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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Retro-Commissioning Sensor Suitcase for Energy Efficiency (CRADA Final Report)

The Sensor Suitcase project aimed to enable cost-effective, data-driven retro-commissioning (RCx) in small commercial buildings through a self-installable toolkit consisting of portable sensors and diagnostic software. Results showed that the Sensor Suitcase could identify common operational faults leading to 5–15% energy savings opportunities, with automated reports requiring minimal user interpretation. The project adhered closely to its Statement of Work (SOW) but evolved the reporting interface and sensor logging duration based on field feedback. The development and validation of diagnostics are documented in PNNL technical reports and internal analysis summaries.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Reducing Mass of Steel Auto Bodies using Thin Advanced High Strength Steel with Carbon-Fiber Reinforced Epoxy

Diversitak, a company based in Detroit, MI, has developed a proprietary, low specific gravity, carbon fiber-reinforced epoxy (CFRE) under U.S. patent number 9,963,58832. Preliminary testing on this new material conducted in collaboration with ArcelorMittal Steel Company proved out the CFRE concept. A thin layer of this CFRE was applied to a stamped sheet of steel with residual stamping oils from a mill, in a time corresponding to automotive processing (e.g., ~15 seconds), and processed following automotive e-coat procedures (phosphating + 175–200°C heating), to complete the curing. No problems with adherence or performance were noted. While the CFRE does add weight to a thin gauge steel panel, it weighs much less than what is displaced by using thicker conventional mild steel gauges. The application of the coating showed a significant increased dent resistance, oil canning resistance, and part stiffness.This current two-year project was designed to mature this new technology to near manufacturing readiness to reduce the weight of a vehicle and lower the cost of weight reduction. The process involves the use of thinner gauge steels than are currently used. The collaborative development team included two industrial manufacturers: Diversitak and ArcelorMittal Steel Company; LightMAT; and two National Laboratories: Oak Ridge National Laboratory (ORNL) and Idaho National Laboratory (INL). The team developed a new manufacturing process to reduce the weight of a vehicle and lower the cost of weight reduction, as well as a better understanding of how to apply the coating so that it will perform to a high standard in-service. The team also performed an in-depth study to determine the long-term durability of the materials manufactured using this technology and well-known automotive industry standard tests.The overall process involved stiffening the thinner gauge steel by applying the CFRE on only one side. To accomplish this goal, the optimal reinforcement fiber length and fiber concentration was first determined. This was followed by measuring the coefficient of thermal expansion (CTE) in all three directions, so it could be fed into manufacturing models and methods for rapidly and inexpensively applying the coating. This was followed by panel level evaluations of the coating and steel combination, and then by full part demonstration of the technology on door panels. The final step was corrosion testing of the parts.ArcelorMittal characterized the advanced high strength steel (AHSS) (e.g., metallurgy-heat treatment for required AHSS properties as a function of the sheet thickness, state of internal stress) and quantified CFRE adhesion to the steel as a function of sheet preparation (e.g., rolling and stamping).ORNL optimized the fiber length, fiber concentration, and coating thickness for best vehicle function and performance at the least cost. Along with the suppliers, ORNL developed a durable CFRE application process (e.g., gun material, design, robotic dispensing process) and identified the adhesion stability of the CFRE during process holding. An approach to ensure that application/curing timing conforms to conventional assembly line speed and plant cycle times was determined. ORNL also determined the CTE of the material in all three directions and performed material scanning electron microscopy (SEM) analyses.INL characterized the corrosion properties of the steel panels coated with CFRE. The panels were investigated for corrosion resistance and stability as replacement materials used in automotive body panels to reduce mass. The coupons tested at INL were supplied by Diversitak after an optimized CFRE formulation was achieved in the already coated form for corrosion testing.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

NCERC-CL TREAT Free Field Characterization Foil Reaction Rate Results

This report summarizes the analysis and preliminary MCNP reaction rate modeling of free field characterization measurements performed in the Transient Reaction Test Facility (TREAT) at Idaho National Laboratory (INL) between 12/18/2023 and 2/22/2024. A series of seven irradiations were performed using the Big-BUSTER (Broad Use Specimen Transient Experiment Rig) core configuration. Foil sets including 19.5% LEU-Zr alloy wire, S, Au, Fe, Ni, Co, Ti, and Zr were deployed in each irradiation. Identical foil sets were supplied to Los Alamos National Laboratory (LANL), Lawrence Livermore National Laboratory (LLNL), and INL. All foils were supplied by INL. Additional details on the experiment can be found in [2]. The foil sets were shipped to the National Criticality Experiments Research Center Counting Laboratory (NCERC-CL) branch at LANL (NCERC-CL NISC) and were received on 3/25/2024. Additional details on receipt and counting are reported in [3]. All reactor dosimetry measurements were performed in adherence to the ASTM standards applicable to reactor dosimetry. Measured reaction rates from Au, Co, Fe, Ni, and Ti are reported. Select reaction rate ratios and simulated reaction rates and ratios are discussed. Reaction rates for all measured reaction products were modeled with MCNP 6.3.1 and a TREAT input deck supplied by Edward Lum. Neutron emission estimates calculated using the measured and simulated reaction rates show excellent agreement between the three fast-threshold n-p reactions with the percent differences being < 9% between the three reactions. The capture reactions had poorer, but still reasonable, agreement with < 20% percent differences between the neutron emission estimates of the three capture reactions. The percent difference between the fast-threshold and capture reactions is ∼ 100% for each reaction. This supports the TREAT model is not accurately modeling the neutron spectrum and additional measurements are required to characterize TREAT and match measurements to simulation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Software Quality Assurance for the MOOSE-Based Open-Source Multiphysics Code Cardinal - An Expanded CI Testing Suite

Cardinal is a wrapping of the GPU-oriented spectral element Computational Fluid Dynamics (CFD) code NekRS and the Monte Carlo particle transport code OpenMC within the Multiphysics Object-Oriented Simulation Environment (MOOSE). Cardinal provides high-resolution thermal-hydraulics and/or radiation transport feedback to MOOSE multiphysics simulations. Multiphysics feedback is implemented in a geometry-agnostic manner which eliminates the need for rigid one-to-one mappings. A generic data transfer implementation also allows NekRS and OpenMC to couple to any MOOSE application, enabling a broad set of multiphysics capabilities. Cardinal simulations can also leverage combinations of MPI, OpenMP, and GPU resources. Cardinal continuous development and improvement efforts have led to the software being considered as a high-fidelity design and licensing tool for key areas of nuclear reactor relevant physics, including neutron transport, fluid flow, heat transfer, and mechanical processes. The fast development and expansion of the software from a pure R&D framework towards its application in the nuclear industry and regulation require a focus on developing, enhancing and, maintaining Cardinal’s software quality through strict adherence to a Software Quality Assurance (SQA) framework and SQA program. To facilitate compliance with SQA standards, the Cardinal SQA Program has been initiated during Fiscal Year 2023 (FY23). During the development of the Cardinal SQA Program, multiple gaps have been identified. These gaps are primarily related to model verification and code pedigree as they relate to the use of Cardinal as a safety analysis tool. These gaps have been captured in a report published in 2023. A second report highlighted the progress made during Fiscal Year 2024 (FY24) and described Argonne’s effort to document and integrate software verification within Cardinal’s software development process. This report documents a snapshot of the verification test cases currently available for Cardinal and NekRS in their assimilation into a Continuous Integration (CI) platform. Following the CI practice permits the integrating of source code changes frequently and ensuring that the integrated codebase clears the verification testing for the software. It should be noted that the SQA program itself, including the program plans, procedures, configuration management, and testing strategies, need to be developed in a future step of this task.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Progress Towards NQA-1 for Cardinal in FY25

Cardinal is a wrapping of the GPU-oriented spectral element Computational Fluid Dynamics (CFD) code NekRS and the Monte Carlo particle transport code OpenMC within the Multiphysics Object-Oriented Simulation Environment (MOOSE). Cardinal provides high-resolution thermal-hydraulics and/or radiation transport feedback to MOOSE multiphysics simulations. Multiphysics feedback is implemented in a geometry-agnostic manner which eliminates the need for rigid one-to-one mappings. A generic data transfer implementation also allows NekRS and OpenMC to couple to any MOOSE application, enabling a broad set of multiphysics capabilities. Cardinal simulations can also leverage combinations of MPI, OpenMP, and GPU resources. Cardinal continuous development and improvement efforts have led to the software being considered as a high-fidelity design and licensing tool for key areas of nuclear reactor relevant physics, including neutron transport, fluid flow, heat transfer, and mechanical processes. The fast development and expansion of the software from a pure R&D framework towards its application in the nuclear industry and regulation require a focus on developing, enhancing,and maintaining Cardinal’s software quality through strict adherence to a Software Quality Assurance (SQA) framework and SQA program. To facilitate compliance with SQA standards, the Cardinal SQA Program was initiated during Fiscal Year 2023 (FY23). During the development of the Cardinal SQA Program, multiple gaps have been identified. These gaps are primarily related to model verification and code pedigree as they relate to the use of Cardinal as an analysis tool. These gaps were captured in a report published in 2023. A second report highlighted the progress made during Fiscal Year 2024 (FY24) and described Argonne’s effort to document and integrate software verification within Cardinal’s software development process. This report documents the progress made towards NQA-1 for Cardinal in the Fiscal Year 2025 (FY25). All cases in the expanded Continuous Integration (CI) suite of NekRS are included in this report which test the solvers and modules available in NekRS exhaustively. The NekRS tests are integrated with the Cardinal CI suite and made available in publicly accessible Github documentation. Following the CI practice permits integrating of source code changes frequently and ensuring that the integrated codebase clears the verification testing for the software. Also in this report is a brief overview of the development of the Cardinal Software Quality Assurance Plan (SQAP) that was done in FY25, though it should be noted that the rest of the documentation for the SQA program needs to be developed in a future step of this task.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Testing, Calibration, and UxS Integration of the Kromek GR1 Plus CZT Gamma Spectrometer

Collecting radiation measurements can be a hazardous task, especially in the presence of highly active sources. Various scenarios necessitate source search, classification, and quantification, often with limited or no a priori information. Activities such as disaster mitigation, emergency response, environmental monitoring, and site remediation may involve dangerous radioactive sources. In these situations, there is a pressing need for remote monitoring capabilities that protect human operators from potential harm and enhance adherence to the principle of “As Low as Reasonably Achievable” (ALARA) for radiation doses. The first step toward achieving remote radiation measurement capabilities is the remote operation of a radiation sensor. Once this milestone is reached, the next challenge is to integrate this remote sensing capability into suitable actuation agents, collectively referred to as uncrewed systems (UxS). These systems include familiar platforms such as robotic quadrupeds, aerial multirotor vehicles, and ground vehicles, any of which may be teleoperated, act autonomously, or utilize a combination of both. A critical factor in achieving remote radiation sensing is the availability of data from the appropriate sensor. Many commercially available radiation sensors have closed-source documentation for their communication protocols. Typical end-user products are often self-contained, handheld devices designed for manual measurement scenarios. While there are commercial off-the-shelf (COTS) integrations of radiation sensors with UxS available for purchase, these solutions are typically tailored for specific use cases and may not meet the requirements of different applications. This paper discusses efforts to remotely acquire radiation measurements from a small form-factor CZT gamma spectrometer. Sandia National Laboratories has successfully demonstrated the initial capability to integrate low size, weight, and power (SWaP) gamma spectroscopy into various UxS, alongside co-located GPS data logging and sensor calibration and qualification. With remote gamma spectroscopy achieved, the stage is set for UxS integration of this capability.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Explainable and Differentiable Reinforcement Learning for Multi-objective Optimization in Particle Accelerators

Operating particle accelerators involves optimizing multiple goals simultaneously, which can be challenging due to trade-offs among objectives. While evolutionary algorithms like the genetic algorithm (GA) have been used for various Multi-Objective Optimization (MOO) tasks, they are not inherently suited for complex control problems. This talk highlights two variations of Reinforcement Learning (RL) for concurrently optimizing heat load and trip rates at the Continuous Electron Beam Accelerator Facility (CEBAF). The problem involves strict constraints on individual states, actions, and overall energy requirements of the beam. First, this talk highlights how differentiability can be harnessed through a Deep Differentiable Reinforcement Learning (DDRL) approach to address MOO issues within particle accelerators. We examine the DDRL method alongside Model Free Reinforcement Learning (MFRL), GA, and Bayesian Optimization (BO). The performance of these methods is assessed by generating a Pareto-front for two objectives. Our findings indicate that DDRL excels in handling high-dimensional problems more effectively than MFRL, BO, and GA. Next, we will show integration of explainable physics-based constraints into RL algorithms to enhance trans- parency and trust in decision-making processes by enabling users to verify that agents adhere to established physical principles. This surrogate function can be modeled using neural networks or sparse dictionary mod- els. By examining the mathematical form of the learned constraint function, we are able to confirm the agent has learned to use the established physics of each environment provided but the surrogate model. In addi- tion, we find that the introduction of a mathematical functional dictionary based surrogate model enables our reinforcement learning algorithms to reliably converge for difficult high-dimensional accelerator controls environments.

Rajput, Kishansingh [Thomas Jefferson National Acc↗
DOE OSTI2025

Laser Confocal Microscopy Uncertainty Quantification Study

At Los Alamos National Laboratory (LANL), the Storage Safety and Engineering (SSE) team completes annual surveillance on a subset of in-use interim nuclear material storage containers in fulfilment of requirements outlined in DOE Manual M 441.1-1. The containers are selected through several methods, such as subject matter expert judgement, random selection, and trending items. Following these selections, the SSE team has the capacity to complete surveillance on 15-20 containers each fiscal year, composed of a combination of SAVY-4000 and Hagan storage containers. Through previous work, the stainless-steel components of the containers have been identified as life limiting components, with an emphasis on the thin-walled bodies. The team is focused on understanding the extent of general and pitting corrosion, due to observations of extensive corrosion from stored contents and bag-out-bag degradation. Quantifying corrosion effects on the thin-walled stainless steel container bodies, and understanding potential impacts to the respective design release rates and design qualification release rates is paramount to the team. To date, destructive examination (DE) has proven to be the most insightful method for developing an understanding on the extent of corrosion on used containers. To standardize this process, the SSE team developed a destructive examination guide for analyzing stainless steel components of the containers. Corroded containers of interest are identified during surveillance activities and set aside for sectioning and characterization. Following sectioning, a major step in the DE workflow is the utilization of laser confocal microscopy for scanning corroded samples of interest and extracting data on pits, such as count, depth, and equivalent diameter. Adhering to the techniques outlined in the DE guide, analysis has been completed on two Hagans and one SAVY-4000 container, with the maximum pit depth recorded as 139.1 ± 22.82 μm on a 17.5 year old Hagan. The findings from the completed destructive examinations will be utilized to support lifetime extension efforts of the SAVY-4000 as the team can better estimate corrosion rates and effects over time based on stored contents and age. Due to the implications of observing extreme pit depths that approach the nominal container body thickness of .0299 inches (0.759 mm) or minimum container thickness of 0.236” (0.6 mm), high confidence in the LCM measurements is desired. Through testing outlined in, it was concluded that the total error ascribed to the 20x objective when conducting large image mapping on the Keyence VK-X3050 laser confocal microscope (LCM) relative to a 50x objective (reference) is 16.4% (± 8.73%). For shallow features on the order of pristine SAVY surface defects (i.e. 5 μm), this uncertainty is appropriate. However, this conservative estimate of total error poses a fundamental concern for pit depths that approach the thickness of the measured samples. That is, with the measurement uncertainty currently employed on all measurements, the LCM would be unable to resolve if a pit with a depth of 515 μm is through wall. Standard step height samples were procured and used in the present study to assess the resolution and repeatability of height measurements. Understanding the resolution and repeatability of height measurements was the first focus of the team as it relates directly to pit depth, which is of primary concern. Calibration gratings were procured to evaluate the resolution and repeatability of measurements in the X and Y axes of the LCM stage. The results of the depth uncertainty study were conducted first and presented in the subsequent sections. The planar uncertainty study is appended to the depth study with conclusions from both summarized at the end of the report.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Reducing Mass of Steel Auto Bodies using Thin Advanced High Strength Steel with Carbon-Fiber Reinforced Epoxy

Diversitak, a company based in Detroit, MI, has developed a proprietary, low specific gravity, carbon fiber reinforced epoxy (CFRE) under U.S. patent number 9,963,58832. Preliminary testing on this new material conducted in collaboration with ArcelorMittal Steel Company proved out the CFRE concept. A thin layer of this CFRE was applied to a stamped sheet of steel with residual stamping oils from a mill, in a time corresponding to automotive processing (e.g., ~15 seconds), and processed following automotive e-coat procedures (phosphating + 175–200°C heating), to complete the curing. No problems with adherence or performance were noted. While the CFRE does add weight to a thin gauge steel panel, it weighs much less than what is displaced by using thicker conventional mild steel gauges. The application of the coating showed a significant increased dent resistance, oil canning resistance, and part stiffness.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Package Testing Program Process for Validation and Verification of Software

The purpose of this document is to define the process for validation and verification (V&V) of software used to test the thermal data acquisition equipment in the Oak Ridge National Laboratory (ORNL) Package Testing Program (PTP) as part of the testing configuration(s). This process adheres to all applicable ORNL Standards Based Management System requirements and correlates with the PTP Quality Management System and the Software Quality Assurance Plan.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

ORNL Package Testing Program Overview

When transporting radioactive or hazardous materials, safety and security are top priorities. Packaging and transportation regulations require reliable evidence that containers have passed rigorous performance tests to ensure that the public and the environment are protected from the hazardous nature of the cargo. Because of its long history in energy and security research, Oak Ridge National Laboratory (ORNL) has needed to ship hazardous packages for the past 65 years. Since the 1940s, ORNL has made significant contributions to transportation regulations and has been at the forefront of regulatory testing development. Today, ORNL is a leader in standards development and testing of designs of radioactive material packages. ORNL plays a critical role in ensuring the safe transportation of radioactive materials across the United States by executing rigorous testing campaigns of packages that contain radioactive materials. Early testing activities focused on supporting the development of transportation regulations established by both the International Atomic Energy Agency (IAEA) and the United States. Currently, all package testing activities are performed under the Package Testing Program (PTP) at the Package Evaluation Facility (PEF) located at the National Transportation Research Center (NTRC), which is about 10 miles from the main ORNL campus. The PTP develops and evaluates testing solutions, ensuring that they are safe efficient, and in compliance with regulatory requirements. The vision for the PTP is to be a world-class leader in the evaluation and testing of radioactive and hazardous material packages. The key elements necessary to fulfill this mission are an experienced and professional staff; state-of-the-art facilities, equipment, and instrumentation; and completion of challenging programs and projects important to package transportation. Collaborations with other internal and external organizations play a significant role in building stronger teams and achieving this vision. By adhering to and advancing regulatory standards, ORNL’s PTP supports development of safe and compliant packaging, safeguarding the transportation process from potential risks associated with radioactive material logistics.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Heavy-Duty Hydrogen Station Equipment Performance Device (HD HyStEP) Specifications and Design Considerations

The original Hydrogen Station Equipment Performance (HyStEP) device was commissioned in 2015 and was critical for the rapid validation of light-duty (LD) hydrogen fueling stations. As applications for hydrogen as a heavy-duty (HD) transportation fuel continue to grow, new heavy-duty stations are being developed to fuel these vehicles that require larger onboard storage tank systems to meet HD transportation demands and drive cycles. The differences in size and geometry from LD vehicles have driven the creation of new hydrogen fueling protocols that will enable safe and economical fueling of HD vehicles. With the new requirements that are set out in HD fueling protocols like SAE J601-5, a new HyStEP-like device is needed to evaluate the capabilities of high-flow hydrogen stations to fuel HD vehicles to the new protocol standard. To create a heavy-duty HyStEP (HD HyStEP) device, an effort has been undertaken to evaluate the requirements that would form the basis for the design of a successful HD HyStEP device. The primary design goal of this HD HyStEP device is its capability of following a test methodology similar to what is outlined in CSA HGV 4.3, which guides the validation of LD fueling dispensers but with adjustments to verify adherence to the HD fueling protocols (SAE J2601-5) for 70 MPa and 35 MPa pressure class vehicles rather than the LD fueling protocols (SAE J2601). The design presented here aims to provide information that enables the creation of an HD HyStEP device that achieves the primary design goal while being informed by the years of experience from the current HyStEP operators.

08 HYDROGEN↗
DOE OSTI2026

SolarAPP+ Performance Review (2024 Data)

The Solar Automated Permit Processing Plus (SolarAPP+) platform is an online portal to facilitate and expedite rooftop solar photovoltaic (PV) and battery storage permitting processes. SolarAPP+ allows PV contractors to upload system specifications, have that information automatically reviewed for code compliance, and receive instant approval for code-compliant systems, reducing authority having jurisdiction (AHJ) staff time needed for review. SolarAPP+ also provides inspection checklists to verify installation practices and adherence to approved designs. This report is part of an ongoing series of reviews of SolarAPP+ performance. Consistent with previous performance reviews, we summarize SolarAPP+ adoption trends to date and compare various metrics for PV systems permitted through SolarAPP+ versus systems permitted through traditional AHJ permitting processes. As of the end of 2024, 799 AHJs had expressed interest in the platform, with 264 fully adopting (215) or piloting (49) the platform. In 2024, 861 installers submitted 37,393 permits through the SolarAPP+ platform, including 27,375 permits for PV+storage systems. SolarAPP+ permits accounted for around 43% of all permits issued in all participating AHJs, and more than 60% of all permits in several participating AHJs. We compare permitting timelines through SolarAPP+ to traditional AHJ permitting processes to assess the platform's performance. Consistent with previous SolarAPP+ performance reviews, we find that permitting timelines are significantly shorter for SolarAPP+ projects. Based on median timelines, a typical SolarAPP+ project is permitted and inspected 12 business days sooner than traditional projects. We estimate that automatic SolarAPP+ permitting saved around 18,400 hours of AHJ staff time in 2024. Finally, we estimate that SolarAPP+ eliminated over 100,000 business days in permitting-related delays in 2024.

14 SOLAR ENERGY↗
DOE OSTI2025

Feasibility and Qualitative Risk Assessment of Fuel Cell Electric Bus Use for Emergency Backup Power for Airport Applications

Airports rely on robust backup power systems to maintain critical operations during emergencies. This paper investigates the feasibility of using hydrogen fuel cell electric buses (FCEBs) as emergency backup power sources, focusing on their technical specifications, operational challenges, and associated risks. Using Portland International Airport as a case study, the analysis evaluates the power output and hydrogen consumption of New Flyer’s 40’ Xcelsior Charge fuel cell buses, to provide power for key airport systems. Additionally, the paper explores the design and layout of how FCEBs can be connected to airport facilities, and addresses safety considerations, hydrogen supply logistics, and electrical compatibility. While FCEBs present significant advantages in terms of sustainability and resilience, their implementation requires careful planning, infrastructure investment, and adherence to safety standards. This research provides actionable insights for airport stakeholders seeking to transition to hydrogen-based backup power system.

08 HYDROGEN↗
DOE OSTI2026

Draft Feasibility Assessment for Use of AI in Preparing Transportation Safety Analysis Reports

Preparing transportation safety analysis reports for microreactors is time and labor intensive, requiring extensive cross referencing to Federal regulations, previously approved documents, and expert review comments across structural, thermal, criticality, shielding, containment, and security. These burdens are magnified by the novelty of microreactor technologies and the evolving regulatory landscape, as well as current workforce constraints. Generative AI and supporting machine learning tools present an opportunity to accelerate drafting timelines, lift generalized writing burdens, and systematically enforce regulatory adherence through retrieval augmented generation and other knowledge retrieval and mapping methods. This draft report presents a preliminary feasibility assessment of the use of AI to expedite the preparation of microreactor transportation safety analysis reports and proposes an initial methodology for doing so.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

The Effects of Beta Irradiation on the Performance of ATR Fiber Probes

The use of real-time inline monitoring to track the chemical composition of slurries is recognized as a promising method to reduce the processing time of Hanford tank waste. The conversion of waste into glass suitable for long-term storage requires adherence to waste acceptance criteria and process control limits that respectively ensure safe operation of the vitrification facility and suitable glass quality. Real-time measurements of blended waste materials can ensure that the various criteria are met or can be addressed in a timely manner.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗