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At least 91 records · Page 5

Astrometric microlensing probes of the isolated neutron star population with Roman

Notoriously hard to detect and study, isolated neutron stars (NSs) might provide valuable answers to fundamental questions about stellar evolution and explosion physics. With the upcoming Roman Space Telescope, scheduled for launch in 2026, a new and powerful channel for their detection will become available: astrometric microlensing. We set out to create a realistic sample of simulated gravitational microlensing events as observed by Roman with the Galactic Bulge Time Domain Survey. We focus in particular on the population of NS lenses, which has until now been largely understudied. We used dedicated Galactic models tailored for application to microlensing by compact objects. In addition to populations of stars, white dwarfs, and black holes, we simulated four different NS populations with Maxwellian natal kick distributions: v = (150, 250, 350, 450) km/s. For each simulation, we applied projected Roman precision, cadence, and detectability criteria. We found that the parameter space log10 t E –log 10 θ E , which will be accessible to Roman observations, is efficient for the classification of stellar remnants. We found a feature in this space that is characteristic of NSs; using this feature, optimal samples of NS candidates can be constructed from Roman -like datasets. We describe the dependence of the observable parameter distributions on the assumed mean kick velocities. As the effects of natal kicks are very complex and mutually counteracting, we suggest that more detailed studies focused on the dynamics of NSs are needed in anticipation of Roman and future surveys. We estimate that Roman will observe approximately 11 000 microlensing events, including ~100 with NS lenses, whose photometric and astrometric signals are detectable; the event yield decreases by 38% when gap-filling low-cadence observations are not included. We make all simulated microlensing event datasets publicly available in preparation for Roman data.

79 ASTRONOMY AND ASTROPHYSICS↗

Enhancing fire emissions inventories for acute health effects studies: integrating high spatial and temporal resolution data

Daily fire progression information is crucial for public health studies that examine the relationship between population-level smoke exposures and subsequent health events. Issues with remote sensing used in fire emissions inventories (FEI) lead to the possibility of missed exposures that impact the results of acute health effects studies. This paper provides a method for improving an FEI dataset with readily available information to create a more robust dataset with daily fire progression. High temporal and spatial resolution burned area information from two FEI products are combined into a single dataset, and a linear regression model fills gaps in daily fire progression. The combined dataset provides up to 71% more PM 2.5 emissions, 69% more burned area, and 367% more fire days per year than using a single source of burned area information. The FEI combination method results in improved FEI information with no gaps in daily fire emissions estimates. The combined dataset provides a functional improvement to FEI data that can be achieved with currently available data.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

Extending the Nuclide Inventory Validation Basis for High-Burnup Fuel with New Radiochemical Assay Data

Efforts are underway at Oak Ridge National Laboratory to improve the nuclide inventory validation basis for spent nuclear fuel at high burnups. Recently conducted radiochemical assay experiments provided new measurement data for nine samples of fuel irradiated in a pressurized water reactor, with estimated sample burnups in the 30 to 70 GWd/t range. This type of destructive assay data is essential for validating computational methods, tools, and nuclear data applied in nuclear safety analyses and for improving our understanding of the bias and uncertainty in code predictions. The measurement data include key actinides and fission products that span a gamut of needs and interests for nuclear science and engineering applications in criticality safety, reactor physics, nuclide inventory, decay heat, and radiation shielding. The SCALE 6.3 code system with ENDF/B-VII.1 cross-section libraries was used to simulate the irradiation histories of the measured fuel samples. The calculated nuclide concentrations are compared to corresponding measurement data. The significance of the comparisons is discussed, emphasizing how the addition of the new measurement data fills gaps in the validation basis at high burnups and contributes to the decrease in bias and uncertainty for predicted nuclide concentrations. The discussion addresses the effect of the sample burnup used in the simulation—which is based on reactor operator records or on calibration to measured data for burnup indicator fission products—on the validation results.

Nuclide inventory↗

Spurious solar-wind effects on acceleration noise in LISA Pathfinder

Spurious solar-wind effects are a potential noise source in future Laser Interferometer Space Antenna (LISA) measurements. One noise coupling mechanism is constrained by estimating solar-wind effects on acceleration noise in LISA Pathfinder (LPF). While LISA is designed for drag-free differential measurement, predicting the realistic impact both bounds the operational environment and assesses whether LISA could provide serendipitous space-weather observations. Data from NASA's Advanced Composition Explorer (ACE), situated at the L1 Lagrange point, serves as a reliable source of solar-wind data. The data sets are compared over the 114 d time period from 1 March 2016 to 23 June 2016. This period gives the longest readily-available open data set, without interference from other commissioning activities. To evaluate space weather effects, the data from both satellites are formatted, gap-filled/interpolated, and fast-Fourier transformed for amplitude spectral density and coherence comparisons. Solar wind effects are not seen in a coherence plot between LPF and ACE; modest coherence in the planned LISA observational frequency band can be attributed to chance. This result indicates that measurable correlation due to solar-wind acceleration noise over 3 month timescales will be a negligible noise source. LISA is unlikely to inform solar wind measurements routinely. Another source of noise from the Sun, solar radiation pressure, is estimated to impart greater acceleration noise, but has yet to be analyzed.

79 ASTRONOMY AND ASTROPHYSICS↗

The Chlamydomonas Genome Project, version 6: reference assemblies for mating type plus and minus strains reveal extensive structural mutation in the laboratory

Five versions of the Chlamydomonas reinhardtii reference genome have been produced over the last two decades. Here we present version 6, bringing significant advances in assembly quality and structural annotations. PacBio-based chromosome-level assemblies for two laboratory strains, CC-503 and CC-4532, provide resources for the plus and minus mating type alleles. We corrected major misassemblies in previous versions and validated our assemblies via linkage analyses. Contiguity increased over ten-fold and >80% of filled gaps are within genes. We used Iso-Seq and deep RNA-seq datasets to improve structural annotations, and updated gene symbols and textual annotation of functionally characterized genes via extensive manual curation. We discovered that the cell wall-less classical reference strain CC-503 exhibits genomic instability potentially caused by deletion of the helicase RECQ3, with major structural mutations identified that affect >100 genes. We therefore present the CC-4532 assembly as the primary reference, although this strain also carries unique structural mutations and is experiencing rapid proliferation of a Gypsy retrotransposon. We expect all laboratory strains to harbor gene-disrupting mutations, which should be considered when interpreting and comparing experimental results. Collectively, the resources presented here herald a new era of Chlamydomonas genomics and will provide the foundation for continued research in this important reference organism.

59 BASIC BIOLOGICAL SCIENCES↗

Toward Common Weakness Enumerations in Industrial Control Systems

Here, the storyline of MITRE’s common weakness enumeration framework illustrates how the security and privacy technical community can collaborate/cooperate with policy makers to advance policy, giving it specifics and filling gaps of technical knowledge to improve security and resilience of critical infrastructure.

42 ENGINEERING↗

What's New in the NSRDB

The National Solar Radiation Database (NSRDB) provides solar resource data across the globe at a high temporal and spatial resolution. This data is primarily used in solar energy modeling. The NSRDB is updated annually for the United States and North, Central and South America and the data is currently available from 1998-2021. In 2022 the NSRDB was updated using the latest version of the underlying Physical Solar Model (PSM). This update includes improved surface albedo and gap-filling of cloud properties. The inclusion of these updates reduced the uncertainty in the data compared to previous versions of the NSRDB. The Himawari and Meteosat Indian Ocean Data Coverage (IODC) satellites were added to the Geostationary Operational Environmental Satellite (GOES) and made our coverage global. While standard data from the GOES continues to be served at an hourly 4km x 4km resolution, full resolution data has also been made available to the user. The NSRDB now contains over 200Tb of data with nearly 40Tb being added annually. We provide significant flexibility for data download depending on the amount of data required by the users. In this paper we provide an update on the current status on the NSRDB.

photovoltaic systems↗

A novel approach to partitioning evapotranspiration into evaporation and transpiration in flooded ecosystems

Reliable partitioning of micrometeorologically measured evapotranspiration (ET) into evaporation (E) and transpiration (T) would greatly enhance our understanding of the water cycle and its response to climate change related shifts in local-to-regional climate conditions and rising global levels of vapor pressure deficit (VPD). While some methods on ET partitioning have been developed, their underlying assumptions make them difficult to apply more generally, especially in sites with large contributions of E. Here, we report a novel ET partitioning method using artificial neural networks (ANNs) in combination with a range of environmental input variables to predict daytime E from nighttime ET measurements. The study uses eddy covariance data from four restored wetlands in the Sacramento-San Joaquin Delta, California, USA, as well as leaf-level T data for validation. The four wetlands vary in their vegetation make-up and structure, representing a range of ET conditions. The ANNs were built with increasing complexity by adding the input variable that resulted in the next highest average value of model testing R 2 across all sites. The order of variable inclusion (and importance) was: VPD > gap-filled sensible heat flux (H_gf) > air temperature (T air ) > friction velocity (u * ) > other variables. The model using VPD, H_gf, T air , and u * showed the best performance during validation with independent data and had a mean testing R 2 value of 0.853 (averaged across all sites, range from 0.728 to 0.910). In comparison to other methods, our ANN method generated T/ET partitioning results which were more consistent with CO 2 exchange data especially for more heterogeneous sites with large E contributions. Further, our method improves the understanding of T/ET partitioning. While it may be particularly suited to flooded ecosystems, it can also improve T/ET partitioning in other systems, increasing our knowledge of the global water cycle and ecosystem functioning.

59 BASIC BIOLOGICAL SCIENCES↗

EPWgen (EPW generator) [SWR-26-017]

EPWgen fetches hourly station observations (NOAA/Meteostat), fills gaps with MERRA2 reanalysis, merges data, and writes EPW files with computed headers (HDD/CDD, ground temperatures). It also runs QC checks, supports CSV-driven batch and metered-variable exports, and provides a PyQt5 GUI with mapping and progress tracking for single/multi-year workflows.

Bianchi, Carlo [National Laboratory of the Rockies↗

KCMP Minnesota Tall Tower Carbon Dioxide Data 2007-2019

This data set contains carbon dioxide concentration values and supporting metadata information measured at a tall tower (KCMP radio station, 244 meters) site near St. Paul, Minnesota, USA. The data package includes a text file containing hourly continuous carbon dioxide concentration measurements from 2007 to 2019, including raw, de-spiked, and gap filled data. Carbon dioxide concentrations were measured at approximately 100 meters using tunable diode laser spectroscopy. The instrumentation was maintained at the base of the tall tower in a temperature-controlled building. This data was collected to track and improve the understanding of carbon dioxide levels over a twelve year period for the region.

54 ENVIRONMENTAL SCIENCES↗

Scripts and raw/processed precipitation NRCS network data, Upper Colorado, 2008-2017

This data package consists of scripts and data that were used to generate all the results and figures for Mital et al., 2020 (https://doi.org/10.3389/frwa.2020.00020). The purpose of the study was to develop a new algorithm to impute (or gap-fill) missing daily precipitation data. Study area was the Upper Colorado Water Resources Region (UCWRR), over a time period of 2008-2017. In terms of data, the package consists of raw precipitation measurements from Natural Resources Conservation Service (NRCS) network. These raw measurements are compiled into a single csv file (NRCS_dates_2008_2017.csv). The corresponding metadata are compiled in Metadata_processed.csv. The package also consists of outputs generated during Baseline and Sequential imputation runs as documented in Mital et al., 2020. Scripts used to generate all the results and figures for Mital et al., 2020 are also uploaded. A readme file documenting the layout of the data archive is also uploaded, and a KML file shows the geographic range for the data.

54 ENVIRONMENTAL SCIENCES↗

QA/QC-ed Groundwater Level Time Series in PLM-1 and PLM-6 Monitoring Wells, East River, Colorado (2016-2022)

This data set contains QA/QC-ed (Quality Assurance and Quality Control) water level data for the PLM1 and PLM6 wells. PLM1 and PLM6 are location identifiers used by the Watershed Function SFA project for two groundwater monitoring wells along an elevation gradient located along the lower montane life zone of a hillslope near the Pumphouse location at the East River Watershed, Colorado, USA. These wells are used to monitor subsurface water and carbon inventories and fluxes, and to determine the seasonally dependent flow of groundwater under the PLM hillslope. The downslope flow of groundwater in combination with data on groundwater chemistry (see related references) can be used to estimate rates of solute export from the hillslope to the floodplain and river. QA/QC analysis of measured groundwater levels in monitoring wells PLM-1 and PLM-6 included identification and flagging of duplicated values of timestamps, gap filling of missing timestamps and water levels, removal of abnormal/bad and outliers of measured water levels. The QA/QC analysis also tested the application of different QA/QC methods and the development of regular (5-minute, 1-hour, and 1-day) time series datasets, which can serve as a benchmark for testing other QA/QC techniques, and will be applicable for ecohydrological modeling. The package includes a Readme file, one R code file used to perform QA/QC, a series of 8 data csv files (six QA/QC-ed regular time series datasets of varying intervals (5-min, 1-hr, 1-day) and two files with QA/QC flagging of original data), and three files for the reporting format adoption of this dataset (InstallationMethods, file level metadata (flmd), and data dictionary (dd) files).QA/QC-ed data herein were derived from the original/raw data publication available at Williams et al., 2020 (DOI: 10.15485/1818367). For more information about running R code file (10.15485_1866836_QAQC_PLM1_PLM6.R) to reproduce QA/QC output files, see README (QAQC_PLM_readme.docx). This dataset replaces the previously published raw data time series, and is the final groundwater data product for the PLM wells in the East River. Complete metadata information on the PLM1 and PLM6 wells are available in a related dataset on ESS-DIVE: Varadharajan C, et al (2022). https://doi.org/10.15485/1660962. These data products are part of the Watershed Function Scientific Focus Area collection effort to further scientific understanding of biogeochemical dynamics from genome to watershed scales. 2022/09/09 Update: Converted data files using ESS-DIVE’s Hydrological Monitoring Reporting Format. With the adoption of this reporting format, the addition of three new files (v1_20220909_flmd.csv, V1_20220909_dd.csv, and InstallationMethods.csv) were added. The file-level metadata file (v1_20220909_flmd.csv) contains information specific to the files contained within the dataset. The data dictionary file (v1_20220909_dd.csv) contains definitions of column headers and other terms across the dataset. The installation methods file (InstallationMethods.csv) contains a description of methods associated with installation and deployment at PLM1 and PLM6 wells. Additionally, eight data files were re-formatted to follow the reporting format guidance (er_plm1_waterlevel_2016-2020.csv, er_plm1_waterlevel_1-hour_2016-2020.csv, er_plm1_waterlevel_daily_2016-2020.csv, QA_PLM1_Flagging.csv, er_plm6_waterlevel_2016-2020.csv, er_plm6_waterlevel_1-hour_2016-2020.csv, er_plm6_waterlevel_daily_2016-2020.csv, QA_PLM6_Flagging.csv). The major changes to the data files include the addition of header_rows above the data containing metadata about the particular well, units, and sensor description. 2023/01/18 Update: Dataset updated to include additional QA/QC-ed water level data up until 2022-10-12 for ER-PLM1 and 2022-10-13 for ER-PLM6. Reporting format specific files (v2_20230118_flmd.csv, v2_20230118_dd.csv, v2_20230118_InstallationMethods.csv) were updated to reflect the additional data. R code file (QAQC_PLM1_PLM6.R) was added to replace the previously uploaded HTML files to enable execution of the associated code. R code file (QAQC_PLM1_PLM6.R) and ReadMe file (QAQC_PLM_readme.docx) were revised to clarify where original data was retrieved from and to remove local file paths.

54 ENVIRONMENTAL SCIENCES↗

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 2 Sensor Data v2-1

This is the version v2-1 Level 2 (L2) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. Level 2 (L2) data consist of sensor observations from the COMPASS-FME synoptic sites, TEMPEST, and DELUGE. Compared to the L1 data, these are more consistent (always 15-minute timestamps for the entire year); better QA/QC’d (out of bounds, out of service, and extreme outlier values are removed); and more complete, with a gap-filled time series available alongside the main observations, and additional derived (calculated) variables. L2 data are intended to be rapidly and easily usable in analyses and simulations. However, algorithmic outlier identification always carries the risk of removing valid data, and Level 1 data may be more suitable for analyses that focus on variability or extreme events. This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific Parquet (a high performance, space efficient format; see https://parquet.apache.org) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are reported every 15 minutes. Please see v2-1 TEMPEST L2 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. Data files are in Apache Parquet, a high performance, space efficient format for tabular data. These files can be read using R's `arrow` package (https://arrow.apache.org/docs/r/), with similar tools available in other languages. The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods. * Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021 * TEMPEST 1: June 22, 2022 * TEMPEST 2: June 6-7, 2023 * TEMPEST 3: June 11-13, 2024

EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATU↗

COMPASS-FME Synoptic Sites Level 2 Sensor Data v2-1

This is the version 2-1 Level 2 (L2) data release for COMPASS-FME environmental sensors located at our synoptic field sites. COMPASS-FME is studying sites in two distinct regions, the Chesapeake Bay and the Western Lake Erie Basin. We established the network at seven "synoptic" (observational) sites along the Chesapeake Bay and Lake Erie coastlines, collectively generating over three million observations per month, to track and comprehend environmental changes where land and water intersect. Additionally, the two regions provide an interesting contrast of saltwater and freshwater coasts that allow us to differentiate the impacts of inundation and coastal water chemistries in two nationally important coastal systems. Level 2 (L2) data consist of sensor observations from the COMPASS-FME synoptic sites, TEMPEST, and DELUGE. Compared to the L1 data, these are more consistent (always 15-minute timestamps for the entire year); better QA/QC’d (out of bounds, out of service, and extreme outlier values are removed); and more complete, with a gap-filled time series available alongside the main observations, and additional derived (calculated) variables. L2 data are intended to be rapidly and easily usable in analyses and simulations. However, algorithmic outlier identification always carries the risk of removing valid data, and Level 1 data may be more suitable for analyses that focus on variability or extreme events. This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific Parquet (a high performance, space efficient format; see https://parquet.apache.org) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are reported every 15 minutes. Data files are in Apache Parquet, a high performance, space efficient format for tabular data. These files can be read using R's `arrow` package (https://arrow.apache.org/docs/r/), with similar tools available in other languages. Please see v2-1 L2 Sensor Package QStart.pdf for detailed information on data package structure, temporal coverage, and versioning.

EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATU↗

CHESS 2025: Orthorectified airborne RGB imagery from NEON AOP surveys

This dataset provides Level 1 (L1) and Level 3 (L3) orthorectified Red-Green-Blue (RGB) imagery collected for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). This high-resolution imagery is a photographic record of red, green, and blue visible light from sunlight reflected off of the Earth’s surface. The data comprise full-color images of the ground surface and are primarily intended to provide context to imaging spectroscopy and light detection and ranging (LiDAR) data. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. RGB images were acquired using the PhaseOne IXM-RS150F high-resolution digital camera onboard the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). The package data include both an L1 product comprising one camera frame per file and an L3 mosaic aligned to the Universal Transverse Mercator (UTM) Zone 13N grid and the World Geodetic System (WGS) 84 projection. Both products are provided in geotif (.tif) format at 0.1 m ground resolution. The bulk of the imagery was collected during the main CHESS field campaign from June 13 to July 15, 2025. Additional images of a portion of the Upper Taylor (UPTA) domain were collected on September 18, 2025, to fill gaps in imagery identified after the main campaign was complete. RGB camera imagery is not radiometrically calibrated, and therefore pixel values should not be exploited for scientific analysis. Pixel values have undergone a manual adjustment to enhance feature identification. The imagery is rigorously geolocated which does allow for reliable geometric information to be retrieved. To generate the orthorectified imagery, the NEON AOP camera captured visible spectrum in red, green, and blue bands. The raw images were then processed using NEON’s camera orthorectification workflow. A boresight calibration flight was made to build a complete camera, distortion, and alignment model. Color balance/white balance and exposure correction were applied to the raw RGB images. The corrected images were orthorectified by ray-tracing image pixels to a lidar-derived digital surface model (DSM) mesh using the refined camera model, outputting orthorectified raster pixels on a regular grid. Flightline-level data were mosaicked by selecting per-pixel contributions from overlapping orthorectified images using line-of-sight (LOS) zenith angle minimization to reduce edge distortions. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗

Wind speed data from NCEI Marcos A Gelabert station, Panama, Jan 2008 – Dec 2019

This data package contains wind speed data retrieved from the National Oceanic and Atmospheric Administration's National Center for Environmental Information (NCEI). Measurements were collected at the Marcos A. Gilbert airport in Panama City, Panama. This station can be considered as an analogue to the NGEE Tropics Parque Natural Metropolitano site in Panama. In the attached Excel file, wind speed units have been converted from MPH to m/s (1MPH = 0.447 m/s). Data from NCEI can be accessed at: https://gis.ncdc.noaa.gov/maps/ncei/, STATION NAME: MARCOS A GELABERT (Airport in Panama City). Within the attached zip file are three .csv data files, three PDF files containing an explanation of gap filling procedures and site information, and a read.me text file with metadata and instructions for accessing data from NCEI.

54 ENVIRONMENTAL SCIENCES↗

SMART Mobility. Mobility Decision Science Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019, and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Mobility Decision Science Pillar. The Mobility Decision Science Pillar sought to fill gaps in existing knowledge about the human role in the mobility system including travel decision-making and technology adoption in the context of future mobility. The objective was to study how underlying preferences, needs, and contextual factors might constrain or hasten future transportation system scenarios. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant pillar’s Capstone Report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Creep-fatigue Behavior and Damage Accumulation of a Candidate Structural Material for Concentrating Solar Power Solar Thermal Receiver (Final Technical Report)

Creep-fatigue deformation is an important consideration for a thermal receiver in Concentrating Solar Power systems due to the constant static stress or pressure, diurnal cycling, and elevated service temperatures required for efficient operation. An accurate description of the creep-fatigue behavior, not available for five of the six candidate materials, is important for assessment of preliminary designs. This project has provided a detailed analysis of the creep-fatigue behavior and damage accumulation of Alloy 740H, a candidate structural material for a CSP solar thermal receiver to address a critical knowledge barrier for receiver designs identified in the CSP Gen3 Demonstration Roadmap. This project was divided in to three tasks, split between two general categories: Experimental and design methodology. Tasks 1 and 3, relating to the collection of data on Alloy 740H plate and sheet forms, respectively, were performed at the Idaho National Laboratory and used as input to Task 2, the development of design methodology performed at Argonne National Laboratory. Experimental work covered limited tensile and creep testing to fill gaps in available data that is currently available and to demonstrate how the plate of Alloy 740H tested in this work compared to historical data. A more substantial testing program for fatigue and creep-fatigue was performed, with particular emphasis on creep-fatigue as there is little available data and it is necessary to understand the creep-fatigue interaction and develop related design models. This effort includes the development of rules for the design of solar receiver components against high temperature creep-fatigue and ratcheting failure modes. The ASME Code rules for high temperature nuclear components was used to the basis of the method but adjustments will be made to reflect the generally shorter, diurnal operating cycles of thermal receivers and the relative consequences of failure, comparing nuclear to solar components. This work has provided guidance for the design of components for concentrating power facilities operating at high temperatures and undergoing high, secondary thermal stresses relative to the applied primary pressure stress. The design rules were developed for the design of Generation 3 CSP tubular receivers manufactured from Alloy 740H, but are generally applicable to a wide range of component types undergoing similar loads. Procedural design rules for components to be used in conjunction with the 2019 edition of the ASME Boiler & Pressure Vessel Code and the corresponding design data for Alloy 740H have been developed. A commentary describing the rationale behind the design rules and the data underlying the design material properties, as well as an extensive set of worked sample problems detailing the application of the rules to CSP components were developed as a part of this project, with a separate report covering these aspects in detail and included in the Appendix of this report.

14 SOLAR ENERGY↗