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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Use of MODIS Satellite Data to Evaluate Juniperus spp. Pollen Phenology to Support a Pollen Dispersal Model, PREAM, to Support Public Health Allergy Alerts

Juniperus spp. pollen is a significant aeroallergen that can be transported 200-600 km from the source. Local observations of Juniperus spp. phenology may not be consistent with the timing and source of pollen collected by pollen sampling instruments. Methods: The Dust REgional Atmospheric Model (DREAM)is a verified model for atmospheric dust transport modeling using MODIS data products to identify source regions and quantities of dust. We successfully modified the DREAM model to incorporate pollen transport (PREAM) and used MODIS satellite images to develop Juniperus ashei pollen input source masks. The Pollen Release Potential Source Map, also referred to as a source mask in model applications, may use different satellite platforms and sensors and a variety of data sets other than the USGS GAP data we used to map J. ashei cover type. MODIS derived percent tree cover is obtained from MODIS Vegetation Continuous Fields (VCF) product (collection 3 and 4, MOD44B, 500 and 250 m grid resolution). We use updated 2010 values to calculate pollen concentration at source (J. ashei ). The original MODIS derived values are converted from native approx. 250 m to 990m (approx. 1 km) for the calculation of a mask to fit the model (PREAM) resolution. Results: The simulation period is chosen following the information that in the last 2 weeks of December 2010. The PREAM modeled near-surface concentrations (Nm-3) shows the transport patterns of J. ashei pollen over a 5 day period (Fig. 2). Typical scales of the simulated transport process are regional.

Luvall, J. C.↗
NASA NTRS2017

Assessment of NASA Airborne Laser Altimetry Data Using Ground-Based GPS Data near Summit Station, Greenland

A series of NASA airborne lidars have been used in support of satellite laser altimetry missions. These airbornelaser altimeters have been deployed for satellite instrument development, for spaceborne data validation, and to bridge the data gap between satellite missions. We used data from ground-based Global Positioning System (GPS) surveys of an 11 km long track near Summit Station, Greenland, to assess the surface elevation bias and measurement precision of three airborne laser altimeters including the Airborne Topographic Mapper (ATM), the Land, Vegetation, and Ice Sensor (LVIS), and the Multiple Altimeter Beam Experimental Lidar (MABEL). Ground-based GPS data from the monthly ground-based traverses, which commenced in 2006, allowed for the assessment of nine airborne lidar surveys associated with ATM and LVIS between 2007 and 2016. Surface elevation biases for these altimeters over the flat, ice-sheet interior are less than 0.12 m, while assessments of measurement precision are 0.09 m or better. Ground-based GPS positions determined both with and without differential post-processing techniques provided internally consistent solutions. Results from the analyses of ground-based and airborne data provide validation strategy guidance for the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) elevation and elevation-change data products.

Summit Station↗
NASA NTRS1982

The performance of VLA as a telemetry receiver for Voyager planetary encounters

The Very Large Array (VLA) was proposed for use as a supplement to the Deep Space Network (DSN) for telemetry reception at Voyager 2 Uranus and Neptune encounters. The main problem with the use of VLA for telemetry is that it is not capable of producing a continuous stream of data. Gaps of one millisecond follow every 51 milliseconds of data. The effect of these millisecond gaps on coded telemetry is investigated. An upgapped system of the same aperture as the VLA would be capable of handling data rates of 38.4 kbps at Uranus encounter and 19.2 kbps at Neptune encounter. It is shown that VLA with (7, 1/2) convolutional coding (the baseline coding scheme for Voyager) will support a data rate of 10.8 kbps but not 19.2 kbps at both Uranus and Neptune. It is also shown that by implementing Voyager's concatenated Reed-Solomon/convolutional coding capability, data rates of 38.4 kbps and 19.2 kbps would be achievable at Uranus and Neptune respectively. Concatenation also offers a factor of 2 improvement in overall throughput.

Deutsch, L. J.↗
DOE OSTI2023

Carbon Storage Technical Viability Approach (CS TVA) Database

The Carbon Storage Technical Viability Approach (CS TVA) database was developed to support the implementation of the CS TVA Matrix to a national data availability assessment for technically viable carbon storage. This database leverages the efforts of multiple adjacent and overlapping databases by non-redundantly combining the databases into a single database along with additionally providing tags facilitating the CS TVA. The non-redundant aspect of the database permits an accurate assessment of the concentration of available data, aiding in spatial and categorical data gaps analysis relative to the individual CS TVA Matrix Components. Version 2.0 of the database is an expansion of Version 1.0. Version 2.0 was created to include additional data gathered to fill gaps in the existing data set. Downloading the CS TVA v2.0 database will result in two separate databases, the version 1.0 original .gdb, and a second addendum .gdb with the new data gathered, together these two databases make up v2.0. Please see the ReadMe file below for full details, metadata information, use disclaimer, and attributions.

Coal↗
DOE OSTI2025

WELLS Interactive Application

The Wellbore Exploration and Location Logistic System (WELLS) Interactive Application is an interactive tool to enable easy exploration and visualization of the living national wellbore database (WELLS Database (https://edx.netl.doe.gov/dataset/wells_database)). The tool and underlying database were created and are maintained by the National Energy Technology Laboratory (NETL), providing visualization of the more than six million public wellbore records from more than 65 authoritative state, federal, and tribal resources. The WELLS Interactive Application serves up wellbore data from oil, gas, underground injection, research, geothermal, geotechnical, groundwater, and other types of wells in a single, standardized, unified system. In addition to the surface location of these wells, the underlying database combines select key attributes for features such as well age, depth, and operating status. The system also provides users with references back to the original sources used in this unified platform. The underlying data can be accessed through the WELLS Database: https://edx.netl.doe.gov/dataset/wells_database Additional Information: The WELLS Interactive Application (formerly titled CO2-Locate) enables visualization and access to the public wellbore records through an intuitive web-based mapping tool. The WELLS Interactive Application was designed to help users visualize, query, analyze, and download wellbore records. Public wellbore points are included as a layer in the Map page, called Public Wells. Additionally, a multivariate hexagon grid summarizing well density from proprietary well data, called Well Density, is included to identify data gaps between the public and proprietary well data. Filtering functionalities in the tool allow these two layers to be spatially filtered by state, county, or basin as well as by status, type, true vertical depth, and spud year. The WELLS Interactive Application also contains a Near Me tool can be used to search and explore wellbore data within a user-defined distance of a specified location on the map, which can also be downloaded. The Query tool allows users to query the selected or filtered wells in the Public Wells layer and export the data. For additional information on these tool functionalities, see the help documentation on the About page of the tool. Notes for Consideration: The Well Density layer provided in this application is derived from proprietary wellbore data, the records of which do not always contain values for key features (status, type, true vertical depth, or spud year). Therefore, data might not be available when layers are queried for all filter combinations. Additionally, visualizing layers and applying filters may take additional time to load (i.e., draw on the map) due to the large size of the data.

ccs↗
DOE OSTI2022

Data Curation for Machine Learning Applied to Geothermal Power Plant Operational Data for GOOML: Geothermal Operational Optimization with Machine Learning: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach to physics-guided, data-centric machine learning. This framework has been used to develop digital twins that provide steamfield operators with operational environments to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management in real world applications. To create, test, and apply the GOOML framework, diverse time-series datasets spanning multiple years were sourced from various geothermal power plant components within several complex real-world geothermal operations. These operations are based in the United States and New Zealand and include a variety of technologies, end-uses and configurations, collectively covering nearly all relevant operating conditions for modern geothermal fields. Datasets were acquired from multiple sources to ensure that machine learning experiments generalized properly to various operating conditions. It was found that the data varied in quality, format, and completeness. To ensure consistency between the various datasets, a standardized data curation process was developed to reliably streamline data preparation. This paper will discuss best practices as learned from the GOOML data curation process which takes the following steps: 1) acquisition of large quantities of data from power plant operators, 2) digestion of data to gain an initial understanding of what is included, 3) data transformation, which includes converting the data into a standardized machine-readable format so that they can be visualized, quality checked, and cleaned, 4) quality assurance and quality control, involving identification of significant data gaps and apparent anomalies through mapping of data features to real world componentry via the GOOML historical model, followed by discussion with modelers and power plant operators to identify additional data needs and to resolve issues, 5) use in machine learning algorithms, and 6) repetition of steps one through five until all data needs are met and data are deemed suitable for producing trustworthy modeling results which may be disseminated, ideally along with the curated dataset. This iterative process is focused on improving the quality of the data rather than tuning machine learning model parameters and supports a shift towards data-centric AI as a means to improving real-world applicability of geothermal machine learning projects.

access↗
DOE OSTI2025

A model to assess Zircaloy’s mechanical property changes following a transient beyond critical heat flux

Maintaining the integrity of nuclear fuel rods is essential for ensuring public health and safety in nuclear power generation. During reactor operation, this integrity is confirmed by demonstrating compliance with established regulatory acceptance criteria. For moderate-frequency events, such as limiting transients and anticipated operational occurrences (AOOs), the current fuel integrity criterion is based on preventing boiling transition. This criterion assumes that prevention of boiling transition will prevent excessive cladding heating and, thus, fuel failure during normal operations. While conservative, this approach places significant constraints on core design, fuel cycle economics, and a plant’s ability to perform major power uprates, leading to suboptimal fuel utilization and inefficient carbon-free energy production. A more efficient approach could be achieved by revising the failure criterion to a material-specific limit rather than strictly preventing the boiling transition, since boiling transition per se is not a cause of fuel cladding failure. Here, as a result, a new licensing framework based on material properties, termed time-at-temperature (t@T), is needed. This approach would allow for brief periods of post–critical heat flux operation during an AOO without compromising safety. Implementing the t@T licensing strategy requires a robust technical foundation in material properties, which must be established through comprehensive data collection on both unirradiated and irradiated fuel and cladding materials. This foundation would enable the development of a safety basis that ensures safe operation while providing greater flexibility and efficiency for reactor operation. This paper documents a thorough review of the available data to establish a baseline knowledge that can inform the development of cladding mechanical models, as well as identify experimental data gaps that need to be addressed in future research. Machine learning and data informatics were utilized to extract the importance of parameters on the t@T parameter. Industry tools were used to perform baseline analyses to define the relevant transient conditions for data analysis. The subsequent review successfully identified applicable experimental data, as well as sufficient data to evaluate changes in cladding mechanical properties following an AOO transient. Rather than developing new models, this work coupled existing irradiation annealing and recrystallization models to calculate changes in hardness, yield stress, and ultimate tensile stress following an AOO event. The findings from this review were summarized to highlight the experimental data needs required to fill remaining gaps and support the development of future t@T licensing methodologies.

Cladding performance↗
NASA NTRS2004

The Distribution of Non-Volatile Elements on Mars: Mars Odyssey GRS Results

The major scientific objective of the Gamma-Ray Spectrometer (GRS) on the 2001 Mars Odyssey Mission is to determine the distribution of elements in the near-surface of Mars. Mars Odyssey has been in its mapping orbit since February, 2002, and the GRS boom, which removes the instrument from the gamma-ray background of the spacecraft, was erected in June, 2002. In the 580 days since boom erection, we have accumulated 453 days of mapping data. The difference is due mostly to two times when Odyssey went into safe mode and the instrument warmed up forcing us to anneal out radiation damage that manifests itself after warming. Other data losses are due to simple transmitter data gaps and to intense solar particle events. The data from the GRS is statistical in nature. We have a very low count rate and a very low signal-to-noise ratio. With the exception of K, the most easily mapped elements have a signal/noise ratio on the order of 0.1 (0.5 for K) and the counting rates are on the order of 0.3 to 0.7 counts/min (4 cpm for K). In order to map the distribution of an element, we have to divide the total signal from Mars up into many cells that define the map s spatial resolution (unless the statistics are good enough that the intrinsic spatial resolution of the instrument, about 550 km diameter, dominates). The data for several elements have now achieved a statistical precision that permits us to make meaningful maps.

Boynton, W.↗
DOE OSTI2022

Machine Learning Assisted Gap-Filled Discharge Data for the East River Community Watershed, Colorado, for Water Years 2014-2021

This dataset contains a collection of machine learning assisted gap-filled discharge data created for all discharge stations across the East River Watershed, Colorado. This data was generated by using raw discharge data collected by Rosemary Carroll, and conducting a random forest machine learning analysis to gap-fill discharge data across all years at the hourly time level. Discharge data with gaps creates problems for analysis of measured and modeled fluxes of carbon and nitrogen exported out of each sub-watershed. Gap-filled data is also required as an input to surface water models, which helps to address our main research question related to how snowmelt timing impacts the timing and magnitude of nitrogen exports. Data is provided in one csv file.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1988

VLA telemetry performance with concatenated coding for Voyager at Neptune

Current plans for supporting the Voyager encounter at Neptune include the arraying of the Deep Space Network (DSN) antennas at Goldstone, California, with the National Radio Astronomy Observatory's Very Large Array (VLA) in New Mexico. Not designed as a communications antenna, the VLA signal transmission facility suffers a disadvantage in that the received signal is subjected to a gap or blackout period of approximately 1.6 msec once every 5/96 sec control cycle. Previous analyses showed that the VLA data gaps could cause disastrous performance degradation in a VLA stand-alone system and modest degradation when the VLA is arrayed equally with Goldstone. New analysis indicates that the earlier predictions for concatenated code performance were overly pessimistic for most combinations of system parameters, including those of Voyager-VLA. The periodicity of the VLA gap cycle tends to guarantee that all Reed-Solomon codewords will receive an average share of erroneous symbols from the gaps. However, large deterministic fluctuations in the number of gapped symbols from codeword to codeword may occur for certain combinations of code parameters, gap cycle parameters, and data rates. Several mechanisms for causing these fluctuations are identified and analyzed. Even though graceful degradation is predicted for the Voyager-VLA parameters, catastrophic degradation greater than 2 dB can occur for a VLA stand-alone system at certain non-Voyager data rates inside the range of the actual Voyager rates. Thus, it is imperative that all of the Voyager-VLA parameters be very accurately known and precisely controlled.

Dolinar, S. J., Jr.↗
DOE OSTI2022

Detection of surface water temperature variations of Mongolian lakes benefiting from the spatially and temporally gap-filled MODIS data

Lakes provide critical water resources for human activities and ecosystems, particularly in the Mongolian Plateau (MP), which is characterized by a dry climate and a harsh environment. As a region that is sensitive to anthropogenic warming, tracking lake surface water temperature (LSWT) changes in Mongolian lakes is crucial for understanding the consequences of a warming climate on lake ecosystems. However, the long-term monitoring of LSWT is restricted by the spatiotemporal gaps in the raw imagery of remote sensing-based land surface temperature (LST), e.g., the commonly used Moderate Resolution Imaging Spectroradiometer (MODIS) LST products. This study applied an improved gap-filling method by utilizing the discrete cosine transform-based penalized least squares (DCT-PLS) strategy in the spatial domain combined with the linear interpolation (LI) algorithm in the temporal domain. The method was applied to fill gaps in the LSWT imagery of 12 representative lakes across MP. The randomly sampled high-quality MODIS LSWT values in the spatial and temporal domains were excavated as false data gaps and considered “virtual true” validation datasets. The spatial validation results showed that the estimated LSWT for all the lake cases were comparable with the “virtual true” LSWT values, with the average values of the coefficient of determination, mean absolute error, mean square error, and root mean square error being 0.98, 0.38 °C, 0.45 °C, and 0.59 °C, respectively. Meanwhile, the error of nighttime LSWT results was relatively lower than that of daytime LSWT. For temporal interpolation validation, the LI algorithm exhibited relatively better performance and could more objectively indicate the variation in LSWT. Benefiting from the spatially and temporally well-constrained data, we analyzed the interannual and intra-annual change characteristics of the LSWTs of the 12 lakes. The long-term variations of annual and seasonal mean LSWTs in the 12 selected lakes exhibited no evident trends in 2000–2020, while presented apparent interannual fluctuations. The slight changes in the average LSWTs of the 12 selected lakes were in excellent synchronization with the surrounding LST derived from the reanalysis datasets, confirming the widely reported phenomenon of “global warming hiatus” that occurred in the early 21st century. This study improves the understanding of the LSWT variations in Mongolian lakes in response to global climate change. It has the potential to provide an effective approach for monitoring LSWT changes in other large-scale studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Quantifying conditional probabilities of fish-turbine encounters and impacts

Tidal turbines are one source of marine renewable energy but development of tidal power is hampered by uncertainties in fish-turbine interaction impacts. Current knowledge gaps exist in efforts to quantify risks, as empirical data and modeling studies have characterized components of fish approach and interaction with turbines, but a comprehensive model that quantifies conditional occurrence probabilities of fish approaching and then interacting with a turbine in sequential steps is lacking. We combined empirical acoustic density measurements of Pacific herring ( Clupea pallasii ) and when data limited, published probabilities in an impact probability model that includes approach, entrainment, interactions, and avoidance of fish with axial or cross-flow tidal turbines. Interaction impacts include fish collisions with stationary turbine components, blade strikes by rotating blades, and/or a collision followed by a blade strike. Impact probabilities for collision followed by a blade strike were lowest with estimates ranging from 0.0000242 to 0.0678, and highest for blade strike ranging from 0.000261 to 0.40. Maximum probabilities occurred for a cross-flow turbine at night with no active or passive avoidance. Estimates were lowest when probabilities were conditional on sequential events, and when active and passive avoidance was included for an axial-flow turbine during the day. As expected, conditional probabilities were typically lower than analogous independent events and literature values. Estimating impact probabilities for Pacific herring in Admiralty Inlet, Washington, United States for two device types illustrates utilization of existing data and simultaneously identifies data gaps needed to fully calculate empirical-based probabilities for any site-species combination.

collision risk↗
NASA NTRS2011

Historic Emissions from Deforestation and Forest Degradation in Mato Grosso, Brazil: 1. Source Data Uncertainties

Historic carbon emissions are an important foundation for proposed efforts to Reduce Emissions from Deforestation and forest Degradation and enhance forest carbon stocks through conservation and sustainable forest management (REDD+). The level of uncertainty in historic carbon emissions estimates is also critical for REDD+, since high uncertainties could limit climate benefits from mitigation actions. Here, we analyzed source data uncertainties based on the range of available deforestation, forest degradation, and forest carbon stock estimates for the Brazilian state of Mato Grosso during 1990-2008. Results: Deforestation estimates showed good agreement for multi-year trends of increasing and decreasing deforestation during the study period. However, annual deforestation rates differed by >20% in more than half of the years between 1997-2008, even for products based on similar input data. Tier 2 estimates of average forest carbon stocks varied between 99-192 Mg C/ha, with greatest differences in northwest Mato Grosso. Carbon stocks in deforested areas increased over the study period, yet this increasing trend in deforested biomass was smaller than the difference among carbon stock datasets for these areas. Conclusions: Patterns of spatial and temporal disagreement among available data products provide a roadmap for future efforts to reduce source data uncertainties for estimates of historic forest carbon emissions. Specifically, regions with large discrepancies in available estimates of both deforestation and forest carbon stocks are priority areas for evaluating and improving existing estimates. Full carbon accounting for REDD+ will also require filling data gaps, including forest degradation and secondary forest, with annual data on all forest transitions.

Morton, Douglas C.↗
DOE OSTI2026

Imputation of urban environmental sensor data using gated attention bidirectional long short-term memory (GA-BiLSTM): methods, performance, and implications

Urban environmental monitoring networks frequently encounter significant data gaps due to sensor malfunctions, environmental disturbances, and communication failures. Reliable approaches to address these gaps are essential for ensuring the continuity and quality of environmental data streams. In this study, we developed a gated attention bidirectional long short-term memory (GA-BiLSTM) model to impute missing data in a dense urban monitoring network. Using observations from the CROCUS network in Chicago, we evaluated GA-BiLSTM against widely used approaches (XGBoost and K-nearest neighbors) under scenarios of both short-term intermittent gaps and prolonged outages. GA-BiLSTM consistently outperformed comparative methods, particularly during extended outages of up to ten days, demonstrating its ability to capture spatiotemporal dependencies across sensor nodes. Beyond performance metrics, feature importance and spatial network analyses highlighted the unexpected but critical predictive role of peripheral rural nodes, underlining their strategic value for maintaining robust urban monitoring systems. These results emphasize that advanced imputation methods can substantially improve the reliability of environmental monitoring networks and support more resilient data infrastructures for urban sustainability.

Data imputation↗
DOE OSTI2021

Surface Meteorology at Teller Mile 47 Watershed, Seward Peninsula, Alaska, Ongoing from 2018

Meteorological data are currently being collected at one location at the Teller Mile 47 (TL_MM47) Research Basin Site, Seward Peninsula (N64 58' 36.918", W166 12' 32.67", 67 meters above sea level). The site was installed and initial measurements started in September 2018 and it has operated continuously since then. The meteorological station is co-located with a continuous snow depth sensor and two soil pits for subsurface temperature and moisture measurements. These data are being collected to better understand the energy dynamics above the active layer and permafrost. They complement in-situ snow and soil measurements also at this location. The data could also be used as supporting measurements for other research and modeling activities. There are 35 comma separated value format (*.csv) files provided, where each file contains the full data for an individual parameter (e.g. air temperature at 1.5 meters above the ground surface (teller_m47_air_temperature_150cm_ags_Avg.csv) or soil temperature 20 centimeters below ground surface (teller_m47_dry_soil_pit_temperature_20cmbgs_Avg.csv)) plus the time in Universal Coordinated Time (UTC) and Alaska Standard Time (UTC time minus nine hours). The site was installed and initial measurements started in September 2018. It has been operated continuously since. Primary data gaps are due to battery failure or sensor failure. These data are being collected to better understand the surface energy dynamics above the active layer and permafrost. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy?s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy?s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1997

Arctic and Antarctic Sea Ice Concentrations from Multichannel Passive-Microwave Satellite Data Sets: October 1978-September 1995 User's Guide

Satellite multichannel passive-microwave sensors have provided global radiance measurements with which to map, monitor, and study the Arctic and Antarctic polar sea ice covers. The data span over 18 years (as of April 1997), starting with the launch of the Scanning Multichannel Microwave Radiometer (SMMR) on NASA's SeaSat A and Nimbus 7 in 1978 and continuing with the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI) series beginning in 1987. It is anticipated that the DMSP SSMI series will continue into the 21st century. The SSMI series will be augmented by new, improved sensors to be flown on Japanese and U.S. space platforms. This User's Guide provides a description of a new sea ice concentration data set generated from observations made by three of these multichannel sensors. The data set includes gridded daily ice concentrations (every-other-day for the SMMR data) for both the north and south polar regions from October 26, 1978 through September 30, 1995, with the one exception of a 6-week data gap from December 3, 1987 through January 12, 1988. The data have been placed on two CD-ROMs that include a ReadMeCD file giving the technical details on the file format, file headers, north and south polar grids, ancillary data sets, and directory structure of the CD-ROM. The CD-ROMS will be distributed by the National Snow and Ice Data Center in Boulder, CO.

Sea ice concentration↗
DOE OSTI2022

On the thermal oxidation of nuclear graphite relevant to high-temperature gas cooled reactors

Thermal oxidation of nuclear graphite components is highly undesirable because it can cause structural and property degradation that negatively affect a reactor's intended operation. In this work, the body of knowledge of nuclear graphite oxidation is highlighted, including when O 2 , H 2 O, and/or CO 2 are the oxidant. Oxidation conditions relevant to high-temperature gas-cooled reactors (i.e., when oxidation could occur either as an acute or chronic phenomenon) are emphasized. Here, the objective is to summarize graphite oxidation data in a practical and accessible way to inform future research and regulatory requirements. Although each grade of nuclear graphite is different, the oxidation mechanism has underlying commonalities. Although oxidation behavior is grade dependent, the general temperature dependence is well described by a sequence of elementary steps which become rate limiting. Because the regime transition temperature depends on sample microstructure, size, and oxidant supply rate, extrapolating results beyond the experimental range should be done cautiously. Gravimetric oxidation rate measurements generally replicate well. However, caution must be exercised when rates are estimated by other methods or for samples that deviate significantly in size. Air oxidation data for IG-110, NBG-18, and PCEA graphite is critically reviewed to emphasize this point. Despite the amount of experimental data, gaps remain. Sample size and shape effects are not fully explained. Data on the oxidant penetration depth are insufficient. Analytical assessments demonstrate that lower temperature oxidation does not necessarily imply that oxidation is uniform throughout the bulk. Oxidation occurs faster at higher temperature but is more localized to the exposed surface. Paradoxically, at equal mass loss percentage, low temperature oxidation leads to greater property degradation than at high temperature. The isolated effect of oxidation is important; however, a gap remains in the systematic understanding of any potential effect of neutron irradiation on graphite structure and reactivity.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A three-year dataset supporting research on building energy management and occupancy analytics

Abstract This paper presents the curation of a monitored dataset from an office building constructed in 2015 in Berkeley, California. The dataset includes whole-building and end-use energy consumption, HVAC system operating conditions, indoor and outdoor environmental parameters, as well as occupant counts. The data were collected during a period of three years from more than 300 sensors and meters on two office floors (each 2,325 m 2 ) of the building. A three-step data curation strategy is applied to transform the raw data into research-grade data: (1) cleaning the raw data to detect and adjust the outlier values and fill the data gaps; (2) creating the metadata model of the building systems and data points using the Brick schema; and (3) representing the metadata of the dataset using a semantic JSON schema. This dataset can be used in various applications—building energy benchmarking, load shape analysis, energy prediction, occupancy prediction and analytics, and HVAC controls—to improve the understanding and efficiency of building operations for reducing energy use, energy costs, and carbon emissions.

24 POWER TRANSMISSION AND DISTRIBUTION↗