in full polarisation. The first legacy product data release (DR1), made available with this paper, includes the MeerKAT visibilities, basic image cubes at ~8" resolution, and enhanced spectral and polarisation image cubes at ~8" and 15" resolutions. Typical sensitivities for the full-resolution MGCLS image products range from ~3–5 μJy beam –1 . The basic cubes are full-field and span 2° × 2°. The enhanced products consist of the inner 1.2° × 1.2° field of view, corrected for the primary beam. The survey is fully sensitive to structures up to ~10' scales, and the wide bandwidth allows spectral and Faraday rotation mapping. Relatively narrow frequency channels (209 kHz) are also used to provide H I mapping in windows of 0 < z < 0.09 and 0.19 < z < 0.48. Here, in this paper, we provide an overview of the survey and the DR1 products, including caveats for usage. We present some initial results from the survey, both for their intrinsic scientific value and to highlight the capabilities for further exploration with these data. These include a primary-beam-corrected compact source catalogue of ~626 000 sources for the full survey and an optical and infrared cross-matched catalogue for compact sources in the primary-beam-corrected areas of Abell 209 and Abell S295. We examine dust unbiased star-formation rates as a function of cluster-centric radius in Abell 209, extending out to 3.5 R 200 . We find no dependence of the star-formation rate on distance from the cluster centre, and we observe a small excess of the radio-to-100 μm flux ratio towards the centre of Abell 209 that may reflect a ram pressure enhancement in the denser environment. We detect diffuse cluster radio emission in 62 of the surveyed systems and present a catalogue of the 99 diffuse cluster emission structures, of which 56 are new. These include mini-halos, halos, relics, and other diffuse structures for which no suitable characterisation currently exists. We highlight some of the radio galaxies that challenge current paradigms, such as trident-shaped structures, jets that remain well collimated far beyond their bending radius, and filamentary features linked to radio galaxies that likely illuminate magnetic flux tubes in the intracluster medium. We also present early results from the H I analysis of four clusters, which show a wide variety of H I mass distributions that reflect both sensitivity and intrinsic cluster effects, and the serendipitous discovery of a group in the foreground of Abell 3365.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

The Atacama Cosmology Telescope: DR6 constraints on extended cosmological models

We use new cosmic microwave background (CMB) primary temperature and polarization anisotropy measurements from the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6) to test foundational assumptions of the standard cosmological model, ΛCDM, and set constraints on extensions to it. We derive constraints from the ACT DR6 power spectra alone, as well as in combination with legacy data from the Planck mission. To break geometric degeneracies, we include ACT and Planck CMB lensing data and baryon acoustic oscillation data from DESI Year-1. To test the dependence of our results on non-ACT data, we also explore combinations replacing Planck with WMAP and DESI with BOSS, and further add supernovae measurements from Pantheon+ for models that affect the late-time expansion history. We verify the near-scale-invariance (running of the spectral index dn s /d ln k = 0.0062 ± 0.0052) and adiabaticity of the primordial perturbations. Neutrino properties are consistent with Standard Model predictions: we find no evidence for new light, relativistic species that are free-streaming (N eff = 2.86 ± 0.13, which combined with astrophysical measurements of primordial helium and deuterium abundances becomes N eff = 2.89 ± 0.11), for non-zero neutrino masses (∑m ν < 0.089 eV at 95% CL), or for neutrino self-interactions. We also find no evidence for self-interacting dark radiation (N idr < 0.134), or for early-universe variation of fundamental constants, including the fine-structure constant (α EM /α EM,0 = 1.0043 ± 0.0017) and the electron mass (m e /m e,0 = 1.0063 ± 0.0056). Our data are consistent with standard big bang nucleosynthesis (we find Y p = 0.2312 ± 0.0092), the COBE/FIRAS-inferred CMB temperature (we find T CMB = 2.698 ± 0.016 K), a dark matter component that is collisionless and with only a small fraction allowed as axion-like particles, a cosmological constant (w = -0.986 ± 0.025), and the late-time growth rate predicted by general relativity (γ = 0.663 ± 0.052). We find no statistically significant preference for a departure from the baseline ΛCDM model. In fits to models invoking early dark energy, primordial magnetic fields, or an arbitrary modified recombination history, we find H 0 = 69.9 +0.8 -1.5 , 69.1 ± 0.5, or 69.6 ± 1.0 km/s/Mpc, respectively; using BOSS instead of DESI BAO data reduces the central values of these constraints by 1–1.5 km/s/Mpc while only slightly increasing the error bars. In general, models introduced to increase the Hubble constant or to decrease the amplitude of density fluctuations inferred from the primary CMB are not favored over ΛCDM by our data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

GeoThermalCloud framework for fusion of big data and multi-physics models in Nevada and Southwest New Mexico

Our GeoThermalCloud framework is designed to process geothermal datasets using a novel toolbox for unsupervised and physics-informed machine learning called SmartTensors. More information about GeoThermalCloud can be found at the GeoThermalCloud GitHub Repository. More information about SmartTensors can be found at the SmartTensors Github Repository and the SmartTensors page at LANL.gov. Links to these pages are included in this submission. GeoThermalCloud.jl is a repository containing all the data and codes required to demonstrate applications of machine learning methods for geothermal exploration. GeoThermalCloud.jl includes: - site data - simulation scripts - jupyter notebooks - intermediate results - code outputs - summary figures - readme markdown files GeoThermalCloud.jl showcases the machine learning analyses performed for the following geothermal sites: - Brady: geothermal exploration of the Brady geothermal site, Nevada - SWNM: geothermal exploration of the Southwest New Mexico (SWNM) region - GreatBasin: geothermal exploration of the Great Basin region, Nevada Reports, research papers, and presentations summarizing these machine learning analyses are also available and will be posted soon.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Anomaly detection in Hyper Suprime-Cam galaxy images with generative adversarial networks

ABSTRACT The problem of anomaly detection in astronomical surveys is becoming increasingly important as data sets grow in size. We present the results of an unsupervised anomaly detection method using a Wasserstein generative adversarial network (WGAN) on nearly one million optical galaxy images in the Hyper Suprime-Cam (HSC) survey. The WGAN learns to generate realistic HSC-like galaxies that follow the distribution of the data set; anomalous images are defined based on a poor reconstruction by the generator and outlying features learned by the discriminator. We find that the discriminator is more attuned to potentially interesting anomalies compared to the generator, and compared to a simpler autoencoder-based anomaly detection approach, so we use the discriminator-selected images to construct a high-anomaly sample of ∼13 000 objects. We propose a new approach to further characterize these anomalous images: we use a convolutional autoencoder to reduce the dimensionality of the residual differences between the real and WGAN-reconstructed images and perform UMAP clustering on these. We report detected anomalies of interest including galaxy mergers, tidal features, and extreme star-forming galaxies. A follow-up spectroscopic analysis of one of these anomalies is detailed in the Appendix; we find that it is an unusual system most likely to be a metal-poor dwarf galaxy with an extremely blue, higher-metallicity H ii region. We have released a catalogue with the WGAN anomaly scores; the code and catalogue are available at https://github.com/kstoreyf/anomalies-GAN-HSC; and our interactive visualization tool for exploring the clustered data is at https://weirdgalaxi.es.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Gaussian Process Optimization of Sensitivity-Based Similarity Metrics between New Nuclear Applications and New/Existing Benchmarks

Confidently designing safe, new nuclear criticality experiments requires expert judgement, which could take years of experience. Sensitivity/uncertainty (S/U) analysis can be utilized by less experienced individuals to conservatively estimate uncertainties in important parameters, such as k eff , in newly proposed nuclear applications. This type of analysis relies on matching new nuclear applications with existing benchmark experiments. The Whisper-1.1 software package included in MCNP6.2 ®1 contains more than 1,100 International Criticality Safety Benchmark Evaluation Project (ICSBEP) benchmarks. These benchmarks however rarely match new nuclear applications. The number of benchmarks available to match a given set of materials or geometric configurations varies significantly. Furthermore, recently performed benchmark experiments may not have had enough time to be properly documented and published. Benchmarks are vital for determining the accuracy of nuclear data and can assist nuclear physics and evaluators in improving nuclear data libraries. Exhaustively exploring the parameter space using simulations with software such as MCNP is too computationally expensive. In this work, Gaussian process optimization was implemented to reduce the number of simulations needed for optimization over multiple parameters. This optimization scheme was designed to selectively generate new benchmarks with high sensitivity-based similarity metrics to user-defined nuclear applications. Two benchmark models of spherically nested shells containing plutonium, uranium, tantalum, and water were used in the optimization to match an application containing plutonium plates, stacked in a tantalum reflector, surrounded by water.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

The Exploration of Local VolumE Satellites (ELVES) Survey: A Nearly Volume-limited Sample of Nearby Dwarf Satellite Systems

We present the final sample of the Exploration of Local VolumE Satellites (ELVES) survey, a survey of the dwarf satellites of a nearly volume-limited sample of Milky Way (MW)-like hosts in the Local Volume. Hosts are selected simply via a cut in luminosity (${M}_{{K}_{s}}\lt -22.1$ mag) and distance (D < 12 Mpc). We cataloged the satellites of 25 of the 31 such hosts, with another five taken from the literature. All hosts are surveyed out to at least 150 projected kpc ( ~ R vir /2), with the majority surveyed to 300 kpc ( ~ R vir ). Satellites are detected using a consistent semiautomated algorithm specialized for low surface brightness dwarfs. As shown through extensive tests with injected galaxies, the catalogs are complete to M V ~ -9 mag and μ 0,V ~ 26.5 mag arcsec -2 . Candidates are confirmed to be real satellites through distance measurements including redshift, tip of the red giant branch, and surface brightness fluctuations. Across all 30 surveyed hosts, there are 338 confirmed satellites with M V < -9 mag, with a further 106 candidates awaiting distance measurement. For the vast majority of these, we provide consistent multiband Sérsic photometry. We show that satellite abundance correlates with host mass, with the MW being quite typical among comparable systems, and that satellite quenched fraction rises steeply with decreasing satellite mass, mirroring the quenched fraction for the MW and M31. The ELVES survey represents a massive increase in the statistics of surveyed systems with known completeness, and the provided catalogs are a unique data set to explore various aspects of small-scale structure and dwarf galaxy evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Warming of the lower Columbia River , 1853 to 2018

Abstract Water temperature is a critical ecological indicator; however, few studies have statistically modeled century‐scale trends in riverine or estuarine water temperature, or their cause. Here, we recover, digitize, and analyze archival temperature measurements from the 1850s onward to investigate how and why water temperatures in the lower Columbia River are changing. To infill data gaps and explore changes, we develop regression models of daily historical Columbia River water temperature using time‐lagged river flow and air temperature as the independent variables. Models were developed for three time periods (mid‐19th, mid‐20th, and early 21st century), using archival and modern measurements (1854–1876; 1938–present). Daily and monthly averaged root‐mean‐square errors overall are 0.89°C and 0.77°C, respectively for the 1938–2018 period. Results suggest that annual averaged water temperature increased by 2.2°C ± 0.2°C since the 1850s, a rate of 1.3°C ± 0.1°C/century. Increased water temperatures are seasonally dependent. An increase of approximately 2.0°C ± 0.2°C/century occurs in the July–Dec time‐frame, while springtime trends are statistically insignificant. Rising temperatures change the probability of exceeding ecologically important thresholds; since the 1850s, the number of days with water temperatures over 20°C increased from ~5 to 60 per year, while the number below 2°C decreased from ~10 to 0 days/per year. Overall, the modern system is warmer, but exhibits less temperature variability. The reservoir system reduces sensitivity to short‐term atmospheric forcing. Statistical experiments within our modeling framework suggest that increased water temperature is driven by warming air temperatures (~29%), altered river flow (~14%), and water resources management (~57%).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Dark kinetic heating of exoplanets and brown dwarfs

Dark kinetic heating of neutron stars has been previously studied as a promising dark matter detection avenue. Kinetic heating occurs when dark matter is sped up to relativistic speeds in the gravitational well of high-escape velocity objects, and deposits kinetic energy after becoming captured by the object, thereby increasing its temperature. We show that dark kinetic heating can be significant even in objects with low-escape velocities, such as exoplanets and brown dwarfs, increasing the discovery potential of such searches. This can occur if there is a long-range dark force, creating a “dark escape velocity”, leading to heating rates substantially larger than those expected from neutron stars. We consequently set constraints on dark sector parameters using Wide-field Infrared Survey Explorer and JWST data on Super-Jupiter WISE 0855-0714, and map out future sensitivity to the dark matter scattering cross section below 10 −40 cm 2 . We compare dark kinetic heating rates of other lower escape velocity objects such as the Earth, Sun, and white dwarfs, finding complementary kinetic heating signals are possible depending on particle physics parameters.

Astrophysics↗
DOE OSTI2021

Chain elongators, friends, and foes

Bioproduction of medium chain carboxylic acids has recently emerged as an alternative strategy to valorize low-value organic waste and side-streams. Key to this route is chain elongation, an anaerobic microbial process driven by ethanol, lactic acid, or carbohydrates. Additionally, because these technologies use wastes as feedstocks, mixed microbial communities are often considered as biocatalysts. Understanding and steering these microbiomes is key to optimize bioprocess performance. From a meta-analysis of publicly available sequencing data, we (i) explore how the current collection of isolated chain elongators compares to microbiome members, (ii) discuss the main beneficial and antagonistic interactions with community partners, and (iii) identify the key research gaps and needs to help understand chain elongation microbiomes, and design/steer these novel bioproduction processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Improving the accuracy of freight mode choice models: A case study using the 2017 CFS PUF data set and ensemble learning techniques

Here, the US Census Bureau has collected two rounds of experimental data from the Commodity Flow Survey, providing shipment-level characteristics of nationwide commodity movements, published in 2012 (i.e., Public Use Microdata) and in 2017 (i.e., Public Use File). With this information, data-driven methods have become increasingly valuable for understanding detailed patterns in freight logistics. In this study, we used the 2017 Commodity Flow Survey Public Use File data set to explore building a high-performance freight mode choice model, considering three main improvements: (1) constructing local models for each separate commodity/industry category; (2) extracting useful geographical features, particularly the derived distance of each freight mode between origin/destination zones; and (3) applying additional ensemble learning methods such as stacking or voting to combine results from local and unified models for improved performance. The proposed method achieved over 92% accuracy without incorporating external information, an over 19% increase compared to directly fitting Random Forests models over 10,000 samples. Furthermore, SHAP (Shapely Additive Explanations) values were computed to explain the outputs and major patterns obtained from the proposed model. The model framework could enhance the performance and interpretability of existing freight mode choice models.

42 ENGINEERING↗
DOE OSTI2023

At the interfaces of the hydrologic sciences: Connecting water, elements, ecosystems, and people through the major contributions of Dr. Emily Bernhardt

Here, in this paper, we describe the major contributions of Professor Emily Bernhardt to the hydrologic sciences. Dr. Bernhardt’s work addresses how carbon, nutrient, and contaminant dynamics respond to a wide range of environmental perturbations that alter hydrologic dynamics within and connectivity among ecosystems. Her research leverages intensive and extensive field sampling, experimental manipulations, macroscale data harmonization and exploration, and continental to global-scale synthesis activities to uncover key drivers and patterns of the impacts human perturbations have on water and elemental cycles. Dr. Bernhardt’s research program is defined by her ability to ask questions and use approaches that explicitly consider connectivity and interfaces in a variety of ways. Here, we highlight significant contributions from Dr. Bernhardt’s work, organized by connectivity, interfaces, and interactions among and across (1) elemental cycles, (2) ecosystems, (3) watersheds, (4) scales, and (5) disciplines. We conclude with a section on Dr. Bernhardt’s impact on the hydrologic sciences and beyond through her exceptional dedication to mentorship, engagement, and service.

58 GEOSCIENCES↗
DOE OSTI2023

Insights into Constraining Rate Coefficients in Fuel Oxidation Mechanisms Using Genetic Algorithm Optimization

Accurate fuel oxidation mechanisms can enable predictive capabilities that aid in advancing combustion technologies. High-level computational kinetics can yield reasonable rate coefficients with uncertainties, in some cases, below a factor of 2. Computed rate coefficients can be constrained further by optimizing against experimental data. Here, we explore the application of genetic algorithm (GA) optimization to constrain computed rate coefficients in complex fuel oxidation mechanisms in conjunction with temperature-dependent species mole fractions from jet-stirred reactor (JSR) measurements. Cyclohexane is a model candidate for understanding the reactivity of cyclic fuels. In this work, we optimize the rate coefficients of the most recent literature cyclohexane mechanism, which incorporates theoretically computed rate coefficients for the reaction networks stemming from the first and second O 2 addition pathways, against the experimental results of two separate literature JSR studies. Optimization consistency is evaluated by carrying out three GA optimizations: fitting to the temperature-dependent species mole fractions in each JSR experiment separately and simultaneously fitting the species mole fractions in both experiments. Local sensitivity analyses are used to identify five influential low-temperature oxidation reactions for optimization. Although the three optimizations do not yield identical rate coefficients, the direction of change in all five rate coefficients is consistent among the three optimizations. Performance of the models from the three optimizations is assessed against literature ignition delay times with differences in the level of agreement observed among the different optimizations. In conclusion, comparisons are made with our recent optimization work of a cyclopentane oxidation master-equation model against time-resolved species concentrations, and insights and improvements of the strategy for constraining rate coefficients using GA optimization are discussed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

RBSP-ECT Combined Pitch Angle Resolved Electron Flux Data Product

We describe a new data product combining pitch angle resolved electron flux measurements from the Radiation Belt Storm Probes (RBSP) Energetic Particle Composition and Thermal Plasma (ECT) suite on the National Aeronautics and Space Administration’s Van Allen Probes. Here, we describe the methodology used to combine each of the data sets and produce a consistent set of pitch-angle-resolved spectra for the entire Van Allen Probes mission. Three-minute-averaged flux spectra are provided spanning energies from 15 eV up to 20 MeV. This new data product offers researchers a consistent cross calibrated data set to explore the particle dynamics of the inner magnetosphere across a wide range of energies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Improved thermonuclear rate of 42 Ti( p , γ ) 43 V and its astrophysical implication in the rp process

Context. Accurate 42 Ti(p,γ) 43 V reaction rates are crucial for understanding the nucleosynthesis path of the rapid capture process (rp process) that occurs in X-ray bursts. Aims. We aim to improve the thermonuclear rates of 42 Ti(p,γ) 43 V based on more complete resonance information and a more accurate direct component, together with the recently released nuclear masses data. We also explore the impact of the newly obtained rates on the rp process. Methods. We reevaluated the reaction rate of 42 Ti(p,γ) 43 V by the sum of the isolated resonance contribution instead of the Hauser-Feshbach statistical model. We used a Monte Carlo method to derive the associated uncertainties of new rates. The nucleosynthesis simulations were performed via the NuGrid post-processing code ppn. Results. The new rates differ from previous estimations due to the use of a series of updated resonance parameters and a direct S factor. Compared with the previous results from the Hauser-Feshbach statistical model, which assumes compound nucleus 43 V with a sufficiently high-level density in the energy region of astrophysical interest, large differences exist over the entire temperature region of rp-process interest, up to two orders of magnitude. We consistently calculated the photodisintegration rate using our new nuclear masses via the detailed balance principle, and found the discrepancies among the different reverse rates are much larger than those for the forward rate, up to ten orders of magnitude at the temperature of 10 8 K. Using a trajectory with a peak temperature of 1.95×10 9 K, we performed the rp-process nucleosynthesis simulations to investigate the impact of the new rates. Our calculations show that the adoption of the new forward and reverse rates result in abundance variations for Sc and Ca of 128% and 49%, respectively, compared to the variations for the statistical model rates. On the other hand, the overall abundance pattern is not significantly affected. The results of using new rates also confirm that the rp-process path does not bypass the isotope 43 V. Conclusions. Our study found that the Hauser-Feshbach statistical model is inappropriate to the reaction rate evaluation for 42 Ti(p,γ) 43 V. The adoption of the new rates confirms that the reaction path of 42 Ti(p,γ) 43 V(p,γ) 44 Cr(β + ) 44 V is a key branch of the rp process in X-ray bursts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

On-Demand Column Joining for High Energy Physics

As the Large Hadron Collider (LHC) transitions into the High-Luminosity LHC (HL-LHC) era, the volume of data to be processed is expected to increase significantly. The CMS Experiment currently utilizes various data formats, including AOD, MiniAOD, and NanoAOD, each with different levels of detail and storage requirements. This paper addresses the challenges of data duplication and storage inefficiencies in high-energy physics (HEP) analyses by proposing an on-demand column-joining solution. This approach aims to reduce data duplication by enabling the dynamic combination of NanoAOD data with auxiliary information from larger data tiers, such as MiniAOD. The proposed solution leverages Trino, a high-performance distributed SQL query engine, to perform efficient and scalable data joins. Benchmarks using CMS OpenData demonstrate the feasibility of this approach, showing that it can handle large datasets with low latency. Integration with the scikit-hep ecosystem and the coffea analysis framework is also discussed, highlighting the potential for seamless end-to-end data processing and analysis. Ongoing and future work focuses on expanding benchmarks, integrating ServiceX for data transformation, and exploring the use of native object storage solutions.

Manganelli, Nicholas [Northeastern U.]↗
DOE OSTI2025

Stockmayer fluid simulations for viscosity and glass transition temperature of ionic liquids

We develop a Stockmayer fluid model for molecular dynamics simulations of ionic liquids that captures molecular polarization, ionic conductivity, viscosity, and glass transition temperature, using ethylammonium nitrate (EAN) as an example. The ions in EAN are treated as spheres interacting via the Lennard-Jones potential with an embedded point charge and a permanent dipole moment. We show that our simulation results for EAN are consistent with experimental data and then explore the effects of the molecular parameters on the viscosity of ionic liquids. Our results indicate that viscosity monotonically increases with ionic charge and dipole moment but non-monotonically changes with ionic diameter (or molar volume). This non-monotonic trend arises from the competition among the electrostatic interactions, molecular packing, and size asymmetry between the cation and anion. In conclusion, our model also shows that long-lived ion pairs result in higher viscosities.

Coarse-grained simulations↗
DOE OSTI2020

Ab initio leading order effective potentials for elastic nucleon-nucleus scattering

Calculating microscopic effective interactions (optical potentials) for elastic nucleon-nucleus scattering has already in the past led to a large body of work. For first-order calculations a nucleon-nucleon ( NN ) interaction and a one-body density of the nucleus were taken as input to rigorous calculations of microscopic full-folding calculations. Based on the spectator expansion of the multiple scattering series we employ a chiral next-to-next-to-leading order (NNLO) nucleon-nucleon interaction on the same footing in the structure as well as in the reaction calculation to obtai n an in leading-order consistent effective potential for nucleon-nucleus elastic scattering, which includes the spin of the struck target nucleon. The first order effective folding potential is computed by first deriving a nonlocal scalar density as well as a spin-projected momentum distribution. Those are then integrated with the off-shell Wolfenstein amplitudes $A$, $C$, and $M $. The resulting nonlocal potential serves as input to a momentum-space Lippmann-Schwinger equation, whose solutions are summed to obtain the nucleon-nucleus scattering observables. As a result, we calculate elastic scattering observables for $^4$He, $^6$He, $^8$He, $^{12}$C, and $^{16}$O in the energy r egime between 100 and 200~MeV projectile kinetic energy, and compare to available data. We also explore the extension down to about 70 MeV, and study the effect of ignoring the spin of the struck nucleon in the In our calculations we contrast elastic scattering off closed-shell and open-shell nuclei. We find that fo r closed-shell nuclei the approximation of ignoring the spin of the struck target nucleon is excellent. We only see effects of the spin of the struck target nucleon when considering $^6$He and $^8$He, which are nuclei with a $N/Z$ ratio larger than 1.

6 ≤ A ≤ 19↗