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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Core Model Proposal #399: Updating the SSP Database (v3.0) (Population, GDP, and Labor Force) and Labor Productivity (KLEM)

This Core Model Proposal (CMP) updates the Shared Socioeconomic Pathway (SSP) database to a recent version (v3.0.1; released in 2024) within GCAM. Currently, GCAM relies on socioeconomic drivers, including population, GDP, and labor force projections, from the original SSP database version released in 2013. These projections, provided by independent socioeconomic dynamic models (e.g., multi-dimensional demographic models and macroeconomic models of convergence growth), may need regular updates when (1) near-term observations become available and (2) there are updates and advancements in the socioeconomic modeling. Timely updates of socioeconomic drivers in global economic equilibrium and multisector dynamic modeling will ensure (1) alignment of historical years and near-term projections with observations, enhancing base year calibrations, including calibration parameters and labor productivity, and (2) improvement of long-term projections with updated socioeconomic drivers, which set the scale of the economy. This CMP updates the SSP data (from v2013 to v2024) and also fixes/reconciles historical GDP data sources in GCAM. We investigate the impact of these updates on GCAM projections.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Prototype Software to Demonstrate a Data Catalog for Hanford Environmental Datasets

Ensuring that data on long-term environmental remediation at the Hanford Site is high-quality, traceable, and easily accessible is an ongoing challenge, complicated by decades of data collection, multiple contractors maintaining data sources, and the wide range of data types. A centralized data catalog, known as the Hanford Environmental Information and Data Index (HEIDI), has been under development as part of the Hanford Environmental Data Management (HEDM) program to address these challenges. HEIDI fulfills a critical need to bring together a wide range of data types and sizes from multiple authoritative data sources, while documenting the data pedigree and quality information (i.e., traceable to the data source/originator). This document describes additional development and maturation of the HEIDI prototype. Key accomplishments included deploying the catalog software, Esri Geoportal Server, on a server accessible to Hanford Local Area Network users, conducting cybersecurity evaluations, investigating integrated authentication solutions, and conducting functional testing of the catalog prototype. The server-based deployment enabled targeted feedback, leading to enhancements including improved accessibility features and an expanded metadata schema. Specifications for the server-based deployment of the prototype catalog and the HEIDI metadata schema are provided in this document to support subsequent HEIDI deployment by the U.S. Department of Energy Richland Operations Office.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Development of a 95-Year Solar Dataset for Resource Adequacy Studies

Long-term high-resolution solar data provides enhanced understanding of variability of solar generation and enhances our ability to develop strategies for a resilient and reliable electric grid under high deployment of solar energy. Therefore, it is important to develop long-term synthetic datasets that can provide multiple occurrences of various severe weather scenarios that are expected to test the limits of resource adequacy under scenarios contain various energy generation sources. Examples of such scenarios could be long periods of high temperatures when demand for electricity is high or periods where high winds could lead to a shut-down of transmission lines for long periods of time to ensure fire safety. NREL has developed the first version of such a dataset covering a 95-year period covering 2006-2100 at a 4km hourly resolution. This dataset contains all variables necessary to calculate solar generation. During development of this dataset, we focused on creating unbiased, high-resolution solar irradiance through statistical downscaling methods, using Regional Climate Model (RCM) simulations from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) as input. The National Solar Radiation Database (NSRDB) containing over 25 years of observations was used to calibrate the statistical downscaling models. This presentation will outline the primary steps in developing this dataset, including (1) regridding RCM data to a common grid at 20-km resolution, (2) correcting RCM biases with NSRDB, (3) applying temporal and spatial downscaling methods to generate high-resolution (4-km, hourly) solar and ancillary data. Additionally, we will present an evaluation of the downscaled data against the NSRDB across various zones in the CONUS. Lastly, we will present a user guide for accessing the datasets.

14 SOLAR ENERGY↗
DOE OSTI2023

A Fusion of Geothermal and InSAR Data with Machine Learning for Enhanced Deformation Forecasting at the Geysers

The Geysers geothermal field in California is experiencing land subsidence due to the seismic and geothermal activities taking place. This poses a risk not only to the underlying infrastructure but also to the groundwater level which would reduce the water availability for the local community. Because of this, it is crucial to monitor and assess the surface deformation occurring and adjust geothermal operations accordingly. In this study, we examine the correlation between the geothermal injection and production rates as well as the seismic activity in the area, and we show the high correlation between the injection rate and the number of earthquakes. This motivates the use of this data in a machine learning model that would predict future deformation maps. First, we build a model that uses interferometric synthetic aperture radar (InSAR) images that have been processed and turned into a deformation time series using LiCSBAS, an open-source InSAR time series package, and evaluate the performance against a linear baseline model. The model includes both convolutional neural network (CNN) layers as well as long short-term memory (LSTM) layers and is able to improve upon the baseline model based on a mean squared error metric. Then, after getting preprocessed, we incorporate the geothermal data by adding them as additional inputs to the model. This new model was able to outperform both the baseline and the previous version of the model that uses only InSAR data, motivating the use of machine learning models as well as geothermal data in assessing and predicting future deformation at The Geysers as part of hazard mitigation models which would then be used as fundamental tools for informed decision making when it comes to adjusting geothermal operations.

Yazbeck, Joe (ORCID:0000000302235260)↗
DOE OSTI2023

Impacts of Lanthanide Ion Complexation on the Radiation Robustness of Diglycolamide Extractants

Nuclear power reactors represent a clean and reliable source of baseload energy. However, nuclear reactors produce used nuclear fuel (UNF), which, if not recycled/reprocessed, must be disposed of as high-level radioactive waste, necessitating long-term storage options. UNF contains approximately a third of the periodic table, including the radioactive minor actinides (MA) americium and curium. Removal of these MA from UNF would greatly reduce the radiotoxic burden, volume, and cost of a storage facility or geological repository. Thus, a significant global effort has been devoted to the design of reprocessing strategies for the clean separation of the MA from UNF. As MA separations are difficult, due to the presence of lanthanide elements in UNF and their remarkably similar chemical properties, many extractants have been studied. The diglycolamide (DGA) class of extractants are promising for this separation as they exhibit high distribution coefficients for the MAs, high metal loading capacities, and are robust in highly acidic radiation environments.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

Geospatial Data Platform for All

Spatiotemporal data has evolved in scale due to augmented use in cross-domain applications. Simultaneously, there is substantial growth in the availability of Geographic Information Systems (GIS) data provided by the United States Geological Survey (USGS) along with other federal, state, county, or local agencies through open-data portals and public access APIs. However, data availability does not equate with accessibility. Large-scale analyses and applications require robust, performant data management with co-location of data storage and computing. The insufficiency of data management infrastructure compels researchers to adopt ad hoc project- specific GIS data storage solutions (e.g., copying data to High-Performance computer file systems). As an ad hoc storage strategy does not scale, it hampers cross-domain analyses causing difficulty in data reuse and utilizing existing code bases. Furthermore, GIS data is complex and requires expertise to analyze and manipulate due to its intricate data structures and data-specific projection transformations. Despite the challenges, we recognize that derived GIS data products, e.g., satellite or LIDAR-based images, can be used in downstream applications such as AI by domain, but non-GIS experts. To address the data needs and overcome the challenges, we are working towards a GIS Data Platform focused on efficient data storage, data discovery and access, and an API to enable common workflows. We propose a knowledge-graph (KG) approach for data discovery, whereby datasets are semantically linked to higher- level constructs such as projects and research areas. The semantic data links enable researchers to explore datasets in a top-down approach by specifying relevant and meaningful terms (assists in finding hidden data). An advantage is that the nodes and edges in a knowledge graph create built-in semantic documentation. Deeper spatiotemporal connections between data sources can be encoded via Graph Neural Networks (GNN) (Zhang et al., 2021). The KG approach can be extended to integrate the data itself in a Virtual KG (VKG). Our work will derive inspiration from large-scale VKG efforts that have been undertaken or are currently underway as part of the OpenStreetMap project (Ding et al., 2021). For DOE Data Days, we share the proposed geospatial data platform hybrid (cloud/on-prem) architecture, our work-to-date on storing, retrieving, and transforming LiDAR and raster data relevant to two important NREL use-cases, including the Renewable Energy Potential (reV) Model, and present our proposal for a KG based data discovery engine.

data platform↗
DOE OSTI2024

Beyond Ion Migration in Metal Halide Perovskites: Toward a Broader Photoelectrochemistry Perspective

Ion migration is a broad term used to account for the degradation of halide perovskite materials and devices. However, ion mobility is only one piece of the full picture-mobile ions/defects are first created, then transported, and eventually annihilated or immobilized. In this Perspective, we summarize emerging work that shows how tractable photochemical and Faradaic reactions provide a continuous source of ions to migrate. Furthermore, we discuss strategies to fundamentally manipulate ion migration by targeting specific electrochemical and reduction/oxidation mechanisms. This highlights the important role of defect photoelectrochemistry, as well as the soft nature of the perovskite lattice, in ion migration and self-healing. We conclude that distinguishing more detailed processes involved in “ion migration”, with an emerging focus on the reactions that form mobile ionic defects, is necessary to greatly improve the stability of devices and open up more technological applications.

defects↗
DOE OSTI2022

Particle emissions from mobile sources: Discussion of ultrafine particle emissions and definition

There is no universally agreed upon definition for ultrafine particles (UFP). Commonly used definitions for UFP are either particle number below 100 nm or total particle number, but without an agreed upon lower cut point. For example, a lower cut point of 3 nm compared to 10 nm could result in a substantially higher count. Another definition for UFP is total particle mass but without a commonly agreed upon aerodynamic diameter upper cut point, e.g., below 100 nm, 200 nm, 300 nm, etc. Yet another definition is lung deposited surface area weighted by lung deposition fraction, found mainly in the particle mobility diameter range from 20 to 400 nm. It is clear from these definitions that there are inconsistencies in the way UFP is used and defined in the literature. Sometimes these metrics are well correlated, sometimes not. In this paper we suggest three exposure metrics: UFP-N, UFP-M, and UFP-S, that we believe will add clarity. These metrics represent total number, mass, and surface area below 500 nm, respectively. For surface area and mass, the 500 nm cut point can be either aerodynamic or mobility diameter depending upon measurement methodology. For all metrics, this cut point captures nearly all of the primary particle emissions from mobile sources. Furthermore, UFP-N would include a lower cut point of 3–6 nm and would not require an upper size cut point because there is very little particle number above 500 nm or even above 100 nm. Thus, our definition of UFP-N is consistent with the current definition of ultrafine number except for, importantly, the specification of a lower cut point. These exposure metrics can help facilitate consistency in the characterization of both short- and long-term UFP ambient exposures and associated health effects in epidemiological studies.

42 ENGINEERING↗
DOE OSTI2022

A Framework to Integrate Human Reliability Data Obtained from Different Sources Based on the Complexity Scores of Proceduralized Tasks

For many decades, PSA (Probabilistic Safety Assessment) or PRA (Probabilistic Risk Assessment) techniques have been used to enhance the operational safety of nuclear power plants (NPPs) based on the consideration of potential hazards that could result in an unexpected consequence. As human error is one of the potential hazards, diverse human reliability analysis (HRA) methods have been proposed to provide a systematic way to estimate the likelihood of human errors (i.e., human error probability, HEP) in specific task contexts. Accordingly, it is evident that the quality of HRA results strongly depends on the credibility of HEP estimations. This implies that, in terms of enhancing this credibility, the collection of raw information (HRA data) that is helpful for understating when and why human errors occur is a crucial issue. In order to address this issue, in this study, the feasibility of a framework to integrate HRA data obtained from different sources is investigated based on the complexity of proceduralized tasks.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

MIXv2: a long-term mosaic emission inventory for Asia (2010–2017)

The MIXv2 Asian emission inventory is developed under the framework of the Model Inter-Comparison Study for Asia (MICS-Asia) Phase IV and produced from a mosaic of up-to-date regional emission inventories. We estimated the emissions for anthropogenic and biomass burning sources covering 23 countries and regions in East, Southeast and South Asia and aggregated emissions to a uniform spatial and temporal resolution for seven sectors: power, industry, residential, transportation, agriculture, open biomass burning and shipping. Compared to MIXv1, we extended the dataset to 2010–2017, included emissions of open biomass burning and shipping, and provided model-ready emissions of SAPRC99, SAPRC07, and CB05. A series of unit-based point source information was incorporated covering power plants in China and India. A consistent speciation framework for non-methane volatile organic compounds (NMVOCs) was applied to develop emissions by three chemical mechanisms. The total Asian emissions for anthropogenic/open biomass sectors in 2017 are estimated as follows: 41.6/1.1 Tg NO x , 33.2/0.1 Tg SO 2 , 258.2/20.6 Tg CO, 61.8/8.2 Tg NMVOC, 28.3/0.3 Tg NH 3 , 24.0/2.6 Tg PM 10 , 16.7/2.0 Tg PM 2.5 , 2.7/0.1 Tg BC (black carbon), 5.3/0.9 Tg OC (organic carbon), and 18.0/0.4 Pg CO 2 . The contributions of India and Southeast Asia were emerging in Asia during 2010–2017, especially for SO 2 , NH 3 and particulate matter. Gridded emissions at a spatial resolution of 0.1° with monthly variations are now publicly available. This updated long-term emission mosaic inventory is ready to facilitate air quality and climate model simulations, as well as policymaking and associated analyses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Basin-scale analysis of Mokelumne River Formation for multi-well CO 2 injection

Large-scale carbon sequestration will likely require multiple projects injecting CO 2 into the same subsurface formation, raising concerns about safe operation and efficient use of storage capacity. This study evaluates the long-term response of the Mokelumne River Formation in California’s Sacramento Basin to multi-megaton CO 2 injection using three geologic models of the formation and the open-source simulator GEOS. The analysis focuses on three aspects of reservoir performance: (1) average pressure increase and dissolved CO 2 mass after 30 years for varying well counts and injection rates, (2) pressure interference in a multi-well configuration, and (3) dynamic storage capacity with identification of overpressure-prone regions. The results show that average formation pressure increases linearly with injected mass, while CO 2 dissolution exhibits mixed scaling: approximately linear with the number of wells but sublinear with injection rate, indicating that distributing injection across more wells enhances dissolution more effectively than increasing per-well rates. Pressure-interference effects are significant, with lower-permeability conditions delaying their onset but amplifying their magnitude at later times. Dynamic capacity, defined by the first occurrence of pressure exceeding the local overburden-based limit anywhere in the formation, varies across geologic models and assumed overburden pressure gradients. A lower fidelity geologic model predicts nearly twice the storage capacity of the two higher fidelity models, which consistently estimate approximately 1 Gt under the upper-bound overburden pressure gradient considered for the Sacramento Basin. In all model scenarios, overpressure develops away from injection wells, particularly in higher-elevation regions, highlighting the importance of basin-scale modelling for identifying risks beyond the immediate well vicinity.

Basin-scale↗
DOE OSTI2023

BeyondPlanck: VII. Bayesian estimation of gain and absolute calibration for cosmic microwave background experiments

We present a Bayesian calibration algorithm for cosmic microwave background (CMB) observations as implemented within the global end-to-end BEYONDPLANCK framework and applied to the Planck Low Frequency Instrument (LFI) data. Following the most recent Planck analysis, we decomposed the full time-dependent gain into a sum of three nearly orthogonal components: one absolute calibration term, common to all detectors, one time-independent term that can vary between detectors, and one time-dependent component that was allowed to vary between one-hour pointing periods. Each term was then sampled conditionally on all other parameters in the global signal model through Gibbs sampling. The absolute calibration is sampled using only the orbital dipole as a reference source, while the two relative gain components were sampled using the full sky signal, including the orbital and Solar CMB dipoles, CMB fluctuations, and foreground contributions. We discuss various aspects of the data that influence gain estimation, including the dipole-polarization quadrupole degeneracy and processing masks. Comparing our solution to previous pipelines, we find good agreement in general, with relative deviations of -0.67% (-0.84%) for 30 GHz, 0.12% (-0.04%) for 44 GHz and -0.03% (-0.64%) for 70 GHz, compared to Planck PR4 and Planck 2018, respectively. We note that the BEYONDPLANCK calibration was performed globally, which results in better inter-frequency consistency than previous estimates. Additionally, WMAP observations were used actively in the BEYONDPLANCK analysis, which both breaks internal degeneracies in the Planck data set and results in an overall better agreement with WMAP. Finally, we used a Wiener filtering approach to smoothing the gain estimates. We show that this method avoids artifacts in the correlated noise maps as a result of oversmoothing the gain solution, which is difficult to avoid with methods like boxcar smoothing, as Wiener filtering by construction maintains a balance between data fidelity and prior knowledge. Although our presentation and algorithm are currently oriented toward LFI processing, the general procedure is fully generalizable to other experiments, as long as the Solar dipole signal is available to be used for calibration.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Sextupole reduction via chaos suppression at the National Synchrotron Light Source II

We revisit the nonlinear lattice design approach for the National Synchrotron Light Source II (NSLS-II) storage ring. By suppressing chaos, we identify alternative sextupole configurations to the original design, which relied on the conventional strategy of simultaneously minimizing resonance driving terms (RDTs) and amplitude-dependent detuning (ADD). These alternatives achieve comparable performance while requiring fewer sextupoles. A detailed comparison of two representative solutions is presented and supported by experimental validation. Our results show that the dynamic aperture correlates more strongly with global chaos than with individual RDTs, and that the importance of minimizing ADD may have been overstated in earlier design strategies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Nonresonant two-photon x-ray absorption in Cu

We present a real-space Green's function theory and calculations of two-photon x-ray absorption (TPA). Our focus is on nonresonant 𝐾-shell TPA in metallic Cu, which has been observed experimentally at intense x-ray free electron laser (XFEL) sources. The theory is based on an independent particle Green's function treatment of the Kramers-Heisenberg equation and an approximation for the sum over nonresonant intermediate states in terms of a static quadrupole transition operator. XFEL effects are modeled by a partially depleted 𝑑 band. This approach is shown to give results for 𝐾-shell TPA in quantitative agreement with XFEL experiment and with a Bethe-Salpeter equation approach. Furthermore, we also briefly discuss many-body corrections and TPA sum rules.

Approximation methods for many-body systems↗
DOE OSTI2020

OSTI Semantic Thesaurus v1

The OSTI Semantic Thesaurus is a reference for scientific and technical terms and relationships. The term data is used to implement keyword-to-concept mapping in OSTI.GOV searches, where searched term(s) will be mapped to related scientific concepts, allowing the end user to retrieve results related to their search terms and also explore narrower and similar concepts. The OSTI Semantic Thesaurus originally inherited data and structure from the INIS/ETDE Thesaurus (https://www.etde.org/edb/IAEA-INIS-ETDE-01-2013-08.pdf), which was a controlled terminology for indexing information within the subject scopes of the International Nuclear Information System (INIS) and the Energy Technology Data Exchange (ETDE). The data has been expanded upon since that time with the inclusion of scientific concepts from sources like Wikidata, and through manual curation. This dataset is an export from the OSTI Semantic Thesaurus in RDF/SKOS format, which is specifically suited for representation of controlled vocabularies like thesauri. While the full thesaurus includes additional relation types which may be included in future revisions, this export is limited to broader, narrower, and related term relations. Definitions and scope notes are included where available.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Extension of the PINN diffusion model to k-eigenvalue problems

This paper extends our recent work on the Physics-Informed Neural Networks (PINN) approach for the fixed source diffusion models and applies it to the diffusion theory based k-eigenvalue problems. To make the PINN equitable for the eigenvalue problems, we introduce a novel integral regularization term to the loss function in the framework, and allow the direct inference of the principal eigenvalue and the associated eigenfunction. The regularization term enforces a pre-defined value on the integration of the model predictions, and this value can be directly related to a physical property of the system. We also introduce an additional learnable parameter to approximate the principal eigenvalue. As a proof of principle, we solve the one-group two-dimensional k-eigenvalue neutron diffusion equation in this work. We then provide two numerical examples to demonstrate the applicability of the PINN approach. In each example, we solve the k-eigenvalue diffusion equation in a multi-region configuration constrained with a set of Robin boundary conditions for generality. We use a FEM solution based on the power-iteration method to verify the results of the PINN solution. The results showed relative percentage error in the predicted eigenvalue of about 0.77% and about 1.2% for example 1 and example 2, respectively. The mean absolute error in the predicted flux for example 1 is ∼ 0.002 and for example 2 is ∼ 0.0024. These results indicate some preliminary successes of the PINN application to k-eigenvalue problems. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

TEAMER: MADWEC Techno-Economic Analysis

The objective of this project was for the facility to conduct a techno-economic assessment (TEA) of the Maximal Asymmetric Drag Wave Energy Converter (MADWEC), developed by the University of Massachusetts Dartmouth (UMass Dartmouth). MADWEC is used for powering remote monitoring and Autonomous Underwater Vehicle (AUV) charging systems compared to other existing power supply options. The assessment estimates capital expenditures (CapEx), operational expenditures (OpEx), and power performance for 18 scenarios with the purpose of identifying key cost drivers, comparing total system cost, and comparing the power performance of the power supply options in terms of required installed capacity and estimated theoretical annual energy performance. The 18 assessed scenarios include two end-uses: 1) AUV charging and 2) offshore remote monitoring); three power sources: 1) MADWEC), 2) photovoltaic (PV) solar buoy, 3) and traditional battery swapping); and three locations; 1) nearshore, 2) far-offshore, and 3) high-latitude). In addition, other project goals included developing high level installation, operation, and maintenance plans for each scenario. The techno-economic model, created in Microsoft Excel, estimates CapEx, OpEx, and the power performance of each power supply source. The model has a dynamic format that allows custom inputs to accommodate future changes to the systems being assessed. This is a TEA for the MADWEC project, TEAMER RFTS 7 (request for technical support) program.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

End-to-end optimization for battery materials and molecules by combining graph neural networks and reinforcement learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to design new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗