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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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This is a slide deck for the public PAT Informational Webinar scheduled for July 22, 2025. This the first webinar for PAT after the new tool was launched on nrel.gov on June 16, 2025.
This a poster on PAT for the CESA National Energy Summit for States: Navigating Energy Trends and Federal Programs Conference.
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TREAT microreactor Experiment Cell (T-REXC). This facility will provide a test bed for microreactor developers to perform demonstration projects and critical experiments in an expedited manner. In addition to other systems, structures, and components (SSCs), T-REXC will provide beryllium oxide (BeO) reflector components that T-REXC users can incorporate into their equipment installation. This specification details supplier requirements associated with these BeO components.
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The adoption of machine learning approaches for gamma-ray spectroscopy has received considerable attention in the literature. Many studies have investigated the deployment of various algorithm architectures to a specific task. However, little attention has been afforded to the development of the datasets leveraged to train the models. Such training datasets typically span a set of environmental or detector parameters to encompass a problem space of interest to a user. Variations in these measurement parameters will also induce fluctuations in the detector response, including expected pile-up and ground scatter effects. Fundamental to this work is the understanding that 1) the underlying spectral shape varies as the measurement parameters change and 2) the statistical uncertainties associated with two spectra impact their level of similarity. While previous studies attribute some arbitrary discretization to the measurement parameters for the generation of their synthetic training data, this work introduces a principled methodology for efficient spectral-based discretization of a problem space. A signal-to-noise ratio (SNR) respective spectral comparison measure and a Gaussian Process Regression (GPR) model are used to predict the spectral similarity across a range of measurement parameters. This innovative approach effectively showcased its capability by dividing a problem space, ranging from 5 cm to 100 cm standoff distances and 5 μCi–100 μCi of 137 Cs, into three unique combinations of measurement parameters. The findings from this work will aid in creating more robust datasets, which incorporate many possible measurement scenarios, reduce the number of required experimental test set measurements, and possibly enable experimental training data collection for gamma-ray spectroscopy.
How can independent system operators (ISOs) take advantage of probabilistic solar forecasts to lower generation costs and improve reliability of power systems? We discuss one three-step approach for doing so, focusing on how such forecasts might help the California Independent System Operator (CAISO) prepare unexpected net load ramps, where net load equals gross demand minus wind and solar production. First, we enhance an existing solar forecasting system to provide well-calibrated hours-ahead probabilistic forecasts. We then relate the degree of uncertainty reflected in the forecasted prediction intervals (independent variables) to error distributions for net load ramp forecasts for the CAISO real-time market (dependent variable) using machine learning and quantile regression. Projected ramp forecast errors conditioned on solar uncertainty are translated into flexible ramp requirements that therefore reflect real-time meteorological and solar conditions, improving on typical ISO procedures. Detailed descriptions are provided on the quantile regression and kth-nearest neighbor categorization methods for accomplishing that translation. Finally, a multiple time-scale look-ahead market simulation model is applied to a 118-bus IEEE Reliability Test System, modified to represent the CAISO generation mix and demand distributions. The model runs quantify how solar-conditioned ramp requirements can, first, decrease operating costs by reducing requirements compared to often conservative unconditional methods and, second, decrease generation scarcity events and consequently improve reliability by increasing flexibility requirements at times when unconditional forecast-based requirements understate actual ramp uncertainty. Solar-conditioned ramp requirements are found to reduce generation operating costs by about 2% for the test system (which would be equivalent to over $\$100$ million per year for a CAISO-size system).
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Abstract not provided.
The purpose of this training is to provide a comparison of concepts between what the previous policy (P840-1) stated, and what the new policy (P841-1), now stipulates. This training is intended to offer currently authorized personnel with the necessary information to transition to implementing the new processes and requirements for their positions. The included material will provide relevant descriptions of several of the key processes and tasks that personnel need to be familiar with to perform their duties.
A developer will install a renewable energy system on DOE property. In exchange, DOE agrees to purchase the power generated by the system. These power purchase payments repay the developer over the contract term. The developer owns, operates, and maintains the system for the life of the contract.
Abstract not provided.
One of the most important responsibilities chemical owners at Los Alamos National Laboratory (LANL) have is to ensure the safety of the chemicals they own. An important consideration is how chemicals react to their environment. In the case of 2-propanol, a common solvent found in research laboratories used often in manufacturing and analytical processes, it can develop peroxides with exposure to light. 2-Propanol is classified as a Group B (class 3) peroxide-forming solvent. However, there is a dearth of literature that summarizes the risks surrounding peroxide formation in 2-propanol (IPA), leading to a lack of awareness of these hazards in the chemistry community.
The Institutional Quality and Performance Assurance Division's Quality Support Services Group conducted a surveillance of IQPA-IQ Supplier Evaluation group's required assessments of suppliers providing items and services relevant to the Nevada National Security Site. Based upon records examined, the surveillance team has determined that all supplier evaluation/oversight requirements are being met, and no Findings or Opportunities for Improvement are noted.
The Microreactor Applications Research Validation and Evaluation (MARVEL) project is intended to be among the first U.S. advanced reactor demonstrations in four decades. By virtue of being sponsored directly by the U.S. Department of Energy (DOE), the reactor is expected to benefit the broader nuclear community by exercising design processes, safety reviews, and supply chains. This project is committed to publicly documenting key lessons learned along the way toward demonstration. Building on a 2025 report that focused on lessons learned from guard vessel fabrication and Primary Coolant Apparatus Test (PCAT) testing (Abou-Jaoude et al. 2025), this 2026 edition captures findings spanning structural fabrication, quality assurance (QA), reactivity control system (RCS) assembly, fuel fabrication and shipment, safety basis development, balance-of-plant design, and overall reactor design.
The Energy to Communities (E2C) peer-learning cohort program provides technical assistance to groups of 15 community entities around a common energy topic over the course of 6 months. Every month, participants join a virtual meeting where they hear from experts and exchange strategies and best practices with their peers. This cohort, "Successful Energy Project Implementation" will explore common challenges in implementing energy projects and learn strategies to turn project plans into reality. Each participant will focus on a local energy project or priority as a cornerstone for their learning throughout the series. This presentation is focused on project performance, monitoring, and O&M. This workshop is on May 27.