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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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657 records · Page 37

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DOE OSTI2022

Spectral analysis of ultra-cool white dwarfs polluted by planetary debris

ABSTRACT We identify two ultra-cool (${T_\mathrm{eff}}\lt 4000$ K) metal-polluted (DZ) white dwarfs WD J2147−4035 and WD J1922+0233 as the coolest and second coolest DZ stars known to date with ${T_\mathrm{eff}}\approx 3050$ K and ${T_\mathrm{eff}}\approx 3340$ K, respectively. Strong atmospheric collision-induced absorption (CIA) causes the suppression of red optical and infrared flux in WD J1922+0233, resulting in an unusually blue colour given its low temperature. WD J2147−4035 has moderate infrared CIA yet has the reddest optical colours known for a DZ white dwarf. Microphysics improvements to the non-ideal effects and CIA opacities in our model atmosphere code yields reasonable solutions to observations of these ultra-cool stars. WD J2147−4035 has a cooling age of over 10 Gyr which is the largest known for a DZ white dwarf, whereas WD J1922+0233 is slightly younger with a cooling age of 9 Gyr. Galactic kinematics calculations from precise Gaia EDR3 astrometry reveal these ultra-cool DZ stars as likely members of the Galactic disc thus they could be pivotal objects in future studies constraining an upper age limit for the disc of the Milky Way. We present intermediate-resolution spectroscopy for both objects, which provides the first spectroscopic observations of WD J2147−4035. Detections of sodium and potassium are made in both white dwarfs, in addition to calcium in WD J1922+0233 and lithium in WD J2147−4035. We identify the magnetic nature of WD J2147−4035 from Zeeman splitting in the lithium line and also make a tentative detection of carbon, so we classify this star as DZQH. WD J1922+0233 likely accreted planetary crust debris, while the debris composition that polluted WD J2147−4035 remains unconstrained.

Elms, Abbigail K. (ORCID:0000000176667416)↗

DOE OSTI2023

Neutral Atomic Beam Techniques for Diagnosis of Edge and Scrape-Off Layer Plasmas (2017-2019) [and subsequently titled] Neutral Atomic Beam Techniques for Diagnosis of Interior and Scrape-Off Layer Plasmas (2019-2022) (Final Report)

This is the Final Technical Report for the DOE Measurement Innovation grant DE-SC0017998 titled Neutral Atomic Beam Techniques for Diagnosis of Edge and Scrape-Off Layer Plasmas (2017-2019) and subsequently titled Neutral Atomic Beam Techniques for Diagnosis of Interior and Scrape-Off Layer Plasmas (2019-2022). The grant was funded by the Office of Fusion Energy Sciences (FES) for the period 1 September 2017 – 31 May 2022. Work performed through the grant has resulted in diagnostic innovations and new measurement capabilities that could improve understanding of magnetic equilibrium, edge localized modes in tokamaks, and bootstrap and Pfirsch-Schlüter currents in stellarators. We have performed simulations of a novel non-hydrogen beam-based diagnostic that would determine a local value (in the plasma) of the poloidal flux function by measuring the toroidal angle of the beam particles' velocity at a detector external to the plasma. The approach is similar to that of a Heavy Ion Beam Probe (HIBP), but would be able to operate with a smaller, more economical detection systems and, in doing so, may enable substantial extension of scenarios in which beam-based diagnostics are deployed. Key results include: Identification of the neutral and singly charged atoms that are most appropriate for use in the beam based diagnostic and the ionization cross-sections that are important for studying the plasma edge of a large device; Development of a promising geometry for operation in which the beam is injected horizontally near the torus midplane; Implementation of simulations using banana orbit trajectories of secondary beam ions to access plasma regions that are commonly in-accessible to an HIBP diagnostic, including sample volumes near the X-point of a diverted tokamak; Assessment of the ability of the diagnostic concept (originally developed under the assumption of, and reliant on, symmetry) to work in stellarators by performing simulations with both quasi-symmetric fields and non-quasi-symmetric fields; Measurement of noise levels with a prototype detector on a stellarator and exploration of techniques to minimize effects of noise on a diagnostic.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Accounting for Training Data Error in Machine Learning Applied to Earth Observations

Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is particularly relevant for image-interpreted TD, an increasingly commonly used method due to its practicality and the increasing training sample size requirements of modern ML algorithms. TD errors can cause substantial errors in the maps created using ML algorithms, which may impact map use and interpretation. Despite these potential errors and their real-world consequences for map-based decisions, TD error is often not accounted for or reported in EO research. Here we review the current practices for collecting and handling TD. We identify the sources of TD error, and illustrate their impacts using several case studies representing different EO applications (infrastructure mapping, global surface flux estimates, and agricultural monitoring), and provide guidelines for minimizing and accounting for TD errors. To harmonize terminology, we distinguish TD from three other classes of data that should be used to create and assess ML models: training reference data, used to assess the quality of TD during data generation; validation data, used to iteratively improve models; and map reference data, used only for final accuracy assessment. We focus primarily on TD, but our advice is generally applicable to all four classes, and we ground our review in established best practices for map accuracy assessment literature. EO researchers should start by determining the tolerable levels of map error and appropriate error metrics. Next, TD error should be minimized during sample design by choosing a representative spatio-temporal collection strategy, by using spatially and temporally relevant imagery and ancillary data sources during TD creation, and by selecting a set of legend definitions supported by the data. Furthermore, TD error can be minimized during the collection of individual samples by using consensus-based collection strategies, by directly comparing interpreted training observations against expert-generated training reference data to derive TD error metrics, and by providing image interpreters with thorough application-specific training. We strongly advise that TD error is incorporated in model outputs, either directly in bias and variance estimates or, at a minimum, by documenting the sources and implications of error. TD should be fully documented and made available via an open TD repository, allowing others to replicate and assess its use. To guide researchers in this process, we propose three tiers of TD error accounting standards. Finally, we advise researchers to clearly communicate the magnitude and impacts of TD error on map outputs, with specific consideration given to the likely map audience.

58 GEOSCIENCES↗

DOE OSTI2025

Huge ensembles – Part 1: Design of ensemble weather forecasts using spherical Fourier neural operators

Abstract. Simulating low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems presently use up to 100 members, larger ensembles could enrich the sampling of internal variability. They may capture the long tails associated with climate hazards better than traditional ensemble sizes. Due to computational constraints, it is infeasible to generate huge ensembles (comprised of 1000–10 000 members) with traditional, physics-based numerical models. In this two-part paper, we replace traditional numerical simulations with machine learning (ML) to generate hindcasts of huge ensembles. In Part 1, we construct an ensemble weather forecasting system based on spherical Fourier neural operators (SFNOs), and we discuss important design decisions for constructing such an ensemble. The ensemble represents model uncertainty through perturbed-parameter techniques, and it represents initial condition uncertainty through bred vectors, which sample the fastest-growing modes of the forecast. Using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) as a baseline, we develop an evaluation pipeline composed of mean, spectral, and extreme diagnostics. With large-scale, distributed SFNOs with 1.1 billion learned parameters, we achieve calibrated probabilistic forecasts. As the trajectories of the individual members diverge, the ML ensemble mean spectra degrade with lead time, consistent with physical expectations. However, the individual ensemble members' spectra stay constant with lead time. Therefore, these members simulate realistic weather states during the rollout, and the ML ensemble passes a crucial spectral test in the literature. The IFS and ML ensembles have similar extreme forecast indices, and we show that the ML extreme weather forecasts are reliable and discriminating. These diagnostics ensure that the ensemble can reliably simulate the time evolution of the atmosphere, including low-likelihood high-impact extremes. In Part 2, we generate a huge ensemble initialized each day in summer 2023, and we characterize the simulations of extremes.

Mahesh, Ankur↗

DOE OSTI2021

Improved ELMv1-ECA simulations of zero-curtain periods and cold-season CH 4 and CO 2 emissions at Alaskan Arctic tundra sites

Field measurements have shown that cold-season methane (CH 4 ) and carbon dioxide (CO 2 ) emissions contribute a substantial portion to the annual net carbon emissions in permafrost regions. However, most earth system land models do not accurately reproduce cold-season CH 4 and CO 2 emissions, especially over the shoulder (i.e., thawing and freezing) seasons. Here we use the Energy Exascale Earth System Model (E3SM) land model version 1 (ELMv1-ECA) to tackle this challenge and fill the knowledge gap of how cold-season CH 4 and CO 2 emissions contribute to the annual totals at Alaska Arctic tundra sites. Specifically, we improved the ELMv1-ECA soil water phase-change scheme, environmental controls on microbial activity, and the methane module. Results demonstrate that both soil temperature and the duration of zero-curtain periods (i.e., the fall period when soil temperatures linger around 0°C) simulated by the updated ELMv1-ECA were greatly improved; e.g., the mean absolute error (MAE) in zero-curtain durations at 12 cm depth was reduced by 62 % on average. Furthermore, the MAEs of simulated cold-season carbon emissions at three tundra sites were improved by 72 % and 70 % on average for CH 4 and CO 2 , respectively. Overall, CH 4 emitted during the early cold season (September and October), which often includes most of the zero-curtain period in Arctic tundra, accounted for more than 50 % of the total emissions throughout the entire cold season (September to May) in the model, compared with around 49.4 % (43 %–58 %) in observations. From 1950 to 2017, both CO 2 emissions during the zero-curtain period and during the entire cold season showed increasing trends, for example, of 0.17 and 0.36 gC m -2 yr -1 at Atqasuk. This study highlights the importance of zero-curtain periods in facilitating cold-season CH 4 and CO 2 emissions from tundra ecosystems.

58 GEOSCIENCES↗

DOE OSTI2020

E3SM simulation results and associated python analysis scripts

This archive contains E3SM Land Model simulation results associated with the Journal of Advances in Modeling Earth Systems (JAMES) article titled "More Realistic Intermediate Depth Dry Firn Densification in the Energy Exascale Earth System Model (E3SM)," by Adam M. Schneider, Charles S. Zender, and Stephen F. Price. Also included in the archive are python scripts used to analyze associated data and an offline, statistical firn model.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Empowering Geothermal Research: The Geothermal Data Repository's New AI Research Assistant: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has integrated a Large Language Model (LLM) with the metadata and supporting documents associated with GDR datasets to create an Artificially Intelligent (AI) research assistant. By leveraging work done to make GDR metadata machine-readable and an open-source LLM integration model called the Energy Language Model, developed by the National Renewable Energy Laboratory, AskGDR serves as a virtual research assistant to GDR users. It provides answers to a variety of user-provided questions using natural language processing and generative machine learning. Users can get answers to questions about specific datasets, including inquiries about the equipment, assumptions and methodologies used in the origination of the data; or more abstract questions, such as the applicability of data to specific research fields. AskGDR improves the discoverability of geothermal data by helping guide users to datasets beyond simple keyword searches. It enables users to find data based on properties of the data, discover information contained within supporting documents, and explore data from projects related to their research objectives.

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DOE OSTI2025

Empowering Geothermal Research: The Geothermal Data Repository's New AI Research Assistant

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DOE OSTI2025

Lessons Learned from AskGDR: Usage and Impact Analysis of the Geothermal Data Repository's AI Research Assistant: Preprint

In October of 2024, the Department of Energy's (DOE) Geothermal Data Repository (GDR) team officially launched AskGDR, an AI research assistant resulting from the integration of a Large Language Model (LLM) with the metadata and supporting documents associated with GDR datasets. AskGDR allows GDR users to ask deeper questions about the origin of datasets, the methods used to collect them, and the findings they help support. Using Retrieval Augmented Generation (RAG), AskGDR can be used to summarize findings spread across dozens of papers and technical reports or to extract relevant information describing a single data field. However, generative AI is experimental. The National Renewable Energy Laboratory (NREL) has been collecting metrics on AskGDR and documenting lessons learned during its deployment. This paper will outline the efficacy and impact of AskGDR through analysis of its use, operating costs, number and types of questions asked, and the quality of answers provided.

15 GEOTHERMAL ENERGY↗