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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

PCMDI Metrics Package

The Program for Climate Model Diagnosis & Intercomparison (PCMDI) Metrics Package (PMP) is used to provide "quick-look" objective comparisons of Earth System Models (ESMs) with one another and available observations. The PMP provides a diverse suite of analysis utilities each of which produce summary statistics that gauge the consistency between climate model simulations and available observations. The primary application of the PMP is to evaluate simulations from the Coupled Model Intercomparison Project (CMIP). It can also be used to provide objective performance summaries during the model development process as well as selected research purposes.

Ullrich, PaulA [Lawrence Livermore National Labora↗
DOE OSTI2024

OpenPATH - Leveraging Technology to Measure Travel Behavior

Shifting transportation to more sustainable modes is a key piece of the decarbonization puzzle. However, mobility behavior and travel patterns are difficult to influence because they are difficult to measure. OpenPATH provides a tool to capture longitudinal behaviors through a smartphone application. Agencies interested in gathering data about a population's travel behavior can set up a deployment of the app customized to the needs of their community. Partners can choose between simple mode and purpose labels or surveys for each trip to balance the level of user engagement with the associated burden. The labels, trip surveys, and an initial demographic survey can all be tailored to the specific context of the deployment. The OpenPATH tool is unique in its open-source nature, ability to gather detailed longitudinal travel data, and design allowing direct engagement with travelers. A valuable technological advancement, this tool enables partners to measure the way changes in the transportation landscape impact their community. The suite of tools includes both public and administrator dashboards. The public dashboard supports continuous data analysis through charts presenting trip information updated daily. The administrator dashboard displays geospatial data and supports data export. Example applications have included e-bike programs; gathering valuable metrics on increased access to opportunities and reduction in VMT, and studies aimed at understanding existing mobility behavior to see where advancements such as electric vehicles could fit into these habits. OpenPATH collects travel data in association with an initial demographic survey, enabling detailed insight into the behavior patterns or impact of a certain program on different populations.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Assessment of Condition Monitoring Methods and Technologies for Inservice Inspection and Testing of Nuclear Power Plant Components

This report was prepared for the U.S. Nuclear Regulatory Commission (NRC) to explore the application of advanced technologies toward meeting the current and future regulatory requirements for maintenance and condition monitoring of structures, systems, and components. The advanced technologies considered in this work are advanced sensors and instrumentation, data analytics, machine learning and artificial intelligence (ML/AI), physics-based models, and digital twins (DT). The interest in the application of advanced technologies for condition monitoring in nuclear power plants continues to grow, and current and future licensees are expected to implement advanced technologies as part of their inservice inspection (ISI) and inservice testing (IST) programs. This report delineates the outcomes of an exploratory investigation into the implementation of advanced condition monitoring technologies to address ISI and IST requirements. A thorough review was conducted of the existing regulatory requirements for ISI and IST, along with an analysis of associated industry practices. Additionally, a state-of-the-art assessment was performed on advanced condition monitoring technologies frequently employed in non-nuclear sectors. This research incorporated two nuclear-specific case studies to illustrate the application of these technologies within the current nuclear fleet. The report provides an exhaustive discussion on the technical challenges, considerations, and opportunities associated with the deployment of advanced condition monitoring technologies. The following are key considerations in the application of advanced technologies for the ISI and IST of nuclear power plant components: • Developing adequate verification and validation procedures to confirm the functional and non-functional requirements, • Developing technical capabilities to conduct real-time asset condition monitoring, • Establishing guidance and protocol for modeling and simulation tools to continuously meet regulatory requirements, • Addressing trustworthiness, explainability, and interpretability of ML/AI methods, • Evaluating maintenance activities to maintain an adequate safety margin and avoid undesirable conditions, • Establishing cybersecure condition monitoring programs associated with a computer-based software system, and • Establishing standardized evaluation metrics for advanced condition monitoring programs. Interest in the use of advanced technologies for condition monitoring in ISI and IST programs continues to grow, and the technology is expected to experience rapid and wide industry adoption in the near future. Adoption of advanced technologies for condition monitoring could have novel and unique impacts on regulatory activities associated with ISI and IST programs. The NRC is continuing to explore the regulatory aspects of advanced technologies as part of ISI and IST programs by pursuing additional research in this technical area.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Zero-Emission Transit Bus Needs Assessment

The transition to zero-emissions vehicles (ZEVs) in public transit has gained traction due to significant federal investments from the Bipartisan Infrastructure Law (BIL) and the Inflation Reduction Act (IRA). This needs assessment, commissioned by the Joint Office of Energy and Transportation and conducted by researchers at the Idaho National Laboratory, explores the current state of electrification in transit agencies, identifying barriers to implementation, potential funding sources, and operational considerations necessary for a successful transition. The assessment involved qualitative interviews with representatives from 19 transit service providers across diverse geographic regions. Key findings highlight the challenges related to bus facilities and operations, which require careful planning for charging infrastructure and maintenance capabilities to accommodate battery electric buses (BEBs) and hydrogen fuel cell buses (HFCBs). Agencies reported operational hurdles due to the shorter range of BEBs compared to diesel buses, necessitating revised scheduling and routing strategies. Despite these challenges, many agencies expressed optimism about their capacity to adapt. Funding availability emerged as a critical factor influencing the transition to ZEVs. While agencies welcomed increased financial support, particularly from the Low or No Emission Grant Program (Lo-No), concerns about the sustainability of this funding and the ongoing operational costs were prevalent. The need for a comprehensive funding inventory was underscored to ensure transit agencies are aware of all available resources. Technological constraints were significant barriers to ZEV adoption. The limited range of BEBs was frequently cited as a concern, leading to operational challenges and reliability issues. Agencies reported difficulties in sourcing replacement parts, which exacerbated downtime and maintenance challenges. Workforce development and training were identified as pivotal for a successful transition. Many agencies rely heavily on manufacturers for technician training, highlighting the need for scalable training programs that equip staff with the necessary skills to maintain electric powertrains effectively. This assessment offers actionable recommendations for the Joint Office, including enhancing outreach to transit agencies, developing resources for effective utility partnerships, and facilitating comprehensive training programs. Establishing a zero-emission bus evaluation program to track performance metrics such as cost, range, and reliability could provide valuable insights for transit agencies. The needs assessment provides a detailed examination of the challenges and opportunities facing transit agencies in their transition to zero-emissions bus fleets. By addressing these issues through targeted support, stakeholders can collaboratively work towards a cleaner, more sustainable public transportation system that benefits all communities.

33 - ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Integrated geological, economic, and risk assessment of underground hydrogen storage

Underground hydrogen storage (UHS) is a promising option to buffer variable renewable power and support the hydrogen economy. Yet this technology is early-stage, and key uncertainties remain about how coupled processes such as geochemical and geomechanical impacts affect long-term storage security. In this paper, we seek to integrate the results from a large, multi-scale research program to quantify core feasibility metrics and situate these studies within ongoing efforts. Through this work, we evaluate hydrogen recoverability during storage by assessing diffusive losses, losses to geochemical interactions and their subsequent impact on geomechanical properties, and losses to residual saturation during injection and withdrawal. Molecular and laboratory studies show that lithologic heterogeneity, pore geometry, and mineral surface chemistry govern hydrogen–rock interactions, controlling loss pathways. High-pressure coreflooding is used to estimate relative permeabilities and measure parameters needed for reservoir-scale sensitivity analyses. These core-scale experiments found early hydrogen breakthrough at low saturations driven by capillary and viscous fingering, which limits pore-space utilization at early times, while repeated injection and withdrawal cycles improve deliverability over time. Technoeconomic factors and purity requirements for end uses are also considered as part of our comprehensive feasibility analysis. Finally, we underscore the importance of geophysical monitoring and tailored injection strategies to maintain integrity and efficiency. Together, these results provide a quantitative foundation for safe, scalable deployment of UHS and highlight priorities for future integrated studies.

08 HYDROGEN↗
DOE OSTI2024

Development and Qualification of Advanced Alloys Used in High Helium and Displacement Damage Service for Fusion Energy Applications

This document represents a final report on the Department of Energy (DOE) Office of Science, Office of Fusion Energy Science (OFES) Grant Entitled “Development and Qualification of Advanced Alloys Used in High Helium and Displacement Damage Service for Fusion Energy Applications” (DE-FG02-94ER54275). This grant has been funded since 1994, by a series of five and three-year renewals. We express our deep appreciation for the long-standing support by OFES for our fusion materials research. The latest three-year renewal was awarded in 2019. This renewal subsequently received a 1- year no-cost extension, followed by another funded 1-year phaseout period. Here, we focus on this five-year effort. The final report is composed of 35 Fusion Semiannual Reports covering the period from 2019 to 2024. In addition, Appendix A lists 158 fusion materials papers published since 1994, with the partial, or full, support of the OFES program. We note that, as metrics of their high impact, these papers have received a total of 8582 citations, with an average of 54 per paper.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Event Classifications on DNE2 Main Experiment Data using a Convolutional Neural Network Ensemble

The Dynamic Networks (DN) Experiment for FY24 (DNE2) is an experiment within DN with the goal of quantitatively evaluating the effectiveness of solutions developed so far by various researchers under the Low Yield Nuclear Monitoring (LYNM) program using a shared set of metrics and datasets. A key component of this experiment is the mimicking of a signature processing pipeline, and comparing currently accepted and standard-use processing methods to more state-of-the-art processes developed under DN. In this work, we focus specifically on the Event Characterization (EC) Focus Area (FA) of the pipeline, where a seismic event’s magnitude, yield and class are identified. We use Deep Learning (DL) to classify the type of events being processed as either earthquakes (EQs) or explosions (EXs) for three iterations of experiment datasets. The model is noticeably more confident and accurate in classifying explosions than earthquakes, reflecting a known shortcoming of the model, that being of a bias towards predicting explosions over earthquakes in the west coast due to training data biases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Simulative Prediction of Solar Illuminance and Application of the Du-Sharples Model in Estimating Adapted Daylighting Metrics for an Urban Environment

The practice of daylighting in indoor spaces can significantly reduce electricity consumption and carbon emissions, improve human productivity, and enhance mood and cognitive perception. This work discussed the recent developments in daylighting science and practice, computed the periodic variations in average diurnal daylight levels for each month, quantified in terms of global horizontal illuminance and diffuse horizontal illuminance, for Kolkata, India, a city with tropical wet and dry climate, with two empirical luminous efficacy models of estimating solar illuminance, and assessed daylighting metrics with the Du-Sharples model. A program was formulated that could compute and generate daylight data with monthly-hourly solar irradiation data and the Du-Sharples model was utilized to predict dirt-corrected daylighting metrics for three glazing transmittance values and five elemental carbon deposition levels on glazing material. The highest monthly average global horizontal illuminance is recorded in April (64.05 klx for Littlefair model and 66.82 klx for Muneer-Kinghorn model) and the highest monthly average diffuse horizontal illuminance is recorded in July (33.23 klx for Littlefair model and 30.63 klx for Muneer-Kinghorn model). Further, the computed yearly average global and diffuse horizontal illuminance levels agree well with a previous study that applied the Perez model. Yearly average horizontal work surface illuminance level remained >1.5 klx for window-towall area ratio >30 %. The approach adopted in this work and the temporal variation charts of computed exterior daylight level data may assist building service engineers, architects, and indoor lighting practitioners in making informed policy decisions at different stages of building planning.

Engineering↗
DOE OSTI2026

PYDICE

A new Python program has been created that calculates similarity metrics (E, ck), available as a web server. Users can post one or more Sensitivity Data Files (SDFs) to either calculate similarity metrics (returned as a JSON response packet) or to be converted into a different SDF format (returned as a zip file).

Holcomb, Andrew [Oak Ridge National Laboratory (OR↗
DOE OSTI2024

Low Precision and Efficient Programming Languages for Sustainable AI: Final Report for the Summer Project of 2024

This document contains all relevant material generated during the authors' summer internship at NREL in 2024. This report shows how to improve energy efficiency of a few code samples by using low-precision data types combined with mixed-precision algorithms. The main applications considered here are (i) linear system solvers using mixed precision, and (ii) neural networks using mixed precision. This report also discusses how programming languages affect energy consumption of algorithms, energy metrics for a code and tools, and the available current software and hardware infrastructure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Exploring Enhanced Dominant Resource Fairness Using Linear Programming Calculated Weights

Maintaining resource fairness while achieving optimization for various performance metrics such as resource utilization, turnaround time and job latency is a well-known resource scheduling challenge in cloud computing. Despite the significant progress made with the introduction of dominant resource fairness by Ghodsi et al., which ensures major allocation properties such as sharing incentive, strategy-proofness, envy-freeness and Pareto efficiency to be achieved

Yan, Bo [Binghamton University]↗
DOE OSTI2024

Metric Learning to Accelerate Convergence of Operator Splitting Methods

Recent developments in machine learning have led to promising advances in accelerating the solution of constrained optimization problems. Increasing demand for real-time decision-making capabilities in applications such as artificial intelligence and optimal control has led to a variety of proposed strategies for learning to produce fast solutions to optimization problems. For example, recent works have shown that it is possible to accelerate the convergence of optimization algorithms by learning to select their parameters, such as gradient descent stepsizes. This work proposes a new approach, in which the underlying metric spaces of proximal operator splitting algorithms are learned to maximize convergence rate. While prior works in optimization theory have derived optimal metrics in simple cases, no such result exists for many practical problem forms including general Quadratic Programming (QP). This paper shows how differentiable optimization can enable the end-to-end learning of proximal metrics, enhancing the convergence of proximal algorithms for QP problems beyond what is possible based on known theory. Additionally, the results illustrate a strong connection between the learned proximal metrics and active constraints at the optima, leading to an interpretation in which the predicted proximal metrics can be viewed as a form of active set prediction.

King, Ethan [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2026

Rewarding Grid-Friendly Behavior: Estimating the Potential Bill Reduction and Load Shifting Benefits of Dynamic Prices

Shifting electric load from times of peak demand can be a key strategy to slow price growth as reducing peak demand avoids the cost of upgrading generation, transmission and distribution infrastructure. Utilities are releasing time-varying prices, such as time of use rates or dynamic prices, to incentivize grid-friendly load shifting. New dynamic price programs provide insight into the true cost of operating electricity grids and the potential economic benefits of load shifting. Program developers and device manufacturers need to understand the economic opportunities in terms of 1) the variation in prices across hours, days, and seasons; 2) the change in utility bills for customers who don’t shift load; and 3) the potential load shifted and economic value of different technologies if manufacturers or aggregators deploy price-responsive controls. This paper estimates possible impacts of dynamic price adoption and load shifting controls if customers paid the dynamic rate from one pilot program. Statistical analysis of historical prices identified annual and seasonal metrics as well as representative price curves for each circuit in the pilot. Simulations for residential technologies with price-responsive controls including unitary heat pump water heaters, central multifamily heat pump water heaters, heating & cooling + storage systems, and pool pumps estimated the potential impacts of highly dynamic prices both with and without load shifting controls. Results showed the potential to reduce electricity costs on representative days by 42-94% and reduce consumption during times of high electricity prices by 63-100% compared to baseline operation for those flex-friendly devices.

Grant, Peter↗
DOE OSTI2026

Machine Learning-Based Anomaly Detection for PMT Data Quality Monitoring in the SBN and DUNE

Maintaining high-quality detector data is essential for achieving the scientific objectives of the Short-Baseline Neutrino (SBN) Program at Fermilab. Current data quality monitoring (DQM) procedures rely primarily on threshold-based metrics and manual inspection of detector monitoring plots, making the detection of subtle or gradually developing anomalies both time-consuming and dependent on expert interpretation. This project developed and evaluated a machine-learning workflow for automatically identifying anomalous photomultiplier tube (PMT) channels in the Short-Baseline Near Detector (SBND) using optical-hit amplitude data. A Python-based analysis program was developed to process ROOT files, extract statistical features describing individual PMT amplitude distributions, and generate feature vectors for anomaly detection. These features were used to train an Isolation Forest model using data representing normal detector operation. The trained model was subsequently applied to independent detector runs to identify channels exhibiting statistically unusual behavior relative to the learned reference response. To support expert interpretation, the workflow generated complementary diagnostic products, including anomaly score distributions, normalized amplitude comparisons, decision-tree visualizations, and principal component analysis (PCA) projections. This project demonstrated the feasibility of integrating unsupervised machine learning into detector data-quality monitoring and developed a complete workflow for automated PMT performance assessment to aid expert-driven review. Beyond its technical contributions, the VFP appointment fostered a research collaboration between Aurora University and Fermilab and provided direct workforce development benefits by training the visiting faculty member in detector-scale machine-learning methods that are now being incorporated into undergraduate coursework and research. The methodology developed here provides a foundation for future applications to ProtoDUNE and other liquid argon time projection chamber (LArTPC) detectors, contributing to ongoing efforts to improve detector reliability, reduce manual monitoring requirements, and enable scalable data quality monitoring for future large-scale neutrino experiments, including the Deep Underground Neutrino Experiment (DUNE).

Colón Santana, Juan A. [Unlisted, US, IL]↗
DOE OSTI2025

Fundamental Research Aimed at Diverting Excess Reducing Power in Photosynthesis to Orthogonal Metabolic Pathways

Photosystems are incredible biological machines that use sunlight to drive the conversion of carbon dioxide to sugar. The amount of sunlight available for photosynthesis sometimes exceeds the amount of energy plants can use. This excess energy has to be safely dissipated through non-productive biological processes. The ultimate goal of this project is to understand whether we can utilize that otherwise unused excess energy. In our previous work, we showed that, in principle, it is possible to attach a catalyst to photosystem I and generate H2 using light. Our current strategy is to genetically fuse parts of the photosystem I complex with a recently discovered oxygen-tolerant [FeFe] hydrogenase. Our rationale is that such chimeric proteins may potentially result in the natural incorporation of the photosystem I-hydrogenase link using the inherent genetic machinery of the cell. In this project, we aim to verify that light-driven hydrogen production in this construction is possible. Throughout the project, we designed nanoconstructs that showcase the plausibility of this technology, at least in vitro. We take advantage of these constructs to investigate details of the coupling between photosystem I and a H2-producing enzyme called [FeFe] hydrogenase. This part of the project reveals details of the electron transfer between photosystem I and the attached hydrogenase, providing information that can lead to new strategies for improved biological photocatalysis. We also researched efficient and robust tethering of the [FeFe] hydrogenase to photosystem I in cyanobacteria. This work will highlight successful design strategies to guide the future development of photosynthetic biohybrids. Uncovering the principles governing the utilization of otherwise unusable energy significantly further our understanding of cyanobacterial photosynthesis. The work proposed establishes the feasibility of diverting excess energy under high light conditions to orthogonal enzymatic pathways and set design rules for efficient utilization of such a strategy for scientific and industrial applications in biosensing, renewable energy, and high-value chemicals production. The work addresses the DOE-BES Photosynthetic Systems program goal to develop a multidimensional understanding of photosystems that would provide specific metrics that instruct strategies for improving biological photosynthesis and for guiding the future development of bioreactors and biomimetic energy systems.

Photosynthetic systems, hydrogenase, cyanobacteria↗
DOE OSTI2024

WE-Validate: An Open-Source Framework For Wind Power Validation

Grid operators rely on historical weather time series at existing and planned wind power plants to make informed decisions when planning for a future power grid with very high penetration of renewable power. While synthetic wind power time series have been developed based on historical weather models, their validation with actual power production data remains complex due to variations in modeling practices and methodologies. This paper introduces the WE-Validate framework, originally designed for wind speed validation and now enhanced for wind power validation with a graphical user interface to support users with minimal programming experience. Validation of wind power with WE-Validate is based on robust metrics consisting of RMSE, centered RMSE, average bias, average percent bias, mean absolute error, mean absolute percent error, cross correlation, and calculation of ramping magnitude, rate, and duration. This paper showcases WE-Validate with validation of synthetically derived power for a wind plant in Washington state for one month in 2018. Validation of the synthetic power from two comparison data sets compared with observations shows both comparison series have strong correlation with observed across weekly and monthly aggregations while suffering from persistent negative bias. The suite of metrics within WE-Validate facilitates immediate insight into the utility of the comparison data sets through compression across multiple axes. This user-friendly, open-source tool can be extended beyond wind power, making it a valuable resource for system planners and operators in different domains.

Moncheur de Rieudotte, Malcolm P.↗
DOE OSTI2024

SRNL Nuclear Material Management Strategy

Savannah River National Laboratory (SRNL) is a multidisciplinary laboratory located on the Savannah River Site (SRS) that specializes in applying state-of-the-art science to provide practical solutions to complex technical problems. In 2021 SRNL went through a contract transition to become an independent Federally Funded Research and Development center which kicked off a period of rapid growth in research and an increased demand for the limited nuclear material capacity. A systematic process was developed for analyzing nuclear material holdings to optimize retention and streamline efforts to disposition legacy material without jeopardizing program execution. This process focused on utilizing the expertise of researchers to identify materials for disposition and retention while creating the visibility of tracking metrics for management to monitor material utilization. This resulted in a ~20% reduction in the powder Material At Risk (MAR) and identified additional candidates that could reduce transuranic holdings by an additional ~30% without endangering future program growth.

Ramsey, Catherine M.↗
DOE OSTI2026

State Technical Assistance - New Mexico Energy and Conservation Management Division Report [Slides]

The New Mexico Energy and Conservation Management Division (ECMD) sought technical assistance to enhance their ability to evaluate program impacts using the Low-Income Energy Affordability Data (LEAD) tool. NLR assisted ECMD in leveraging the LEAD tool to calculate and analyze energy burden across electric utility service areas, enabling them to assess program outcomes more effectively. To meet ECMD's goals, NLR developed a customized methodology to calculate utility-specific energy burden metrics using census tract data and available utility service area information from the Energy Information Administration (EIA). While acknowledging some limitations in the EIA dataset, NLR estimated the percentage of households within each service territory and incorporated relevant filters such as income, housing characteristics, and other demographics from the LEAD tool. The analysis provided ECMD with a new capability to evaluate program success based on energy savings, reductions in energy burden, and other performance indicators. The data and methodology also support discussions with utilities to improve the accuracy of service territory datasets. ECMD can use the outputs to track program effectiveness and plan future initiatives. NLR offered the possibility of follow-on work, including capacity-building for ECMD to repeat the analysis independently and the option to refine the analysis with updated service.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗