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

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

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 identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale. This has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from 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. In this work, we implement ML-based geothermal exploration and an enhanced geothermal systems (EGS) design tool to achieve the above goals. Our exploration tool is GeoThermalCloud (GTC) EGS design tool is GeoDT-ML. GTC (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. It enables the identification of critical measurements needed to identify geothermal resource signatures. GeoDT-ML (github.com/SmartTensors/GeoThermalCloud.jl/tree/master/) adds coupling to GeoDT (https://github.com/GeoDesignTool/GeoDT.git) for stochastic EGS design optimization and performance prediction. GeoDT-ML leverages recent advances in deep learning and high-performance computing. Contributors to this effort include LANL, PNNL, Google, Stanford, and Julia Computing.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach

The escalating energy demand and the adverse environmental impacts of fossil-fuel use necessitate a shift towards cleaner and renewable alternatives. Concentrated Solar Power (CSP) technology emerges as a promising solution, offering a carbon-free alternative for power generation. The efficiency and profitability of CSP depend on the Direct Normal Irradiance (DNI) component of solar radiation; hence, accurate DNI forecasting can help optimize CSP plants’ operations and performance. The unpredictable nature of weather phenomena, particularly cloud cover, introduces uncertainty into DNI projections. Existing DNI forecasting models use meteorological factors, which are both challenging to estimate numerically over short prediction windows and expensive to model through data at a sufficiently high spatial and temporal resolution. This research addresses the challenge by presenting a novel approach that formulates DNI prediction as a multi-class classification problem, departing from conventional regression-based methods. The primary objective of this classification framework is to identify optimal periods aligning with specific operational thresholds for CSP plants, contributing to enhanced dispatch optimization strategies. We model the DNI classification problem using four advanced deep neural networks – rectified linear unit (ReLU) networks, 1D residual networks (ResNets), bidirectional long short-term memory (BiLSTM) networks, and transformers – achieving accuracies up to 93.5% without requiring meteorological parameters.

14 SOLAR ENERGY↗
DOE OSTI2025

Enhancement of Rydberg Blockade via Microwave Dressing

Experimental control over the strength and angular dependence of interactions between atoms is a key capability for advancing quantum technologies. Here, in this work, we use microwave dressing to manipulate and enhance Rydberg-Rydberg interactions in an atomic ensemble. By varying the cloud length relative to the blockade radius and measuring the statistics of the light retrieved from the ensemble, we demonstrate a clear enhancement of the interaction strength due to microwave dressing. These results are successfully captured by a theoretical model that accounts for the excitation dynamics, atomic density distribution, and phase-matched retrieval efficiency. Our approach offers a versatile platform for further engineering interactions by exploiting additional features of the microwave fields, such as polarization and detuning, opening pathways for new quantum control strategies.

collective effects in quantum optics↗
DOE OSTI2025

Geospatial Data Workflow Orchestration and Architecture

In an era characterized by explosive growth in geospatial data, the selection of appropriate technologies for data storage, processing, and orchestration is critical for organizations aiming to maintain competitive advantages. This white paper provides a comprehensive analysis of how Oak Ridge National Laboratory (ORNL) has effectively employed various cloud technologies, including containerized applications, container orchestrators, and workflow orchestrators, to develop robust geospatial data processing solutions. We explore the fundamental concepts behind these technologies and compare multiple deployment models tailored to diverse use cases. Our findings conclude that while Kubernetes has emerged as the preferred platform for truly scalable and fault-tolerant production workflows, the choice of workflow orchestration tool requires careful consideration of team needs, pipeline complexity, and deployment environments. This paper aims to serve as a strategic guide for organizations leveraging geospatial data, articulating the balance between technology choices and practical implementation to enhance workflow efficacy and scalability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Compiler-Driven FPGA Virtualization with SYNERGY

FPGAs are increasingly common in modern applications, and cloud providers now support on-demand FPGA acceleration in datacenters. Applications in datacenters run on virtual infrastructure, where consolidation, multi-tenancy, and workload migration enable economies of scale that are fundamental to the provider's business. However, a general strategy for virtualizing FPGAs has yet to emerge. While manufacturers struggle with hardware-based approaches, we propose a compiler/runtime-based solution called Synergy. We show a compiler transformation for Verilog programs that produces code able to yield control to software atsub-clock-tickgranularity according to the semantics of the original program. Synergy uses this property to efficiently support core virtualization primitives: suspend and resume, program migration, and spatial/temporal multiplexing, on hardware which is availabletoday.We use Synergy to virtualize FPGA workloads across a cluster of Intel SoCs and Xilinx FPGAs on Amazon F1. The workloads require no modification, run within 3--4x of unvirtualized performance, and incur a modest increase in FPGA fabric usage.

Computer Science↗
DOE OSTI2026

Spatially Accelerated Winding Numbers for Curved Geometry

The generalized winding number (GWN) is a scalar field that supports robust containment queries on curved geometry, including non-watertight, overlapping, and nested boundary representations. While queries can be easily parallelized over samples, direct evaluation on parametric curves and surfaces remains costly for large and complex models. Fast, state-of-the-art GWN approaches leverage a spatial index to approximate the GWN, typically coupled with a Taylor expansion which approximates the GWN contribution for far clusters of geometric primitives. However, such methods operate only on discrete inputs such as triangle meshes and point clouds, and would introduce containment errors near boundaries if applied to curved input. We extend support for fast GWN evaluation over arbitrary collections of NURBS curves in 2D and trimmed NURBS patches in 3D via a Bounding Volume Hierarchy that stores efficiently precomputed moment data in the hierarchy nodes. When querying the hierarchy, approximations for far clusters are used alongside direct evaluation for nearby NURBS primitives, achieving sub-linear complexity while preserving the geometric features in the vicinity of the query point. Central to our performance improvements is an adaptive subdivision strategy for NURBS primitives during a preprocessing phase, creating better spatial partitions while retaining the same accuracy for containment decisions as a direct evaluation. We demonstrate the performance and accuracy of our approach across a large collection of 2D and 3D datasets.

Computer science↗
DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Custom surface reflectance, shade mask, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study (2025)

This dataset contains land surface reflectance estimates and additional derived products generated from NEON Imaging Spectrometer (NIS) data collected in the Upper Gunnison river basin during June and July of 2025. Data was collected over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). These products were derived from radiance and LiDAR data collected by the NEON Airborne Observation Platform (AOP) campaign funded by the Colorado Headwaters Ecological Spectroscopy Study (CHESS) (doi:10.15485/3017965). Products include per-pixel surface reflectance (rfl) and reflectance uncertainty (rfl_unc), observational data (obs), canopy equivalent water thickness (ewt), and shade masks. Atmospheric correction was performed per flightline using the ISOFIT (Imaging Spectrometer Optimal FITting) optimal estimation framework to estimate surface reflectance and the associated per-band reflectance uncertainty. Reflectance retrievals achieved a mean absolute error of 1.5% across diverse validation surfaces (see validation report.pdf). Equivalent water thickness was calculated from surface reflectance using the Beer–Lambert absorption of liquid water. Shade masks were generated based on the geometry between the sun angle, ground surface, and sensor at the time of flight. Data products are provided per-flightline and as mosaics for each domain. Flightline data products are provided as ENVI-formatted binary files (rfl, rfl_unc, ewt) and GeoTIFFs (shade). Reflectance and uncertainty mosaics are provided as tiled NetCDFs, while all other mosaicked products are provided as cloud-optimized GeoTIFFs. These formats are supported by common geospatial software (e.g., QGIS, ArcGIS, ENVI) and programmatic libraries in Python (e.g., rasterio, xarray, spectral, netCDF4) and R (e.g., terra, ncdf4). Processing workflows were designed to be equivalent to those used to generate the 2018 CHESS campaign airborne imaging spectroscopy data products (doi:10.15485/3013527). All outputs were co-registered to a common spatial grid to support time series analyses. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: Data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). Computational research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004) and was funded by EMIT Extended Mission Phase E Science.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2025

Exploring the interaction between the MW and LMC with a large sample of blue horizontal branch stars from the DESI survey

The Large Magellanic Cloud (LMC) is a Milky Way (MW) satellite that is massive enough to gravitationally attract the MW disc and inner halo, causing significant motion of the inner MW with respect to the outer halo. In this work, we probe this interaction by constructing a sample of 9866 blue horizontal branch (BHB) stars with radial velocities from the DESI spectroscopic survey out to 120 kpc from the Galactic centre. This is the largest spectroscopic set of BHB stars in the literature to date, and it contains four times more stars with Galactocentric distances beyond 50 kpc than previous BHB catalogues. Using the DESI BHB sample combined with SDSS BHBs, we measure the bulk radial velocity of stars in the outer halo and observe that the velocity in the Southern Galactic hemisphere is different by 3.7σ from the North. Modelling the projected velocity field shows that its dipole component is directed at a point 22 deg away from the LMC along its orbit, which we interpret as the travel direction of the inner MW. The velocity field includes a monopole term that is –24 km s –1 ⁠, which we refer to as compression velocity. This velocity is significantly larger than predicted by the current models of the MW and LMC interaction. This work uses DESI data from its first 2 yr of observations, but we expect that with upcoming DESI data releases, the sample of BHB stars will increase and our ability to measure the MW–LMC interaction will improve significantly.

Galaxy: evolution↗
DOE OSTI2024

Assessment and Coordination of EVSE Cybersecurity Standards

Cybersecurity certification programs for Electric Vehicle Supply Equipment (EVSE) are fragmented due to no single certification covering all aspects of the device and additionally the existence of multiple programs and under different levels of regulation. These devices are also confronted by the intricate assembly of product software, firmware, and hardware. Devices contain both logical and physical interfaces. These multifaceted devices have vulnerabilities at many levels and interconnect with other potentially vulnerable systems including the electric vehicle, the cloud where data and payment information are stored, and the electric grid and electric grid equipment including utilities. Of the EVSE certification programs that are found, none are directly for the cybersecurity of EVSE. Many standards are for safety, specifically battery safety, some are cybersecurity standards for other types of equipment and can be modeled for EVSE. In specific, ISA/IEC 62443 is found to be significantly in line with EVSE security needs and will be used in future testing to certify EVSE and help guide the project to demonstrate where gaps exist, where strengths lie in the standard and how this can be used to lead the certification efforts in harmonizing EVSE cybersecurity standards. In addition, there are multiple efforts that are currently seeking to build EVSE standards or revise existing standards to address gaps. This effort is seeking to establish a cybersecurity program for EVSE that will inform customers and help increase the level of security across products and state EVSE procurements to achieve consistency across different jurisdictions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

DOE Repository Metadata Profile (DRMP): A Metadata Framework for Advancing Interoperability and AI Readiness Across Scientific Repositories

The Department of Energy (DOE) funds a diverse and distributed ecosystem of repositories that steward scientific data, publications, and software across its research programs, user facilities, and national laboratories. While significant progress has been made in standardizing dataset-level metadata, the metadata describing repositories themselves (their identity, governance, access interfaces, policies, and technical capabilities) remains inconsistent and fragmented across DOE-funded systems. This variability limits discoverability, interoperability, automated validation, and AI-driven analysis, all of which are increasingly essential for modern scientific workflows. To address this gap, the DOE Data Curation Working Group (DCWG) developed the DOE Repository Metadata Profile (DRMP). The DRMP is a practical, community-driven framework that defines how repositories can describe themselves in a consistent, machine-actionable, and scalable manner. The DRMP is not a new metadata schema. Instead, it is a mapping profile and structured element set capturing the essential characteristics of DOE repositories. It harmonizes repository-level metadata across six widely adopted community schemas: RE3Data; DCAT-US v3; Schema.org; Dublin Core; DataCite 4.6; and PREMIS 3.0. This harmonization eliminates reinvention and enables interoperability within DOE and across the broader scientific ecosystem. A core objective of the DRMP is to reduce burden on repositories by allowing them to reuse their existing metadata through a Rosetta-style crosswalk rather than redesigning local implementations. The profile introduces a three-level conformance model that supports incremental adoption: • Level 1 – Minimum Viable Record (MVR): foundational identification elements required for workflows, project registration, and basic repository presence. • Level 2 – Interoperable: structured metadata enabling alignment with national and international discovery systems. • Level 3 – AI-Ready: enhanced provenance, policy transparency, fixity, semantic context, and capabilities that support automated reasoning, model training governance, and machine-assisted curation. To support implementation, the DRMP includes JSON Schema definitions, OpenAPI patterns, and MCP templates that allow repositories to publish machine-readable metadata directly within existing platforms. These resources are modular and lightweight, enabling adoption without major architectural change. Adopting the DRMP enables repositories to: • Enhance discoverability and interoperability by aligning identifiers, classifications, and descriptive elements across widely used schema standards. • Support federated discovery and cross-registration across DOE systems, Data.gov, and international catalogs. • Enable AI agents and workflow orchestration systems to interpret repository-level metadata within the American Science Cloud (AmSC) through Model Context Protocol (MCP)-based context publication. • Demonstrate alignment with DOE’s open science, stewardship, and FAIR data priorities. This guidance represents a community-driven step forward. Through voluntary adoption and continued feedback, the DRMP advances a cohesive, machine-actionable description of DOE repositories that supports FAIR data practices, preparing the infrastructure for AI-enabled research, and strengthening the discoverability and reuse of DOE’s scientific outputs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Reducing Data Center Peak Cooling Demand and Energy Costs with Underground Thermal Energy Storage (UTES)

By recent estimates, data center energy demands are projected to consume between 6.7% and 12% of U.S. annual electricity generation by the year 2028, driven primarily by expanded demands from cloud services, big data analytics, and Artificial Intelligence (AI) (Shehabi et al., 2024). As much as 40% of data center total energy consumption are loads associated with the site infrastructure cooling systems, and these are often highly water consumptive (Aljbour et al., 2024). For energy system planners, this presents significant challenges to meeting and managing the anticipated loads, and especially the peak loads of projected data center deployments. Geothermal technologies offer two unique solutions to these challenges: 1) by serving loads through the deployment of new conventional and/or next-generation geothermal power technologies such as EGS and 2) through an often-overlooked opportunity to reduce data center peak cooling loads. The latter is the focus of this paper which explores Cold Underground Thermal Energy Storage ("Cold UTES") as an emerging industrial-scale geothermal cooling solution. This cooling solution is energy efficient, non-water-consumptive, and utilizes long duration energy storage (LDES) on both diurnal and seasonal time scales. Cold UTES has the potential to also function as a virtual power plant (VPP). The US Department of Energy's Geothermal Technologies Office is supporting R&D to understand the grid and system-wide value, costs, and impacts of deploying this emergent cooling solution at scale.

AI↗
DOE OSTI2026

Throughput Estimation of Data Transport Networks From Digital Twin Measurements

Digital twins of networked infrastructures, known as Virtual Infrastructure Twins (VITs), are increasingly used for software development, pre-deployment testing, and design space exploration. While VITs avoid the costs and potential disruptions associated with experiments on operational networks, their throughput measurements are typically not sufficiently accurate for performance profiling of wide-area networks that they emulate. Here, machine learning (ML) methods are developed to transform these inaccurate VIT network throughput measurements to closely match in peak and overall profile of those from a physical testbed or production network. First, a micro kernel network reflecting a physical network is utilized to collect one-time measurements on a host to support this ML transformation. Then, a generic multi-modal ML method is developed to learn a map that transforms measurements from subsequent VITs on the same host to match past, current and follow-on testbed and cloud networks. ML generalization equations are derived to establish its correctness and probabilistically guarantee its generalization accuracy. Experimental results are presented for a variety of VIT hosts with target testbed and cloud networks; they include a case study of a four-site science ecosystem wherein inaccurate convex VIT measurement profiles are transformed into accurate concave profiles of target networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Zero Trust Strategies for Chemical, Biological, Radiological, and Nuclear Detection Systems: D.1 Cyber Scenarios

The evolving landscape of cybersecurity necessitates a paradigm shift to a Zero Trust (ZT) model, which assumes breaches and continuously verifies trust. This approach reshapes how trust boundaries are established, focusing on identities, devices, networks, applications, and data, rather than solely relying on perimeter defenses such as firewalls. Central to this transformation is the National Institute of Standards and Technology's (NIST) Special Publication 800-207, outlining the Zero Trust Architecture (ZTA), along with Executive Order 14028, which mandates federal agencies to adopt ZT principles. Complementary to these efforts, the Cybersecurity and Infrastructure Security Agency (CISA) developed the Zero Trust Maturity Model (ZTMM), providing a framework with five pillars and three cross-cutting capabilities to guide agencies toward enhanced cybersecurity maturity. In support of these initiatives, the DHS Countering Weapons of Mass Destruction Office (CWMD) is applying ZT principles to secure Chemical, Biological, Radiological, and Nuclear (CBRN) detection systems. Recognizing the diverse deployment models and network connectivity of these systems—from stationary, non-networked units to mobile, cloud-connected devices—the Pacific Northwest National Laboratory (PNNL) is developing cybersecurity scenarios specifically for CBRN environments. These scenarios examine various configurations and technological capabilities, offering insights into the application of ZTMM pillars in enhancing the security postures of CBRN devices. The cybersecurity scenarios presented by PNNL are hypothetical, crafted to explore theoretical situations and stimulate discussion on the potential use or compromise of CBRN detection systems in varied contexts. These narratives are illustrative and do not reference any real events or actual networks. Instead, they employ generalized reference models to highlight concepts and potential issues within CBRN security, focusing on how Zero Trust strategies can be adapted to address these challenges effectively.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2026

SPARTAN and IMPROVE Comparison Experiment (SPICE) Interim Campaign Report

SPICE (the SPARTAN and IMPROVE Comparison Experiment) aims to obtain and quantify comparisons between aerosol PM 2.5 mass concentration measurements from the University of Oklahoma (OU) Surface Particulate Matter Network (SPARTAN) station and the U.S. Environmental Protection Agency (EPA) Interagency Monitoring of Protected Visual Environments (IMPROVE) station hosted by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility at ARM’s Southern Great Plains (SGP) observatory in Oklahoma. Aerosols—both natural and anthropogenic—affect human populations in multiple ways. Much of ARM’s research focuses on how aerosols influence weather and climate through their optical and radiative effects, as well as their impacts on clouds and precipitation. However, aerosols also pose direct risks to humans and other organisms through inhalation, with the severity of health impacts depending on particle size, chemical composition, and duration of exposure. The IMPROVE network was established by the U.S. EPA to monitor air quality, including visible clarity as well as total and chemically speciated aerosol mass concentrations. The ARM SGP site hosts the IMPROVE SOGP station. Separately, the SPARTAN network operates a globally distributed set of stations similar to IMPROVE but with an emphasis on remote deployment and semi-autonomous operation for use beyond the borders of the United States (IMPROVE only operates within the U.S.). The University of Oklahoma operates a SPARTAN station. To establish confidence in the OU SPARTAN instrumentation and measurement protocol relative to the EPA-certified IMPROVE station, the OU SPARTAN station is currently deployed at SGP in close proximity to the IMPROVE SOGP station. The SPICE campaign was envisioned as a contiguous calendar-year effort for 2025 to capture seasonal variation in mass loading as well as composition. However, independent of the SPICE campaign, the SPARTAN network adopted a new filter construction part-way through the year, interrupting our contiguous data set. Thus, to obtain a contiguous data set with a uniform consistent configuration, SPICE desires an extension through 2026.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Assessment of Extinction‐, Satellite‐, and Model‐Based Vertical Cloud Condensation Nuclei (CCN) Retrieval Methods Using Airborne CCN Measurements Over the Southern Great Plains

Abstract Accurate estimates of the vertical profile of cloud condensation nuclei (CCN) concentration are crucial to better quantify aerosol‐cloud interactions. We assessed the correlation between the vertical CCN concentrations obtained from extinction‐, satellite‐, and model‐based retrieval methods and airborne CCN concentrations collected at 0.24% supersaturation within the 3, 9, 27, and 81 km regions centered over the U.S. Department of Energy's Atmospheric Radiation Measurement User Facility Southern Great Plains (SGP) site during the spring and summer of 2016. The extinction profiles at a wavelength 355 nm were provided by the ground‐based Raman lidar. Our analysis showed moderate correlation between dry‐corrected extinction and airborne CCN data. We found the retrieved number concentration of CCN (RNCCN) method showed regression best‐fit slopes close to unity and consistent prediction errors for the majority of the data. The Lenhardt et al. (2023, https://doi.org/10.5194/amt‐16‐2037‐2023 ) method showed similar conclusions but only during spring, whereas the Mamouri and Ansmann (2016, https://doi.org/10.5194/acp‐16‐5905‐2016 ) method showed poor correlation. The Shinozuka et al. (2015, https://doi.org/10.5194/acp‐15‐7585‐2015 ) satellite‐based method exhibited reasonable agreement during summer but poor correlation during periods where both high (∼1,400 #/cm 3 ) and low (∼50 #/cm 3 ) airborne CCN concentrations were observed. The Copernicus Atmosphere Monitoring Service reanalysis modeled 3‐D CCN data set showed a moderate to weak positive correlation but performed poorly at high airborne CCN concentrations. Our analysis suggests the extinction‐based RNCCN method performed better than other methods across most observation periods under the diverse meteorological conditions observed at the SGP site.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A point-particle-based hydride shell-shedding model for ejecta particle transport in reactive environments

A shock wave passing over a rough or perturbed metal surface will induce a limiting case of Richtmyer–Meshkov instability and will cause small particles to eject from the surface and transport into the surrounding medium. These particles are known as ejecta and can be either solid or liquid in nature. Recent experiments have shown that liquid cerium ejecta clouds exhibit unexpected non-monotonic acceleration behaviors as well as temperature plateaus after a brief temperature rise if they are transporting in a chemically reactive, hydrogen-based medium while they act as expected in an inert medium. This work details a point-particle model developed for reactive cerium ejecta transport, which attempts to account for these new physics through the behavior of a developing solid hydride shell, which is believed to form as a product of the reaction. The overall model incorporates the effects of the reaction on the particle properties as well as the effects of potential shedding of the shell into sub-micrometer scale flakes and potential phase change of the hydride if the ejecta particles reach the melt point of the hydride layer. The model is tested by performing simulations of the original motivating experiments and comparing quantities, such as ejected mass, velocimetry, and temperature profiles, against the experimental data. While the model is able to capture many general features of the observed anomalies, some inaccuracies still exist. These point to both missing physics in the model (such as a deuterium adsorption mechanism on the hydride layer) as well as a lack of knowledge of certain material properties (such as the strength of cerium hydride to determine dynamic fracture thicknesses) needed to fully reduce the uncertainties in the model by up to an order of magnitude and perform a true attempt at model validation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Expanding Access to Science Participation: A FAIR Framework for Petascale Data Visualization and Analytics

The massive data generated by scientists daily serve as both a major catalyst for new discoveries and innovations, as well as a significant roadblock that restricts access to the data. Here, our paper introduces a new approach to removing Big Data barriers and democratizing access to petascale data for the broader scientific community. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hiding the complexities of dealing with file systems or cloud services. We enable FAIR (Findable, Accessible, Interoperable, and Reusable) access to datasets such as NASA’s petascale climate datasets. Our paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction utilizes state-of-the art progressive compression algorithms and machine-learning insights to power scalable visualization dashboards for petascale data. The result provides users with the ability to identify extreme events or trends dynamically, expanding access to scientific data and further enabling discoveries. We validate our approach by improving the ability of climate scientists to visually explore their data via three fully interactive dashboards. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution. These dashboards, released in simplified form to the general public, contribute significantly to a broader push to democratize the access and use of climate data.

Computer science↗