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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Analysis of Ice Mass Growth Over Time on the CRM65 Midspan Hybrid Model

The Aeronautics Research Mission Directorate at NASA is developing and applying tools to enable future technologies towards sustainable flight. Aircraft icing has been identified as a potential barrier to entry into service for innovative designs necessitating improvements to computational ice accretion tools. NASA is developing the Glenn Icing Computational Environment (GlennICE) to address deficiencies in the computational modeling capabilities of previously developed ice accretion solvers. To benchmark and improve the ability to model highly three-dimensional ice accretion, high quality validation data against experimental data is required. The CRM65 Midspan Hybrid geometry was previously tested at the NASA Icing Research Tunnel to generate experimental data for swept wing geometries typical for commercial transport aircraft. As a part of a 2018 icing test campaign, experimental data characterizing the relationship between ice accretion time and ice mass growth was obtained and can be leveraged for use in validation of computational tools. The desire for computational ice accretion solvers to predict ice shapes profiles accreted experimentally has often overshadowed the comparison to the mass and bulk volume of ice accreted. To address this deficiency, an analysis is presented in which GlennICE is applied to simulations of the CRM65 Midspan Hybrid model tested in the NASA Icing Research Tunnel. Results from the computational fluid dynamics simulations compared favorably to the experimental pressure coefficient data, thus validating the modeling setup. The experimental data showed excellent repeatability for the 15.0 minute accretion time. The comparisons between the experimental and computational ice mass over time showed good agreement up to 10.0 minutes after which the ice mass was underpredicted. The experimental ice mass was largely linear with some nonlinear data. The bulk volume of ice accreted experimentally compared well to GlennICE for the scanned ice shapes and mean combined cross section ice shapes, but was underpredicted for the maximum combined cross section ice shapes at longer accretion times. The experimental minimum combined cross section, mean combined cross section, and maximum combined cross section profiles when compared to GlennICE show good agreement for the mean combined cross section up to 15.0 minutes. The analyses show that with a single-shot method, GlennICE currently underpredicts the ice mass for longer accretion times, is not able to match the bulk volume of the maximum combined cross section due to dominating scallop features, and future work is required to generate a more generalized ice bulk density model.

Icing↗
DOE OSTI2021

Data for Intra- and inter-annual variability of nitrification in the rhizosphere of field-grown bioenergy sorghum

These data were collected in 2018 and 2019 at the University of Illinois Energy Farm (N 40.063607, W 88.206926). During each growing season, bulk and rhizosphere soil were collected from replicate Sorghum bicolor nitrogen use efficiency trial plots at three separate time points (approximately July 1, August 1, and September 1). We measured soil moisture, pH, soil nitrate and ammonium, potential nitrification, potential denitrification, and extracted and sequenced the V4 region of the 16S rRNA gene for microbial community analysis. All microbial sequence data is archived in the National Center for Biotechnology Information’s (NCBI) Sequence Read Archive (accession number SRP326979, project number PRJNA741261).

bioenergy↗
DOE OSTI2021

Update of Erosion Conditions and Assessment of Mitigation Options for the L-Bar Site

The L-Bar former uranium milling site was remediated and closed per regulations and is now under the long-term stewardship of the U.S. Department of Energy, Office of Legacy Management (DOE/LM). Since assuming stewardship in 2004, DOE/LM has observed erosion and sedimentation processes at the L-Bar Site that will challenge meeting the design closure criteria in the long term, and therefore erosion control structures were installed in several areas. Portions of these structures were subsequently damaged by summer convective storm events and they are being redesigned. At the request of DOE/LM, Desert Research Institute (DRI) reviewed the proposed redesign plans of the erosion control structures. The purpose of this review is to support DOE/LM decisions regarding mitigation of ongoing erosion processes, including assessing possible repair of the existing erosion control structures, as well as plan for effective long-term site management. This design review is performed in reference to the Uranium Mill Tailings Radiation Control Act (UMTRCA) standards contained in 40 CFR 192.02 that require containment of residual radioactive material be effective for up to 1,000 years (if achievable, but certainly for 200 years), limit radon release, protect groundwater, and minimize future maintenance. The erosion control structures under review are not subject to the UMTRCA standard directly, but they contribute to the performance of the overall containment system by virtue of their relationship with the large diversion channels bounding the disposal cell. The 11 design drawings included in “Erosion Control Alternatives Analysis, L-Bar, New Mexico, Disposal Site, LTS-111-0043-08-002,” dated January 11, 2019, and listed in Appendix A, and supporting calculations in Weston (2019) are the focus of the review. Additionally, DOE/LM provided supporting data such as a 2018 photogrammetry-based digital elevation model of the site, precipitation records collected from an on-site weather station, and sedimentation records collected at an on-site basin. This assessment includes review of the proposed design to mitigate current site conditions but also addresses site changes between the initial erosion assessment report (Miller et al., 2012) and current conditions. The analysis is at a predesign engineering level and does not include development of hydraulic or sediment transport models. Site precipitation data are included in Miller et al. (2012). The natural conditions of the site, the design of site diversion systems, observed erosion at the site, and predesign engineering analysis are used to develop a holistic view of the challenges to L-Bar Site stability and possible responsive actions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

WIND Toolkit Long-Term Ensemble Dataset

WIND Toolkit Long-term Ensemble Dataset (WTK-LED), an updated version of the meteorological WIND Toolkit, is a meteorological dataset providing high-resolution time series, including interannual variability and model uncertainty of wind speed at every modeling grid point to indicate ranges of possible wind speeds. The data were produced using the Weather Research and Forecasting Model (WRF). The vertical grid used in WTK-LED includes many vertical layers in the atmospheric boundary layer to provide information of atmospheric quantities across the rotor layer of utility scale and distributed wind turbines. The WTK-LED includes: (1) Numerical simulations of wind speed and other meteorological variables covering the contiguous United States (CONUS) and Alaska, with high-resolution (5-minute [min], 2-kilometer [km]) data for 3 years (2018-2020): WTK-LED CONUS, WTK-LED Alaska. (2) Climate simulations from Argonne National Laboratory covering North America, including Alaska, Canada, and most of Mexico and the Caribbean islands. These simulations complement the new WTK-LED to offer a 4-km, hourly dataset covering 20 years (2001-2020): WTK-LED Climate. (3) Specific long-term, high-resolution offshore simulations have been conducted separately for the U.S. coasts, Hawaii, and the Great Lakes, leading to the 2023 National Offshore Wind dataset: NOW-23. The data for Hawaii include land-based data and are part of WTK-LED Hawaii. Because the accuracy of simulations from a mesoscale model, such as WRF, varies depending on the location and weather situation, and can reach up to several m/s for wind speed, we provide simulated wind speed uncertainty estimates to the community to be used in conjunction with the deterministic model simulations. This dataset was developed to satisfy a wide group of stakeholders across various wind energy disciplines, including but not limited to stakeholders in the distributed and utility scale wind industry, the new emerging airborne wind energy field, grid integration, power systems modeling, environmental modeling, and researchers in academia, and to close some of the gaps that current public datasets have. Based on our validation results to date, we suggest use cases and applications for each dataset of the WTK-LED as shown in "WTK-LED Use Cases" resource below.

Array↗
DOE OSTI2024

Implementation of an extensible property modeling framework in ESPEI with applications to molar volume and elastic stiffness models

Property models are becoming more widely adopted by commercial Calphad databases, but they are not nearly as common in non-commercial or traditional academic Calphad databases. A primary driver is that user-friendly Calphad modeling tools that support property models are not widely available. Here we present new property modeling capabilities that have been implemented in ESPEI (the Extensible, Self-optimizing Phase Equilibrium Infrastructure). These capabilities include both generating property model parameters from data and improvements to the algorithmic selection of the most appropriate model from a series of candidates. Additionally, two illustrative examples are given that use ESPEI to fit different property models. First, we generate molar volume model parameters for Group IV, V, and VI refractory BCC alloys based on the model by Lu et al. (2005). Second, we demonstrate the extensibility of ESPEI’s property modeling capabilities by implementing a custom PyCalphad model for BCC elastic stiffness parameters to generate and compare parameters to the ones assessed by Marker et al. (2018) using the same data. Property models generated by ESPEI can be used in PyCalphad or further optimized with uncertainty quantification using ESPEI.

36 MATERIALS SCIENCE↗
DOE OSTI2024

DEM, DSM, and Cleaned LiDAR Point Cloud Data from the NGEE Arctic UAS Campaigns at the Teller 27 Field Site from 2017 and 2018, Seward Peninsula, Alaska

A Digital Elevation Model (DEM) and Digital Surface Model (DSM) were derived from airborne Light Detection and Ranging (LiDAR) data collected from Los Alamos National Laboratory's (LANL) heavy-lift unoccupied aerial system (UAS) quadcopter and hexacopter platforms operated by Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) scientists from the EES-14 group at LANL. These data were collected in August 2017 and July 2018 at the NGEE Arctic field site near mile marker 27 of the Bob Blodgett Nome-Teller Memorial Highway between Nome, Alaska and Teller, Alaska. A Vulcan Raven X8 Airframe (Mitcheldean, Gloucestershire, UK), DJI Matrice 600 Pro Airframe (Shenzhen, China), and Routescene UAV LiDARSystem (Edinburgh, Scotland, UK) were used to collect LiDAR data. Following pre-processing in Routescene LidarViewer Pro software, the LiDAR point clouds were cleaned and processed using CloudCompare software to separate ground and off-ground points. A high resolution DEM and DSM were then created using ArcGIS Pro software. This data package contains fully cleaned point clouds of ground and off-ground points (.las), a 25 cm DEM (.tif), and a 25 cm DSM (.tif) for the Teller 27 field site. Ancillary aircraft data, flight mission parameters, weather conditions, and raw lidar data and imagery can be found in the L0 datasets for these campaigns: NGA299 (2017) and NGA297 (2018). Minimally processed point clouds and auxiliary files can be found in the L1 dataset: NGA304 (2017 and 2018).The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) 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↗
DOE OSTI2020

Conceptual Model Update of Mercury Sources and Flux at Y-12 and Upper East Fork Poplar Creek, Oak Ridge,Tennessee

The interactions between the key processes that control the fate and transport of mercury (Hg) in the sediments, groundwater, and stream water within the Y-12 National Security Complex (Y-12) in Oak Ridge, Tennessee are complex, and many aspects are not well understood. In contaminated environments such as Y-12, conceptual models are frequently developed to aid in visualizing and understanding the dynamic nature of the hydrologic, geochemical, and physical environment. These conceptual models integrate data in an internally consistent manner to understand processes that control the fate and transport of contaminants. Over the past few decades of environmental investigation at Y-12, a number of conceptual models have been developed to identify and define various technical processes at various scales. The main purpose of the activities described in this report is to update the 2011 conceptual model for Upper East Fork Poplar Creek using the most recent Hg concentration and flux data and informed scientific interpretation. Emphasis was placed on improving the previous model by implementing a more detailed spatial and temporal approach to visualize transport pathways in the watershed and trends in flux and concentration over time, and to compare baseflow and stormflow system dynamics. Detailed descriptions of historical and current Hg sources and transport pathways are also provided for the east and west ends of the facility. This conceptual model will allow the US Department of Energy to evaluate past and present remedial activities and provide a strong technical basis for prioritizing and optimizing remedial responses in a cost-effective and efficient manner. Twenty years ago, Hg flux from Outfall (OF) 200 at the headwaters of East Fork Poplar Creek represented approximately 20% of the overall flux leaving Y-12. By the time of the 2011 conceptual model report, that percentage had increased to 70%–80%. Flux estimates compiled for this report using data from 2009 to 2018 show that OF200 accounted for roughly 60% of the flux leaving Y-12. The relative role of the four storm drain conduits to OF200 appeared similar to 2011, with OF163 being the greatest contributor to downstream flux. An increase in Hg concentrations and flux occurred throughout and downstream of west end storm drains in 2011 in response to a storm drain cleanout. Leading up to the 2011 storm drain cleanout, annual baseflow flux was higher at OF200A6 than at Station 17. From 2011 to 2018, the trend reversed with concentrations and flux being higher at Station 17 than at OF200A6. A spike in concentrations occurred in July 2018, potentially due to a one-time influx of Hg that occurred during COLEX decontamination and decommissioning activities at Y-12. This report includes recommendations for improving our conceptual understanding of Hg sources, transport pathways, and flux at Y-12. Important recommendations include further evaluation of transport pathways, concurrent measurement of Hg concentration and flux under baseflow and stormflow conditions at multiple sites, and research to understand the connections among periphyton and methylmercury concentration, bioavailability, and bioaccumulation. Ongoing facility decontamination and decommissioning efforts, site characterization, remedial actions, and research are facilitating the collection of new Hg data at Y-12. The conceptual model update provided herein takes advantage of the extensive recent Hg sampling to provide an updated assessment of Hg mass balance and support a refined understanding of Hg behavior at or near Y-12. This assessment should assist in future environmental management decisions and in mitigating the impacts of Hg on the surrounding environment.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

A treatment-effect model to quantify human dimensions of disaster impacts: the case of Hurricane Maria in Puerto Rico

Here, we propose a supervised learning approach using publicly available panel data to statistically quantify the specific manifestations of human impacts of an extreme event, such as changes number of suicides, substance abuse, excess mortality, and unemployment. This allows us to conceptually focus our framework on human impacts and how by attributing them to disaster events along widely accepted psychological, economic, and social dimensions. Our modified treatment-effect model allows counterfactual baseline conditions to be posited for each manifestation from which an aggregated quantitative multi-faceted measure of human impacts can be determined. The developed statistical methodology could be beneficial to policymakers who must allocate scarce resources to those communities in greater need. We illustrate the applicability of our approach using annual and monthly panel data from 2012 to 2018 encompassing the 2017 Hurricane Maria event across various municipalities in Puerto Rico. Our statistical modeling methodology stands apart since (i) it explicitly and more realistically captures the effect of different human-oriented manifestations of an actual event and (ii) it is flexible enough to accommodate individual preferences of various stakeholders in how they assign importance to multiple manifestations of human impacts.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Mapping Inundation from Hurricane Florence (2018) with L-Band Synthetic Aperture Radar, Commercial Imagery, and Ancillary Data via Machine Learning Classification

During and after flooding events, mapping the extent of floodwaters aids in the distribution of resources, recovery efforts, and damage assessment practices. Development of a land cover classification system focused on mapping inundation after major hurricane events using synthetic aperture radar (SAR) data could allow for the production of near-real-time inundation mapping, enabling government and emergency response entities to get a preliminary idea of a developing situation. Complimentary optical and SAR images from domestic and foreign entities are brought together through activations of the International Charter: Space and Major Disasters to support response efforts, from true-color, near-infrared, and thermal remote sensing data obtained by NASA, NOAA, and international satellites to the collection of high-resolution true color aerial photography by NOAA and the National Geodetic Survey. In response to Hurricane Florence of 2018, NASA JPL collected numerous swaths of quad-pol L-band SAR data with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument observing the record-setting river stages across North and South Carolina. The resulting fully-polarized SAR images allow for mapping of inundation extent at a high spatial resolution with a unique advantage over optical imaging stemming from the sensor’s ability to penetrate cloud cover and dense vegetation. In this study, true-color NOAA aerial and commercial satellite imagery are used in conjunction with four UAVSAR data swaths centered on the Lumberton and Cape Fear River basins in southeastern North Carolina to develop a Random Forest classification model focused on mapping open water and floodwater otherwise obscured by vegetation or lingering cloud cover. Ancillary building footprint, transportation route, and population data will also be incorporated into the classification scheme to estimate the societal impacts of flooding based on the proximity of features to detected inundation. Preliminary results from the Hurricane Florence case study will be discussed in addition to the limitations of available validation data for assessment of the classifier’s accuracy.

Alexander M Melancon↗
DOE OSTI2026

Flavor, transverse momentum, and azimuthal dependence of charged pion multiplicities in semi-inclusive deep-inelastic scattering with 10.6 GeV electrons

Measurements of semi-inclusive deep-inelastic scattering multiplicities for 𝜋 + and 𝜋 − from proton and deuteron targets are reported on a grid of hadron kinematic variables 𝑧, 𝑃 𝑇 , and 𝜙* for leptonic kinematic variables in the range 0.3<𝑥<0.6 and 3<𝑄 2 <5GeV 2 . Data were acquired in 2018 and 2019 at Jefferson Lab Hall C with a 10.6 GeV electron beam impinging on 10-cm-long liquid hydrogen and deuterium targets. Scattered electrons and charged pions were detected in the High Momentum Spectrometer and Super High Momentum Spectrometer, respectively. The multiplicities were fitted for each bin in (𝑥,𝑄 2 ,𝑧,𝑃𝑡) to extract the 𝜙*—independent 𝑀 0 and the azimuthal modulations ⟨cos⁡(𝜙*)⟩ and ⟨cos⁡(2⁢𝜙*)⟩. The 𝑃𝑡 dependence of the 𝑀 0 results was found to be remarkably consistent for the four cases studied: 𝑒⁢𝑝→𝑒⁢𝜋 + ⁢𝑋, 𝑒⁢𝑝→𝑒⁢𝜋 − ⁢𝑋, 𝑒⁢𝑑→𝑒⁢𝜋 + ⁢𝑋, 𝑒⁢𝑑→𝑒⁢𝜋 − ⁢𝑋 over the range 0GeV<𝑃 𝑡 <0.4GeV, as were the multiplicities evaluated near 𝜙*=180∘ over the extended range 0GeV<𝑃 𝑡 <0.7GeV. The Gaussian widths of the 𝑃 𝑡 dependence exhibit a quadratic increase with 𝑧. The cos⁡(𝜙*) modulations were found to be consistent with zero for 𝜋 + , in agreement with previous world data, while the 𝜋 − moments were, in many cases, significantly greater than zero. The cos⁡(2⁢𝜙*) modulations were found to be consistent with zero. The higher statistical precision of this dataset of about 20 000 individual multiplicity values, compared with previously published data, should allow improved determinations of quark transverse momentum distributions and higher twist contributions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Orthoimagery and Shapefiles Documenting Pre- and Post-August 2019 Slope Disturbances, Teller Road Site, Seward Peninsula, Alaska, 2018-2019

This dataset was derived from UAS aerial photos and dGPS data collected at the Teller mile marker 47 site in 2018 and 2019 and used to support research quantifying the timing and rate of surface movements and analyze soil transport process in Arctic landscapes. In July 2018, a Phantom uncrewed aerial system (UAS) collected >6800 high resolution aerial photos of the lower portion of the Teller 47 watershed (see for raw photos; pending archive NGA281). During the UAS survey, 25 x 25 cm tile ground control points (GCPs) were laid out and secured with one rebar rod in the center. The four corners and rebar tops were surveyed with differential GPS, and these coordinates were used to construct and validate an orthomosaic of the site. These points were resurveyed in August 2019 and used to track annual movement. Changes in position between the two surveys are reported in (Lathrop et al. 2022; NGA254). Agisoft Metashape photogrammetry software was used to construct a georeferenced orthomosaic image (*.tif file) of a portion (0.68 km2) of the watershed with a final resolution of ~1 cm. Manual delineation of the perimeters of failures visible in the UAS imagery (*.tif files) was conducted to create shapefile polygons (two *.zip files). The failures were identified by the exposure of bare mineral soils, which were made visible by disruption of the overlying tundra vegetation. The shapefiles were then used to analyse the topographic distribution and sizes of the failures. In August 2019 an additional ~300 georeferenced UAS images of slope instability features were collected (data pending submission) and compared with the 2018 orthomosaic. Overview maps of the failure locations included as a *.pdf.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort 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 NTRSDate not supplied

Mapping Inundation from Hurricane Florence (2018) with L-Band Synthetic Aperture Radar, Commercial Imagery, and Ancillary Data via Random Forest Classification

Mapping the extent of floodwaters following extreme rainfall aids in the distribution of resources, recovery efforts, and damage assessment practices. Development of a land cover classification system focused on mapping inundation after major hurricane events using synthetic aperture radar (SAR) data could allow for the production of near-real-time inundation mapping, enabling government and emergency response entities to get a preliminary idea of a developing situation. In response to Hurricane Florence of 2018, NASA JPL collected numerous swaths of quad-pol L-band SAR data with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument observing the record-setting river stages across North and South Carolina. The resulting fully-polarized SAR images allow for mapping of inundation extent at a high spatial resolution with a unique advantage over optical imaging stemming from the sensor’s ability to penetrate cloud cover and dense vegetation. This study seeks to determine how accurately maps of inundation can be generated from L-band SAR imagery through Random Forest classification. Once the extent of water and inundated vegetation is classified, cleanup operations are performed using fuzzy logic to reduce false detections. Estimates of water extent are then combined with datasets describing the distribution of population, buildings, and roads throughout the domain to evaluate societal impacts. Results from the Hurricane Florence case study will be discussed along with the limitations of available validation data for assessment of the classifier’s accuracy.

Alexander Melancon↗
DOE OSTI2022

TRIPOLI-4 neutronics calculations for IAEA-CRP benchmark of CEFR start-up tests using new libraries JEFF-3.3 and ENDF/B-VIII

Sodium-cooled Fast Reactors (SFR) are one of the promising Generation IV fast reactors. The China Experimental Fast Reactor (CEFR) is a 65 MWth pool-type SFR with a high neutron leakage core using high enriched uranium oxide fuel. The CEFR start-up tests in 2010 consist of series of neutronics experiments. Essential experimental data are available from the 2018 IAEA-CEFR-CRP benchmark document and useful for the validation of neutron transport codes and nuclear data libraries. The TRIPOLI-4 Monte Carlo transport code is a general-purpose neutronics code using continuous-energy nuclear data libraries. It has a rich validation database covering different computational and experimental benchmark data sets to assure the accuracy and credibility of numerical studies. Previous TRIPOLI-4 SFR core physics calculations used mainly MOX fuels. SFR control rod worth studies with TRIPOLI-4 were mainly on big-size cores. To test the modeling capability of TRIPOLI-4 on fuel loading patterns and on variable control rods positions for SFRs and to check recent nuclear data libraries, it is interesting to investigate the CEFR start-up tests in this work, including core states from subcritical to supercritical, control rods and rod group worth, point kinetics parameters, and radial foil activation measurements. TRIPOLI-4 calculation results using new data libraries JEFF-3.3, ENDF/B-VIII, and those of ENDF/B-VII.1 for different CEFR core states are reported here. By means of different options of TRIPOLI-4, control-rod SAs reactivity worth, point kinetics parameters, and radial {sup 237}Np(n,f) fission rate distribution were successfully evaluated. Both ENDF/B-VIII and JEFF-3.3 nuclear data libraries provided reliable results with a difference of 170 +/- 11 pcm in k{sub eff}.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRSDate not supplied

Snow Depth from AMSR-2 Using Multispectral Satellite Data in an Artificial Neural Network

By using diffusion theory and Monte Carlo lidar radiative transfer simulations, Hu et al. (2022b) has derived snow depth from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. Lu et al. (2022) calculated the snow depth by applying the methods to the satellite ICESat-2 lidar measurements over the Arctic sea ice, as well as land surfaces of Northern Hemisphere. In this paper, an artificial neural network (ANN) algorithm, employing several channels from Advanced Microwave Scanning Radiometer 2 (AMSR-2) and the humidity vertical profiles from Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System for Instrument Teams (GEOS-IT) product, is trained to determine snow depth identified by time and geolocation matched 2019 ICESat-2 snow-depth data during winter months over the Arctic sea ice. The trained ANN snow-depth was applied to 2018 AMSR-2 clear pixel data, although the algorithms perform reasonably well in thinner clouds. The validation data (different from the training set) of ANN snow depth from AMSR-2 showed a good agreement with time matched and co-located snow-depth values from ICESat-2. The bias was near zero, with mean absolute error (MAE) 0.05 cm and a root-mean-square-error (RMSE) 0.08 cm. Prior applying the trained ANN snow depth to AMSR-2 data, a cloud screening algorithm was developed with a similar approach. A separate ANN cloud mask was trained to determine an AMSR-2 pixel is clear or cloudy with time and geolocation matched 2015 CALIOP Vertical Feature Mask (VFM) over Arctic sea ice. The ANN cloud mask from AMSR-2 under-estimated cloud fraction by 3-6% compared to CALIOP . The additional research is needed to conclusively evaluate the ANN cloud mask accuracy. Finally, this paper will lay the foundation for a sustained long-term snowfall and snow-storm monitoring system. The future Cloud Aerosol LIdar for Global scale Observations of the ocean-Land Atmosphere system (CALIGOLA) mission will provide a means to calculate snow depth from the lidar backscattering pathlength distribution, benefiting from the UV, visible and infrared pulses. With the calculated snow depth as the truth one could develop a machine learning algorithm, as it was done in this paper, using a passive microwave instrument available at that time to generate a wide range of snow depth data, covering extensive spatial areas in the cross-orbit direction.

Neural Network↗
DOE OSTI2023

Throughfall-Reduction Drying Effects on Soil Moisture and Soil Temperature at 30-Minute Intervals Four Lowland Panamanian Forests

Objectives: Climatic drying is predicted for many tropical forests, yet effects on soil properties across moisture and soil gradients within tropical forests remain poorly characterized, hampering predictions of forest-climate feedbacks. We hypothesized that drying would suppress soil CO2 fluxes (i.e., respiration) in already-drier tropical forests by further reductions in soil moisture, but increases CO2 fluxes in wetter tropical forests by alleviating anaerobiosis and soil saturation. We measured soil CO2 fluxes, soil moisture, soil temperature, and forest floor biomass during wet-dry cycles (2015 – 2022) in four Panamanian forests that vary in rainfall and soil fertility. We also surveyed all tree species and identified to species in 2018 and 2019.Results: We found that soil moisture peaked in the wet season and declined in the dry season. Measured soil CO2 fluxes declined in the dry season and peaked in the early wet season ahead of peak soil moisture, resulting in a lower soil moisture optimum for respiration than previously modeled. Chronic throughfall exclusion also suppressed soil moisture across the four forests, and also initially suppressed soil CO2 fluxes across forests. There was sustained suppression of soil CO2 fluxes after four years in the wettest forest only (-28 ± 4% during the dry season), but elevated soil CO2 fluxes in a fertile forest after four years (+75 ± 28% during the late wet season). The unexpected negative drying effect in the wettest, most infertile forest could have resulted from reduced vertical flushing of nutrients into soils, as the drying effect increased with time. Including hydro-nutrient interactions in ecosystem models could improve predictions of tropical forest-climate feedbacks (Cusack et al. 2023). Datasets included: Datasets here include .csv and .xls files for volumetric soil moisture (volume/volume) and soil temperature (°C). Data were taken at 30-minute intervals at 5cm and 20cm depths using permanent probes and data loggers (see methods). Data presented are from 2018-2019. There is also a .kml file that includes coordinates for all 32 plots included in the study of four forests (n = 4 throughfall reduction and n = 4 control plots per site). No special software is needed to open these files.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

TESS Science Processing Operations Center Pipeline and Data Products

TESS launched 18 April 2018 to conduct a two-year, near all-sky survey for at least 50 small, nearby exoplanets for which masses can be ascertained and whose atmospheres can be characterized by ground- and space-based follow-on observations. TESS just completed its survey of the southern hemisphere, identifying >600 candidate exoplanets and unveiling a plethora of exciting non-exoplanet astrophysics results, such as asteroseismology, asteroids, and supernova. The TESS Science Processing Operations Center (SPOC) processes the data downlinked every two weeks to generate a range of data products hosted at the Mikulski Archive for Space Telescopes (MAST). For each sector (~1 month) of observations, the SPOC calibrates the image data for both 30-min Full Frame Images (FFIs) and up to 20,000 pre-selected 2-min target star postage stamps. Data products for the 2-min targets include simple aperture photometry and systematic error-corrected flux time series. The SPOC also conducts searches for transiting exoplanets in the 2-min data for each sector and generates Data Validation time series and associated reports for each transit-like feature identified in the search. Multi-sector searches for exoplanets are conducted periodically to discover longer period planets, including those in the James Webb Continuous Viewing Zone (CVZ), which are observed for up to one year. Data products also include co-trending basis vectors (CBVs) and calibration files, such as the Pixel Response Functions across the field of view of each of TESS's four cameras. To maximize the usability, the TESS science data products are modeled after those for Kepler, including Target Pixel Files and Light Curve files.In this talk, I describe the SPOC pipeline and the chief differences between the TESS and the Kepler pipelines, and the major updates to the SPOC pipeline (4.0) available now to the community at MAST. I also discuss the documentation available to the community to help them in properly interpreting and analyzing the TESS data products.The TESS Mission is funded by NASA's Science Mission Directorate as an Astrophysics Explorer Mission.

Jenkins, Jon M.↗
DOE OSTI2021

Search for dark matter in association with an energetic photon in pp collisions at $ \sqrt{s} $ = 13 TeV with the ATLAS detector

A search for dark matter is conducted in final states containing a photon and missing transverse momentum in proton-proton collisions at s√ = 13 TeV. The data, collected during 2015–2018 by the ATLAS experiment at the CERN LHC, correspond to an integrated luminosity of 139 fb -1 . No deviations from the predictions of the Standard Model are observed and 95% confidence-level upper limits between 2.45 fb and 0.5 fb are set on the visible cross section for contributions from physics beyond the Standard Model, in different ranges of the missing transverse momentum. The results are interpreted as 95% confidence-level limits in models where weakly interacting dark-matter candidates are pair-produced via an s-channel axial-vector or vector mediator. Dark-matter candidates with masses up to 415 (580) GeV are excluded for axial-vector (vector) mediators, while the maximum excluded mass of the mediator is 1460 (1470) GeV. In addition, the results are expressed in terms of 95% confidence-level limits on the parameters of a model with an axion-like particle produced in association with a photon, and are used to constrain the coupling g a Z γ of an axion-like particle to the electroweak gauge bosons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Search for nonresonant new physics signals in high-mass dilepton events produced in association with b-tagged jets in proton-proton collisions at $\sqrt{s}=13$ TeV

A search for nonresonant new physics phenomena in high-mass dilepton events produced in association with b-tagged jets is performed using proton-proton collision data collected in 2016–2018 by the CMS experiment at the CERN LHC, at a center-of-mass energy of 13 TeV corresponding to an integrated luminosity of 138 fb −1 . The analysis considers two effective field theory models with dimension-six operators; involving four-fermion contact interactions between two leptons (ℓℓ, electrons or muons) and b or s quarks (bbℓℓ and bsℓℓ). Two lepton flavor combinations (ee and μμ) are required and events are classified as having 0, 1, or ≥2 b-tagged jets in the final state. No significant excess is observed over the standard model backgrounds. Upper limits are set on the production cross section of the new physics signals. These translate into lower limits on the energy scale Λ of 6.9 to 9.0 TeV in the bbℓℓ model, depending on model parameters, and on the ratio of energy scale and effective coupling, Λ/g*, of 2.0 to 2.6 TeV in the bsℓℓ model. Lepton flavor universality is also tested by comparing the dielectron (ee) and dimuon (μμ) mass spectra for different b-tagged jet multiplicities. No significant deviation from the standard model expectation of unity is observed.

Beyond Standard Model↗