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DOE OSTI2025

New NDA Methods for Thorium Fuel Cycle Safeguards (Final Report)

This project developed portable Neutron Resonance Transmission Analysis (pNRTA) as a new non-destructive assay (NDA) method for thorium fuel cycles safeguards and other applications where multiple isotopes must be measured when present together. pNRTA leverages epithermal neutron resonances to assay multiple safeguards-relevant isotopes (e.g., 233 U and 235 U) when they are present together in a sample. Existing techniques are challenged by this task, driving the need for new active interrogation methods. With selected detectors, pNRTA works in high gamma-ray backgrounds from fission and activation products and 232 U progeny expected in thorium fuel cycle samples. This project leveraged a pNRTA system developed at Pacific Northwest National Laboratory (PNNL) and collaboration with the Massachusetts Institute of Technology (MIT). The system uses a commercially available deuterium-tritium (DT) neutron generator at short standoff (2 m). Key achievements in this project included: first-of-a-kind pNRTA quantitative measurements of 233 U oxide samples, an assessment of neutron detector technologies suitable for pNRTA in high gamma-ray background environments, experimentally demonstrating quantitative assay of samples containing 233 U and 235 U, and modeling studies showing the applicability of pNRTA to a wide range of material forms. Further, a custom algorithm was developed at MIT, which provided mean bias of 9% and relative standard deviation of 36% in assaying 233 U, 235 U, 238 U, and 232 Th content in eight measured samples. These outcomes form a solid technical basis for pNRTA as a new promising capability for international safeguards verification that is portable, non-destructive, quantitative, and isotopic specific.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

NeuroSymbolic Approaches as a Vector for Assured Artificial Intelligence

The deployment of artificial intelligence systems in critical applications requires higher levels of assurance for safety, security, and interpretability. While neurosymbolic (NESY) approaches combining neural networks with symbolic reasoning offer potential advantages for assured AI, existing differentiable neurosymbolic frameworks face significant limitations including computational overhead and performance constraints. This report investigates the ISED (InferSampleEstimateDescend) framework as an alternative approach that enables neurosymbolic learning without requiring endtoend differentiability. We evaluate ISED’s utility for geointelligence applications by comparing neurosymbolic models against standard neural networks on aircraft classification tasks using the RarePlanes and MTARSI imagery datasets. Our results demonstrate that while ISEDbased models achieve slightly lower accuracy (89.7% vs 92.1% on RarePlanes; 91.1% vs 92.5% on MTARSI), they provide critical explainability capabilities that enable tracing incorrect predictions back to specific attribute misclassifications. We also present an automated pipeline that generates both attributeclass mappings and neurosymbolic model architectures from natural language descriptions, significantly reducing the manual effort required for NESY model deployment. These findings suggest that ISED offers a promising direction for developing assured AI systems where interpretability and reasoning transparency are prioritized alongside performance.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Risks to the DOW Mission From Global Advanced Energy Adoption

Advanced energy technologies have the potential to enhance energy security and resilience; however, they can introduce new vulnerabilities even as they mitigate existing ones. This dichotomy highlights that both action and inaction carry strategic risks in a contested and logistically complex operating environment. Regardless of U.S. civilian or military adoption of such technologies, key allies and adversaries are on adoption paths that will impact the U.S. military at the tactical, operational, and strategic level. The global adoption of advanced energy technologies presents the potential for both positive and negative consequences for U.S. Department of Defense (DOD) missions in the next 10-20 years. Three categories of risk drivers are presented in this analysis: infrastructure-related, adoption-related, and response-related. Each risk type can generate consequences for DOD, including power disruptions, suboptimal DOD mission performance and operational effectiveness, higher costs and shortages for both legacy and advanced energy technologies, and loss of U.S. influence and strategic deterrent value. Specific impacts could include tactical impacts, operational impacts, and strategic impacts. Mitigation strategies could include energy resource and technology planning, cybersecurity, supply chain resilience, workforce development, exercises and simulations, investments in commercialized technology, operational testing and pilot programs, research into emerging technologies, partnerships and working groups, common standards, budget planning, and repurposing infrastructure.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

The Role of Strong Magnetic Fields in Stabilizing Highly Luminous Thin Disks

Abstract We present and analyze a set of three-dimensional, global, general relativistic radiation magnetohydrodynamic simulations of thin, radiation-pressure-dominated accretion disks surrounding a nonrotating, stellar-mass black hole. The simulations are initialized using the Shakura–Sunyaev model with a mass accretion rate of M ̇ = 3 L Edd / c 2 (corresponding to L = 0.17 L Edd ). Our previous work demonstrated that such disks are thermally unstable when accretion is driven by an α -viscosity. In the present work, we test the hypothesis that strong magnetic fields can both drive accretion through magnetorotational instability and restore stability to such disks. We test four initial magnetic field configurations: (1) a zero-net-flux case with a single, radially extended set of magnetic field loops (dipole), (2) a zero-net-flux case with two radially extended sets of magnetic field loops of opposite polarity stacked vertically (quadrupole), (3) a zero-net-flux case with multiple radially concentric rings of alternating polarity (multiloop), and (4) a net-flux, vertical magnetic field configuration (vertical). In all cases, the fields are initially weak, with a gas-to-magnetic pressure ratio ≳100. Based on the results of these simulations, we find that the dipole and multiloop configurations remain thermally unstable like their α -viscosity counterpart, in our case collapsing vertically on the local thermal timescale and never fully recovering. The vertical case, on the other hand, stabilizes and remains so for the duration of our tests (many thermal timescales). The quadrupole case is intermediate, showing signs of both stability and instability. The key stabilizing factor is the ability of specific field configurations to build up and sustain strong, P mag ≳ 0.5 P tot , toroidal fields near the midplane of the disk. We discuss the reasons why certain configurations are able to do this effectively and others are not. We then compare our stable simulations to the standard Shakura–Sunyaev disk.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds

The extrapolation of wind speeds measured at a meteorological mast to wind turbine rotor heights is a key component in a bankable wind farm energy assessment and a significant source of uncertainty. Industry-standard methods for extrapolation include the power-law and logarithmic profiles. The emergence of machine-learning applications in wind energy has led to several studies demonstrating substantial improvements in vertical extrapolation accuracy in machine-learning methods over these conventional power-law and logarithmic profile methods. In all cases, these studies assess relative model performance at a measurement site where, critically, the machine-learning algorithm requires knowledge of the rotor-height wind speeds in order to train the model. This prior knowledge provides fundamental advantages to the site-specific machine-learning model over the power-law and log profiles, which, by contrast, are not highly tuned to rotor-height measurements but rather can generalize to any site. Furthermore, there is no practical benefit in applying a machine-learning model at a site where winds at the heights relevant for wind energy production are known; rather, its performance at nearby locations (i.e., across a wind farm site) without rotor-height measurements is of most practical interest. To more fairly and practically compare machine-learning-based extrapolation to standard approaches, we implemented a round-robin extrapolation model comparison, in which a random-forest machine-learning model is trained and evaluated at different sites and then compared against the power-law and logarithmic profiles. We consider 20 months of lidar and sonic anemometer data collected at four sites between 50 and 100 km apart in the central United States. We find that the random forest outperforms the standard extrapolation approaches, especially when incorporating surface measurements as inputs to include the influence of atmospheric stability. When compared at a single site (the traditional comparison approach), the machine-learning improvement in mean absolute error was 28 % and 23 % over the power-law and logarithmic profiles, respectively. Using the round-robin approach proposed here, this improvement drops to 20 % and 14 %, respectively. These latter values better represent practical model performance, and we conclude that round-robin validation should be the standard for machine-learning-based wind speed extrapolation methods.

17 WIND ENERGY↗

DOE OSTI2023

Projections of Hourly Meteorology by County Based on the IM3/HyperFACETS Thermodynamic Global Warming (TGW) Simulations

This dataset contains 40 years (1980-2019) of historical hourly meteorology and 80 years (2020-2099) of projected hourly meteorology for each county in the conterminous United States. Details about the scenarios and variables included in this dataset are in the readme.pdf file. This dataset is derived from the IM3/HyperFACETS Thermodynamic Global Warming (TGW) simulations (https://doi.org/10.57931/1885756). More details on the TGW approach can be found at: https://tgw-data.msdlive.org/. If you use this dataset please also cite the raw TGW dataset (Jones, A. D., Rastogi, D., Vahmani, P., Stansfield, A., Reed, K., Thurber, T., Ullrich, P., & Rice, J. S. (2022). IM3/HyperFACETS Thermodynamic Global Warming (TGW) Simulation Datasets (v1.0.0) [Data set]. MSD-LIVE Data Repository. https://doi.org/10.57931/1885756). The four future climate scenarios in the TGW data are: rcp45cooler, rcp45hotter, rcp85cooler, and rcp85hotter. For the historical period and each of the four future scenarios the dataset has hourly estimates of the spatial-average of six meteorological variables for each county: Temperature, specific humidity, shortwave radiation, longwave radiation, and the U (east-west) and V (north-south) components of the wind speed. Times for each file are in the filename and all times are in Coordinated Universal Time (UTC). Counties are identified by their Federal Information Processing Standard (FIPS) code. The mapping between counties and FIPS codes is provided in the state_and_county_fips_codes.csv file. The code to go from the raw TGW data to county-level projections is available at: https://github.com/IMMM-SFA/im3components/tree/main/im3components/wrf_to_tell.

County↗

DOE OSTI2022

Generating and Analyzing Program Call Graphs using Ontology

Call graph or caller-callee relationships have been used for various kinds of static program analysis, performance analysis and profiling, and for program safety or security analysis such as detecting anomalies of program execution or code injection attacks. However, different tools generate call graphs in different formats, which prevents efficient reuse of call graph results. In this paper, we present an approach of using ontology and resource description framework (RDF) to create knowledge graphs for specifying call graphs to facilitate the construction of full-fledged and complex call graphs of computer programs, realizing more interoperable and scalable program analyses than conventional approaches. We create a formal ontology-based specification of call graph information to capture concepts and properties of both static and dynamic call graphs so different tools can collaboratively contribute to more comprehensive analysis results. Our experiments show that ontology enables merging of call graphs generated from different tools and flexible queries using a standard query interface. Index Terms—Callgraph, ontology, knowl

Dorta, E.↗

DOE OSTI2025

The Power of Many: An Ensemble Approach to Spectral Similarity

Quantifying the similarity between two mass spectra─a known reference mass spectrum and an unidentified sample mass spectrum─is at the heart of compound identification workflows in gas chromatography–mass spectrometry (GC-MS). The reference spectrum most like the sample is assigned as its identification (provided some quantitative similarity threshold is met, e.g., 80%) and thus accurately measuring similarity is essential. Significant research has gone toward developing metrics for this purpose, each of which has attempted to improve upon existing methods by incorporating GC-MS-specific information (e.g., peak ratios or retention times) or adopting various statistical and algorithmic frameworks. While this active development has led to a plethora of similarity metrics with demonstrated value across different contexts, the unfortunate consequence has been confusion surrounding which metric should be used as a global standard. No such metric is currently accepted as the standard method because different metrics have demonstrated optimal performance in different contexts. In this work, we propose an ensemble approach to spectral similarity scoring that combines the collective information from across existing similarity metrics to form an improved, globally representative similarity metric as a step toward establishing a global standard method. In conclusion, the resulting ensemble metrics are evaluated on over 88,000 spectra of varying complexity and demonstrate improved abilities to accurately rank the correct reference spectrum as the top-matching candidate for a sample relative to the rankings generated by individual similarity scores.

Carbohydrates↗

DOE OSTI2025

Polarization-type potential-induced degradation in bifacial PERC modules in the field

This study examines the susceptibility of bifacial glass/glass passivated emitter and rear cell (PERC) modules to potential-induced degradation-polarization (PID-p) in the field. While there are several studies showing PID-p occurring on both front and back faces of bifacial PERC in accelerated tests, we address the yet unclarified behavior in fielded modules. We examine the effects of mounting configuration; specifically, comparing modules mounted near ground and in elevated ground rack configurations. Modules with the cell circuit in -1500 V system voltage configuration, whether mounted on racks about 30 cm above the ground or elevated 2 m high showed mean degradation of 4.5% to 6% in power under standard test conditions over about 2.5 weeks as measured from the front side of the module. This extent of degradation remained sustained for a duration of about 6 months analyzed. Average daytime temperatures of modules in the various mounting configurations were similar and therefore judged to be insufficient to be a primary influence for the modest PID-p rate differences that we observed among mounting configurations. Increased leakage current in the morning suggests morning dew was sustained longer on modules near the ground measured over six months which would be expected to increase the PID-p rate over the long term. However, the main difference seen between the modules on the various mountings during the initial period with up to 6% mean degradation by PID-p was the approximately two times the irradiance from albedo on the rear of modules mounted in elevated ground rack compared to those on the near ground rack. This difference in incident albedo led to a modestly reduced rate of the development of PID-p of the modules on the elevated ground rack. The difference is attributed to the dissipation of PID-p-causing electrical charge by the albedo incident on the module rear. The behavior could be modeled by a sigmoidal equation with consideration of the differences in the insolation on the module rear.

14 SOLAR ENERGY↗

DOE OSTI2024

Search for pair production of higgsinos in events with two Higgs bosons and missing transverse momentum in $\sqrt{𝑠}$ = 13 TeV 𝑝⁢𝑝 collisions at the ATLAS experiment

This paper presents a search for pair production of higgsinos, the supersymmetric partners of the Higgs bosons, in scenarios with gauge-mediated supersymmetry breaking. Each higgsino is assumed to decay into a Higgs boson and a nearly massless gravitino. The search targets events where each Higgs boson decays into $b\bar{b}$, leading to a reconstructed final state with at least three energetic 𝑏-jets and missing transverse momentum. Two complementary analysis channels are used, with each channel specifically targeting either low or high values of the higgsino mass. The low-mass (high-mass) channel exploits 126 (139) fb −1 of $\sqrt{𝑠}$ = 13 TeV data collected by the ATLAS detector during Run 2 of the Large Hadron Collider. No significant excess above the Standard Model prediction is found. At 95% confidence level, masses between 130 GeV and 940 GeV are excluded for higgsinos decaying exclusively into Higgs bosons and gravitinos. Exclusion limits as a function of the higgsino decay branching ratio to a Higgs boson are also reported.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

(U)SAXS characterization of porous microstructure of chert: insights into organic matter preservation

This study characterizes the microstructure and mineralogy of 132 (ODP sample), 1000 and 1880 million-year-old chert samples. By using ultra-small-angle X-ray scattering (USAXS), wide-angle X-ray scattering and other techniques, the preservation of organic matter (OM) in these samples is studied. The scarce microstructural data reported on chert contrast with many studies addressing porosity evolution in other sedimentary rocks. The aim of this work is to solve the distribution of OM and silica in chert by characterizing samples before and after combustion to pinpoint the OM distribution inside the porous silica matrix. The samples are predominantly composed of alpha quartz and show increasing crystallite sizes up to 33 ± 5 nm (1σ standard deviation or SD). In older samples, low water abundances (~0.03%) suggest progressive dehydration. (U)SAXS data reveal a porous matrix that evolves over geological time, including, from younger to older samples, (1) a decreasing pore volume down to 1%, (2) greater pore sizes hosting OM, (3) decreasing specific surface area values from younger (9.3 ± 0.1 m 2 g -1 ) to older samples (0.63 ± 0.07 m 2 g -1 , 1σ SD) and (4) a lower background intensity correlated to decreasing hydrogen abundances. The pore-volume distributions (PVDs) show that pores ranging from 4 to 100 nm accumulate the greater volume fraction of OM. Raman data show aromatic organic clusters up to 20 nm in older samples. Raman and PVD data suggest that OM is located mostly in mesopores. Observed structural changes, silica–OM interactions and the hydrophobicity of the OM could explain the OM preservation in chert.

(U)SAXS↗

DOE OSTI2023

Protect Your Family: Reduce Contamination at Home: A Summary of a Study Conducted by the National Institute for Occupational Safety and Health

Congress enacted the Health Insurance Portability and Accountability Act of 1996 (HIPAA) without including specific provisions for protecting the privacy of healthrelated information. The law gave Congress three years to enact such requirements and provided that if it failed to do so the Secretary of the Department of Health and Human Services (DHHS) was obligated to promulgate regulations to implement the statutory standards. When Congress failed to meet the deadline, the obligation to issue privacy regulations passed to DHHS. DHHS received over 52,000 comments on the proposed regulations. Responses to the comments and the final rule were published in the Federal Register on December 28, 2000. The cost of implementing the regulations over a ten-year period was estimated at $17.6 billion by DHHS. Industry estimates the cost at five times that amount.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Multimodal Deep Learning for Flaw Detection in Software Programs

We explore the use of multiple deep learning models for detecting flaws in software programs. Current, standard approaches for flaw detection rely on a single representation of a software program (e.g., source code or a program binary). We illustrate that, by using techniques from multimodal deep learning, we can simultaneously leverage multiple representations of software programs to improve flaw detection over single representation analyses. Specifically, we adapt three deep learning models from the multimodal learning literature for use in flaw detection and demonstrate how these models outperform traditional deep learning models. We present results on detecting software flaws using the Juliet Test Suite and Linux Kernel.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Joint Analysis of Program Data Representations using Machine Learning for Improved Software Assurance and Development Capabilities

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

PSU ESI Review

A guide to developing an Energy Service Interface (ESI) was created as part of the Grid Modernization Laboratory Consortium 2.5.2 ESI project. The approach applies device-agnostic and service-oriented ESI principles and leverages documents such as the Interoperability Maturity Model and Common Grid Service Definitions to provide a methodology to review, develop, and update standards and profiles to engage distributed energy resources (DER) to provide grid services. This document evaluates the ESI developed by Portland State University’s Power Engineering Group under the Electric Grid of Things project funded by the U.S. Department of Energy. The evaluation explores the compliance of this specific implementation with the GMLC ESI principles to provide an example of an ESI profile and gap analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

C-HER Metadata Overview: Approach, Standards, and Rigor for the Centralized Health and Exposomic Resource

The Centralized Health and Exposomic Resource (C-HER) unifies environmental, demographic, geographic, and health-related data for exposomic research. The source data differ in format, geographic coverage, time period, resolution, terminology, and documentation. We use a common metadata framework to describe those differences and to record how each data resource has been processed, documented, and ingested. This document relates only to the C-HER metadata framework. It explains the information that is recorded for each resource, the standards used to organize that information, the conditions for metadata completeness, and the relationship between metadata and quality review. It is intended for those who need to understand what C-HER metadata communicates and how it supports appropriate use of the data. It is not an implementation specification or procedure. It does not document the database schema, source code, deployment configuration, transformation algorithms, or dataset-specific QA/QC thresholds. Those materials are maintained separately.

MacFarland, Midgie [ORNL] (ORCID:0009000807354078)↗

DOE OSTI2025

Automatically parallelizing batch inference on deep neural networks using Fiats and Fortran 2023 `do concurrent`

This paper introduces novel programming strategies that leverage features of the Fortran 2023 standard of the International Standards Organization (ISO) to automatically parallelize computations on deep neural networks. The paper focuses on the interplay of object-oriented, parallel, and functional programming paradigms in the Fiats deep learning library. We demonstrate how several infrequently used language features play a role in enabling efficient, parallel execution. Specifically, the ability to explicitly declare that a procedure is pure facilitates inference in the context of the language’s loop-parallelism construct `do concurrent`. Also, explicitly prohibiting the overriding of a parent type’s type-bound procedures eliminates the need for dynamic dispatch in performance-critical code. Finally, this paper uses batch inference calculations on a neural network surrogate for atmospheric aerosol dynamics to demonstrate that LLVM Flang compiler’s automatic parallelization of `do concurrent` achieves roughly the same performance and scalability as achieved by OpenMP compiler directives. We also demonstrate that double-precision inference costs 37–72% longer runtime than default-real precision with most values in the range 57-60%.

Rouson, Damian↗

DOE OSTI2022

The Dependence of the Type Ia Supernova Host Bias on Observation or Fitting Technique

More luminous Type Ia supernovae prefer less massive hosts and regions of higher star formation. This correlation is inverted during width–color–luminosity light-curve standardization resulting in step-like biases of distance measurements with respect to host properties. Using the PMAS/PPak Integral-field Supernovahosts COmpilation (PISCO) supernova host sample and Sloan Digital Sky Survey, Galaxy Evolution Explorer, and Two Micron All Sky Survey photometry, we compare host stellar mass and specific star-formation rate (sSFR) from different observation methods, including local versus global, and fitting techniques to measure their impact on the host step biases. Mass-step measurements for all our mass samples are consistent within a 1σ significance from –0.03 ± 0.02 mag to –0.04 ± 0.02 mag. Including or excluding UV information had no effect on measured mass-step size or location. sSFR step sizes are more significant than mass-step measurements and varied from 0.05 ± 0.03 mag (Hα) and 0.06 ± 0.02 mag (UV) for a 51 host sample. The sSFR step location is influenced by the mass sample used to normalize star formation and by sSFR tracer choice. The step size is reduced to 0.04 ± 0.03 mag when using all available 73 hosts with Hα measurements. This 73 PISCO host subsample overall lacked a clear step signal, but here we are searching for whether different choices of mass or sSFR estimation can create a step signal. We find no evidence that different observation or fitting techniques choices can create a distance measurement step in either mass or sSFR.

79 ASTRONOMY AND ASTROPHYSICS↗