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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Femtosecond X-ray Spectroscopy Directly Quantifies Transient Excited-State Mixed Valency

Quantifying charge delocalization associated with short-lived photoexcited states of molecular complexes in solution remains experimentally challenging, requiring local element specific femtosecond experimental probes of time-evolving electron transfer. In this study, we quantify the evolving valence hole charge distribution in the photoexcited charge transfer state of a prototypical mixed valence bimetallic iron–ruthenium complex, [(CN) 5 Fe II CNRu III (NH 3 ) 5 ]-, in water by combining femtosecond X-ray spectroscopy measurements with time-dependent density functional theory calculations of the excited-state dynamics. We estimate the valence hole charge that accumulated at the Fe atom to be 0.6 ± 0.2, resulting from excited-state metal-to-metal charge transfer, on an ~ 60 fs time scale. Our combined experimental and computational approach provides a spectroscopic ruler for quantifying excited-state valency in solvated complexes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

NLR HPC Kestrel Jobs Data

Overview: Anonymized job-level records from the Kestrel HPC system at the National Laboratory of the Rockies (NLR). Each record represents a Slurm batch job with scheduling metadata, resource requests, utilization, energy estimates, and efficiency metrics. Sensitive fields (user, account, job name, submit line, working directory, submit script, and job type) are replaced with 7-character cryptographic hashes. System & Timeframe: Kestrel is located at the NLR campus. Standard compute nodes have 104 cores and 256 GB RAM; bigmem nodes have 2,000 GB. GPU nodes (gpu-h100 partition) use NVIDIA H100 GPUs. Data covers jobs submitted August 2023 through December 2025. Funding provided by the U.S. Department of Energy, EERE. Files: esif.hpc.kestrel.job-anon.zip — Anonymized job records (Hive-partitioned Parquet) datacard.md — Full dataset documentation ~11 million rows, 50 variables. Readable with PyArrow, pandas, DuckDB, Apache Spark, or any Parquet-compatible tool. Data Collection: Jobs collected via sacct with timezone-aware export (SLURM_TIME_FORMAT="%Y-%m-%dT%H:%M:%S%z"), loaded into PostgreSQL. Calculated columns updated via database triggers and batch functions. All timestamps use timestamptz and correctly handle DST transitions. Preprocessing: Anonymization of name, user, account, submit_line, work_dir, submit_script, and job_type via 7-char hex hashes Derived columns: queue_wait, cpu_eff, max/min/avg_mem_eff, energy estimates Simplified job state mapping (e.g., "CANCELLED by 132357" → "CANCELLED") Boolean flags: python_job, reframe_job Temporal decomposition: year, month, day, day_of_week, hour, minute from submit_time Shared node tracking: shared_job_count, nodes_shared, jobs_shared Key Variables: Scheduling: job_id, partition, state_simple, submit_time, start_time, end_time, queue_wait Resources: nodes_req/used, processors_req/used, memory_req, wallclock_req/used, gpus_requested Efficiency: cpu_eff, max/min/avg_mem_eff Energy: cpu_energy_tdp_estimated_max/used_watt_hours, consumed_energy_raw_joules, consumed_energy_raw_watt_hours Sharing: shared_job_count, nodes_shared, jobs_shared Partitions: short, standard, debug, gpu-h100 Job States: CANCELLED, COMPLETED, FAILED, PENDING, RUNNING QoS Levels: normal, high Important Notes: Timestamps include timezone offsets; DST transitions are handled correctly, though adding intervals across DST boundaries requires offset adjustment shared_job_count reflects physical node co-residency, not use of the shared partition Job step records and raw Slurm JSONB fields are excluded Do not attempt to re-identify individuals from hashed fields

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

National and regional waste stream in the United States: conformance and disparity

Accurate estimation of municipal solid waste (MSW) composition is critical for efficient waste management. In the United States, site-specific and material flow approaches determine the MSW composition at regional and national levels. The material flow-based national estimates are determined by the US EPA; the US EPA’s estimates are known to differ substantially from the aggregated tonnage of MSW managed by waste handling facilities in the United States. However, the material class-specific discrepancies of the US EPA’s material flow approach resulting in these differences are unknown. To find the basis of these discrepancies, we analyze the discarded MSW stream of 27 US states, which roughly accounts for 73 percent of the US population. Our analysis indicates that the material flow-based national estimates are accurate for the food, plastic, and glass material classes. In contrast, we find that the US EPA’s material flow-based predictions underestimate paper waste disposal by at least 15 million tons annually. These differences likely stem from incorrect assumptions of residence time. These results highlight the material class-specific strengths and drawbacks of the US EPA’s material flow-based MSW estimates.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Impacts of Distributed Speed Harmonization and Optimal Maneuver Planning on Multi-Lane Roads

A commonly proposed method for improving traffic flow on freeways is speed harmonization. The effectiveness of current speed harmonization approaches, such as variable speed limits, is extremely reliant on human driver compliance. Connected and automated vehicles (CAVs) are expected to come to market within this decade, offering the opportunity to eliminate or reduce the reliance on human compliance. However, extending current roadside infrastructure-based approaches of assigning centrally computed harmonization speeds to individual vehicles is, costly. An alternative approach is to have individual vehicles estimate the traffic state on-board and make distributed decisions to achieve the harmonization goal autonomously. In this work, we present a distributed algorithm for estimating the current average speed of traffic. We couple this with a distributed 2D maneuver planning approach. Then, we study the impact on traffic efficiency in terms of energy consumption and travel time at varying CAV penetration rates.

Goulet, Nathan↗
DOE OSTI2020

Improving qubit readout with hidden Markov models

We demonstrate the application of pattern recognition algorithms via hidden Markov models (HMM) for qubit readout. This scheme provides a state-path trajectory approach capable of detecting qubit-state transitions and makes for a robust classification scheme with higher starting-state assignment fidelity than when compared to a multivariate Gaussian or a support vector machine scheme. Therefore, the method also eliminates the qubit-dependent readout time optimization requirement in current schemes. Using a HMM state discriminator we estimate fidelities reaching the ideal limit. Unsupervised learning gives access to transition matrix, priors, and IQ distributions, providing a toolbox for studying qubit-state dynamics during strong projective readout.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Future Wind Energy Resources and Cost Uncertainties Across the United States

This dataset contains results estimating projections of change of annual capacity factors and levelized cost of energy for several turbine technologies in the 2024 Annual Technology Baseline (ATB). Projections of change are based on downscaled earth system model (ESM) data from Sup3rCC. There has been evidence of reductions in average wind speeds over land in North America since the 1980s, and several models project that average wind speeds will continue to decrease. Concurrently, the cost of wind energy systems in the United States has been decreasing since around 2010, a trend also projected to continue. There is considerable uncertainty in these future projections, with quantitative estimates of future wind resource and system costs varying widely. To study this, we run land-based wind energy models with a range of possible future system costs, turbine designs, and meteorological inputs from multiple downscaled earth system models over the contiguous United States to estimate critical system performance metrics such as annual energy production (AEP) and levelized cost of energy. Where multiple earth system models agree, changes in mean AEP from the time period 2000-2019 to 2040-2059 can be as high as +10% in South Texas or as low as -20% in Iowa. Several additional states in the Midwest that currently have considerable wind generation capacity show the possibility of substantial decreases in AEP by mid-century. Larger turbines and moderate reductions in system costs can offset even the largest projected decreases in wind resource, but much uncertainty remains in the extent to which wind resources will actually change into the future and to what extent wind energy systems can drive down future costs. An analysis of variance shows, in several states in the Midwest, the uncertainty in future wind resource can be almost as important for future changes in the cost of wind energy as the uncertainty in future system costs.

17 WIND ENERGY↗
DOE OSTI2026

Electronic Waste (E-Waste) Recycling: Barriers and Opportunities for State, Local, and Tribal Governments

Electronic waste, or e-waste, encompasses end-of-life electrical and electronic equipment, including computers, monitors, televisions, printers, cell phones, household appliances with electronic components, and small consumer devices containing batteries or circuitry. E-waste is one of the fastest-growing components of the U.S. waste stream, with nearly 8 million tons generated in 2022. E-waste presents two related public-interest considerations. First, e-waste contains economically and strategically important materials: base metals (copper, aluminum, iron, steel), precious metals (gold, silver, platinum group metals), and critical materials such as rare earth elements, gallium, and indium that appear on the U.S. Geological Survey's (USGS) 2025 List of Critical Minerals. Second, many electronic devices contain hazardous constituents - including lead, mercury, cadmium, and brominated flame retardants - as well as rechargeable batteries that can pose fire risks during handling. Effective management of these constituents helps protect workers, communities, and the environment. In the United States, it is estimated that less than 20% of e-waste is formally collected and recycled, leaving over 6 million tons landfilled or otherwise unrecovered. This fact sheet provides state and local decision-makers an overview of (1) e-waste composition and recoverable materials, (2) technology pathways used to recover those materials, and (3) examples of operational and partnership structures used to provide e-waste recycling capacity in the United States. It is intended to complement existing collection-program guidance available through U.S. Environmental Protection Agency (EPA) and other sources.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

INGENIOUS Thermal Conductivity Measurement Source Categorization

Thermal conductivity (TC) data taken for different wells at a specified drill depth. This is an abridged version of the complete SMU heat flow database, downloaded from the SMU node of the NGDS at the beginning of INGENIOUS (approximately April 2021), and filtered to the INGENIOUS study area. This National Geothermal Data System (NGDS) project aggregates geothermal data collected and curated by the SMU Geothermal Laboratory and its partner organizations. All columns in this database are the same as the SMU database, except for 2 additions associated with this project. Repeated columns are for data correlation purposes. Column descriptions and data types are the same as previous iterations of the SMU database. The new values that are the addition are two new columns developed as part of the INGENIOUS project: INGENIOUS TC Value | INGENIOUS notes INGENIOUS notes are individual notes that were written for specific data points during the analysis process. There are not always notes associated with each input value. INGENIOUS TC Value includes 4 values: 1. Assumed Measured These are values that are assumed to be measured thermal conductivity values, either within a specific well or within the same study region. Many of these have either a published reference, a reported standard deviation, or a unique thermal conductivity value. 2. Data release - assumed measured These are values in the SMU database that are from proprietary data that were added to the SMU database and are labeled as data release for their reference. These values were searched for in person at the SMU Geothermal Laboratory as well as virtual examination of data available on the NGDS. For many of these, there are reported thermal conductivity values associated with the heat flow data in the database, but no specific table or reference to measurements in the original data release files. 3. Known measured These are values that have a reported measurement, either as an original file in the SMU data files on the NGDS or a reported table in a publication. In the rare circumstances, Maria Richards or David Blackwell confirmed measurement. Confirmation of measurement would be written in the INGENIOUS notes column. 4. Unmeasured Unmeasured values are those that are known to be unmeasured, either estimated from another report or no information given. In the SMU database, there are wells that have a heat flow but no thermal conductivity. These are categorized as unmeasured. There are also heat flow values that are stated to have estimated or generalized average thermal conductivity values for the region and rock type. Because these are known to be unmeasured, they are categorized as such. 5. Blank Blank values are either A quality or X quality. These quality values are stated in the INGENIOUS notes. These values were not going to change associated with the heat flow analysis, so these were not examined.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Photosynthesis phenology, as defined by solar-induced chlorophyll fluorescence, is overestimated by vegetation indices in the extratropical Northern Hemisphere

Vegetation phenology is highly sensitive to climate change, although the data and methods used to estimate key phenological states can influence this sensitivity. Because of its direct relation to leaf photosynthetic carbon uptake, remotely sensed solar-induced chlorophyll fluorescence (SIF) can provide new insight assessing changes in vegetation phenology. In this work, we investigated the potential of using a SIF time series product named contiguous SIF (CSIF) to estimate spring, summer, and autumn phenology in the extratropical Northern Hemisphere (>30°N) and compared the results with those based on Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) for the period 2001–2018. Overall, we found similar spatial patterns in phenological states. However, specific dates of key phenological events differed when using CSIF vs. MODIS NDVI data. NDVI data indicated that the growing season started earlier (by 10.1 days on average) and ended later (11.5 days on average) relative to CSIF data. This implies that actual periods for photosynthetic activity are shorter (by 21.6 days on average) than those estimated from vegetation indices more directly related to changes in canopy structure. These large differences between results from NDVI and that from CSIF suggest that vegetation indices such as NDVI seem to overestimate the period for active photosynthesis over the extratropical Northern Hemisphere. Furthermore, while phenology of the early growing season is dominated by temperature for both NDVI and CSIF data, phenology of the late growing season is mainly controlled by temperature for NDVI but by precipitation for CSIF. Our findings were further confirmed by other SIF (GOME-2 SIF) and vegetation index (MODIS EVI) datasets. Phenology modes in Earth system modelling are often parameterized using leaf unfolding and senescence from either station or satellite observations. Our results imply that canopy structure-based parameterization schemes may have overestimated photosynthesis active period, and thus productivity responses. We conclude that SIF data offers a novel and unique approach for assessing phenological change - one that is more directly tied to the carbon cycle and how it is being influenced by climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Modeling the Air Quality Impacts of Future Energy Scenarios

Many greenhouse gas (GHG) emission reduction measures achieve simultaneous reductions in air pollutants. Human-Earth system models can estimate such emission changes in the energy system but using them in chemistry-transport models (CTMs) to study their air quality impacts involves resource-intensive emissions processing. This is greatly simplified by an emissions scaling approach linking state-level emissions estimated by a human-Earth system model to a CTM. A scenario continuing pre-2022 energy policy in the U.S. to 2050 shows widespread air quality improvements over the 2015 baseline from SO 2 and NO x emission reductions of 50–80% from electricity generation and light-duty vehicles. Scenarios of GHG mitigation and vehicle electrification at the state and national level add further benefits. However, PM 2.5 increases from increased use of wood heating and bioenergy suggest that additional PM 2.5 management may be needed when using biofuels. In conclusion, this approach helps assess multiple future energy scenarios efficiently without sacrificing chemical detail in the air quality simulations.

air quality↗
DOE OSTI2021

Accurate Effective Stress Measures: Predicting Creep Life for 3D Stresses Using 2D and 1D Creep Rupture Simulations and Data

Operating structural components experience complex loading conditions resulting in 3D stress states. Current design practice estimates multiaxial creep rupture life by mapping a general state of stress to a uniaxial creep rupture correlation using effective stress measures. The data supporting the development of effective stress measures are nearly always only uniaxial and biaxial, as 3D creep rupture tests are not widely available. This limitation means current effective stress measures must extrapolate from 2D to 3D stress states, potentially introducing extrapolation error. In this work, we use a physics-based, crystal plasticity finite element model to simulate uniaxial, biaxial, and triaxial creep rupture. Here, we use the virtual dataset to assess the accuracy of current and novel effective stress measures in extrapolating from 2D to 3D stresses and also explore how the predictive accuracy of the effective stress measures might change if experimental 3D rupture data was available. We confirm these conclusions, based on simulation data, against multiaxial creep rupture experimental data for several materials, drawn from the literature. The results of the virtual experiments show that calibrating effective stress measures using triaxial test data would significantly improve accuracy and that some effective stress measures are more accurate than others, particularly for highly triaxial stress states. Results obtained using experimental data confirm the numerical findings and suggest that a unified effective stress measure should include an explicit dependence on the first stress invariant, the maximum tensile principal stress, and the von Mises stress.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The Role of Interface Band Alignment in Epitaxial SrTiO 3 /GaAs Heterojunctions

Recent concerns surrounding climate change and the contribution of fossil fuels to greenhouse gas (GHG) emissions have sparked interest and advancements in renewable energy sources including wind, solar, and hydroelectricity. These energy sources, often referred to as “clean energy”, generate no operational onsite GHG emissions. They also offer the potential for clean hydrogen production through water electrolysis, presenting a viable solution to create an environmentally friendly alternative energy carrier with the potential to decarbonize industrial processes reliant on hydrogen. To conduct a full life cycle analysis, it is crucial to account for the embodied emissions associated with renewable and nuclear power generation plants as they can significantly impact the GHG emissions linked to hydrogen production and its derived products. In this work, we conducted a comprehensive analysis of the embodied emissions associated with solar photovoltaic (PV), wind, hydro, and nuclear electricity. We investigated the implications of including plant-embodied emissions in the overall emission estimates of electrolysis hydrogen production and subsequently on the production of synthetic ammonia, methanol, and Fischer– Tropsch (FT) fuels. Results show that average embodied GHG emissions of solar PV, wind, hydro, and nuclear electricity generation in the United States (U.S.) were estimated to be 37, 9.8, 7.2, and 0.3 g CO 2 e/kWh, respectively. Life cycle GHG emissions of electrolytic hydrogen produced from solar PV, wind, and hydroelectricity were estimated as 2.1, 0.6, and 0.4 kg of CO 2 e/kg of H 2 , respectively, in contrast to the zero-emissions often used when the embodied emissions in their construction were excluded. Average life cycle emission estimates (CO 2 e/kg) of synthetic ammonia, methanol, and FT-fuel from solar PV electricity are increased by 5.5, 16, and 49 times, respectively, compared to the case when embodied emissions are excluded. This change also depends on the local irradiance for solar power, which can result in a further increase of GHG emissions by 35–41% in areas of low irradiance or reduce GHG emissions by 21–25% in areas with higher irradiance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Redox chemistry of plutonium and plutonium surrogates in vitrified nuclear wastes

Lanthanide borosilicate glasses containing Pu and the analog element, Ce, were subjects of an X-ray absorption spectroscopy investigation to quantify the +3/+4 ratio of the redox-sensitive element. The data show that the dominant oxidation states are +4 for Pu and +3 for Ce. The data also indicate that the reductive potential of glasses can be quantified, although allowances must be made for glass composition, from a solution chemistry method adapted to glass chemistry. These data can therefore be used to formulate glass compositions and processing schedules that lead to knowledge of the oxidation state of Pu in melts. Furthermore, the data show that Ce is a poor analog for Pu behavior in melt and that the suitability of surrogates can be evaluated by this method. The methods demonstrated in this paper can be used to estimate the oxidation states of a range of multi-valent elements as functions of temperature and composition with data from only a single redox couple.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Utah FORGE 2-2404: Application of Advanced Techniques for Determination of Reservoir-Scale Stress State at Utah FORGE - Workshop Presentation

This is a presentation on the Application of Advanced Techniques for Determination of Reservoir-Scale Stress State at Utah FORGE project by the University of Oklahoma, presented by Dr. Ahmad Ghassemi, McCasland Chair Professor. The project's objective was to develop a methodology for the determination of reservoir-scale in-situ stress and use it to estimate the stress state at Utah FORGE. This presentation was featured in the Utah FORGE R&D Annual Workshop on September 7, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Exceedance Probabilities and Recurrence Intervals for Extended Power Outages in the United States

This report provides estimates of recurrence intervals and conditional exceedance probabilities for major power outages by U.S. region between 2015 and 2021. Additionally, we provide estimates for grid management, particularly outages caused by California’s public safety power shutoffs (PSPS), and for natural outages caused by major hurricanes. Outage recurrence intervals are the average number of years between outage events, and conditional exceedance probabilities are the likelihoods that a customer who experiences a major power outage will experience an outage exceeding a given duration. Major outage events are those that affect 10,000 or more customers, as defined by the U.S. Department of Energy’s (DOE’s) Electric Emergency Incident and Disturbance Report, called OE-417 (DOE 2020). These results can be applied to determine the likelihood of experiencing long-duration outages, which can be integrated into cost-benefit analyses of resilience solutions and broader energy resilience studies.

Ericson, Sean↗
DOE OSTI2022

A Simple Program to Estimate the Fast Modular Reactor Steady-State Closed Brayton Cycle Operating Parameters

The Fast Modular Reactor (FMR) is a gas-cooled fast reactor (GFR) that produces a net electricity output of 50 MW with helium-driven closed Brayton cycle. The power plant includes reactor system and power conversion unit (PCU). The reactor system produces fission energy that is transferred by helium flow. The PCU consists of a turbine, two compressors, and three heat exchanges. More specifically, the recuperator is a counter-flow energy recovery heat exchanger. The precooler and intercooler are the heat sinks. To analyze and understand the sensitivity of plant performance to the design parameters, a numerical program was written to calculate the plant operating parameters represented by the helium temperature, pressure, mass flow, and the heat sink temperatures. The program was developed for the steady-state operating condition. etc.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Online Parameter Estimation Methods for Adaptive Cruise Control Systems

Modeling Adaptive Cruise Control (ACC) vehicles enables the understanding of the impact of these vehicles on traffic flow. In this work, two online methods are used to provide real time system identification of ACC enabled vehicles. The first technique is a recursive least squares (RLS) approach, while the second method solves a nonlinear joint state and parameter estimation problem via particle filtering (PF). We provide a parameter identifiability analysis for both methods to analytically show that the model parameters are not identifiable using equilibrium driving. The accuracy and computational runtime of the online methods are compared to a commonly used offline simulation-based optimization (i.e., batch optimization) approach. The methods are tested on synthetic data as well as on empirical data collected directly from a 2019 model year ACC vehicle using data from sensors that are part of the stock ACC system. The online methods are scalable and provide comparable accuracy to the batch method. RLS runs in real time and is two orders of magnitude faster than the batch method for modest sized (e.g., 15 min) datasets. The particle filter also runs in real- time, and is also suitable in streaming applications in which the datasets can grow arbitrarily large.

33 ADVANCED PROPULSION SYSTEMS↗