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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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Per-page control of physical address space distribution among memory modules

Systems, apparatuses, and methods for implementing per-page control of physical address space distribution among memory modules are disclosed. A computing system includes a plurality of processing units coupled to a plurality of memory modules. A determination is made as to which physical address space distribution granularity to implement for physical memory pages allocated for a first data structure. The determination can be made on a per-data-structure basis (e.g., file, page, block, etc.) or on a per-application-basis. A physical address space distribution granularity is encoded as a property of each physical memory page allocated for the first data structure, and physical memory pages of the first data structure distributed across the plurality of memory modules based on a selected physical address space distribution granularity. Page table entries (PTEs) may be annotated with the selected physical address space distribution granularity, using an addressing mapping granularity (AMG) field of a page table entry, where the granularity may be, for example, a fine-grain distribution granularity or a coarse-grain distribution granularity.

Jayasena, Nuwan S.↗
DOE OSTI2024

Vegetation Warming Experiment: Environmental Conditions, Utqiagvik (Barrow), Alaska, 2017

Environmental conditions measured in five warming chambers and paired ambient control plots located on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska from 22 June - 17 September, 2017. These data were recorded in support of the Zero Power Warming (ZPW) vegetation warming experiment, a series of single season vegetation warming treatments conducted over four years from 2017-2021 (no experiment in 2020). Air temperature and humidity, infrared surface (canopy) temperature, soil temperature, soil moisture, NDVI (normalized difference vegetation index), PRI (photochemical reflectance index), solar radiation and chamber venting were recorded in each chamber at 1 minute intervals. Ambient air temperature, humidity, solar radiation and uplooking PRI and NDVI were measured at a centrally located meteorology station. Vapor pressure deficit (VPD) was calculated and included in the final processed data products. Data has undergone full QA/QC and is presented as 1 minute data, and hourly and daily aggregate data products. This data package includes unprocessed raw data (*.dat files), processed data (*.csv) and metadata including a full description of sensors, calculations and processing (*.csv, *.pdf). See related NGEE-Arctic Vegetation Warming Experiment data packages for leaf-level gas exchange and other leaf trait data; chamber, plot and landscape phenocamera images; thaw depth, and GPS locations of chambers and ambient plots. 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↗
DOE OSTI2015

SU-E-T-117: Analysis of the ArcCHECK Dosimetry Gamma Failure Using the 3DVH System

Purpose: To evaluate gamma analysis failure for the VMAT patient specific QA using ArcCHECK cylindrical phantom. The 3DVH system(Sun Nuclear, FL) was used to analyze the dose difference statistic between measured dose and treatment planning system calculated dose. Methods: Four case of gamma analysis failure were selected retrospectively. Our institution gamma analysis indexes were absolute dose, 3%/3mm and 90%pass rate in the ArcCHECK dosimetry. The collapsed cone convolution superposition (CCCS) dose calculation algorithm for VMAT was used. Dose delivery was performed with Elekta Agility. The A1SL(standard imaging, WI) and cavity plug were used for point dose measurement. Delivery QA plans and images were used for 3DVH Reference data instead of patient plan and image. The measured data of ‘.txt’ file was used for comparison at diodes to acquire a global dose level. The,.acml’ file was used for AC-PDP and to calculated point dose. Results: The global dose of 3DVH was calculated as 1.10 Gy, 1.13, 1.01 and 0.2 Gy respectively. The global dose of 0.2 Gy case was induced by distance discrepancy. The TPS calculated point dose of was 2.33 Gy to 2.77 Gy and 3DVH calculated dose was 2.33 Gy to 2.68 Gy. The maximum dose differences were −2.83% and −3.1% for TPS vs. measured dose and TPS vs. 3DVH calculated respectively in the same case. The difference between measured and 3DVH was 0.1% in that case. The 3DVH gamma pass rate was 98% to 99.7%. Conclusion: We found the TPS calculation error by 3DVH calculation using ArcCHECK measured dose. It seemed that our CCCS algorithm RTP system over estimated at the central region and underestimated scattering at the peripheral diode detector point. The relative gamma analysis and point dose measurement would be recommended for VMAT DQA in the gamma failure case of ArcCHECK dosimetry.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Vegetation Warming Experiment: Environmental conditions, Utqiagvik (Barrow), Alaska, 2019

Environmental conditions measured in five warming chambers and paired ambient control plots located on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska from 19 June – 25 September, 2019. These data were recorded in support of the Zero Power Warming (ZPW) vegetation warming experiment, a series of single season vegetation warming treatments conducted over four years from 2017–2021 (no experiment in 2020). Air temperature and humidity, infrared surface (canopy) temperature, soil temperature, soil moisture, NDVI (normalized difference vegetation index), PRI (photochemical reflectance index), solar radiation and chamber venting were recorded in each chamber at 1 minute intervals. Ambient air temperature, humidity, solar radiation and uplooking PRI and NDVI were measured at a centrally located meteorology station. Vapor pressure deficit (VPD) was calculated and included in the final processed data products. Data has undergone full QA/QC and is presented as 1 minute data, and hourly and daily aggregate data products. This data package includes unprocessed raw data (*.dat files), processed data (*.csv) and metadata including a full description of sensors, calculations and processing (*.csv, *.pdf). See related NGEE-Arctic "Vegetation Warming Experiment" data packages for leaf-level gas exchange and other leaf trait data; chamber, plot and landscape phenocamera images; thaw depth, and GPS locations of chambers and ambient plots.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 OSTI2026

arco (Assembled Resource-Constrained Optimization) [SWR-26-030]

Arco (Assembled Resource-Constrained Optimization) is a memory-smart optimization DSL and solver for LP and MIP problems on constrained hardware. The software is an optimization framework built around a KDL-based domain-specific language and a CLI compiler/solver. You write optimization models in .kdl files, and the arco CLI compiles, validates, inspects, and solves them. Language bindings (Python today, more planned) provide programmatic access to the same engine. Built for harder optimization problems on constrained resources, Arco is intentional about every allocation, careful with stack and heap behavior, and relentless about minimizing memory usage so more systems can run real workloads. Arco is built primarily for internal use within our organization. You are welcome to try it, but we make no guarantees about API stability or robustness at this stage

Sanchez Perez, Pedro Andres [National Laboratory o↗
DOE OSTI2013

PERTRAN: Genome-guided RNA-seq Read Assembler

As short RNA-seq reads become a standard, affordable input to any genome annotation project, a sensitive and accurate transcript assembler is an essential part of any gene prediction system. PERTRAN is a pipeline for assembling transcripts from RNA-seq reads which demonstrates higher sensitivity, with fewer fused exons (in most cases), and faster run times compared to other TOPHAT/CUFFLINKS and genome-guided Trinity. PERTRAN shows slightly lower specificity with increased gene fusions in some cases, discussed below. SAM files generated from PERTRAN can be used to compute expression level by cuffdiff and result is comparable to that from TOPHAT.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

Draft Electric Rule 21 Test Protocols for Advanced Inverter Functions

Disclaimer: The following document includes draft certification protocols and should not be viewed as a consensus-based performance standard. Distributed energy resources (DERs), such as photovoltaic (PV) systems, when deployed in a large scale, are capable of significantly influencing the operation of bulk and local power systems. Looking to the future, European and American stakeholders are working on standards to make it possible to manage the potentially complex interactions between DER and the power system. One of the jurisdictions considering modifications to the DER interconnection requirements is California. To determine what changes could improve electric grid reliability and allow greater penetrations of renewable energy, the California Public Utilities Commission (CPUC) and the California Energy Commission (CEC), in conjunction with consultant Frances Cleveland, convened the Smart Inverter Working Group (SIWG) in January 2013. The SIWG--composed of state agencies, utility engineers, national laboratories, manufacturers, trade associations, and advocacy groups--provided the CPUC a set of recommendations in early 2014 1 . The recommendations included specific advanced DER functions and interoperability requirements, along with a proposed timeline, for California to add new capabilities to grid-interconnected DER. On August 18, 2014 the three California Investor-Owned Utilities (IOUs)--Pacific Gas and Electric Company (PG&E), Southern California Edison Company (SCE) and San Diego Gas & Electric Company (SDG&E)--drafted a Advice Letter filing to the CPUC setting forth revisions to Electric Tariff Rule 21 to conform to the seven recommendations made by the Working Group 2 . After a comment period, the CPUC issued an update to the IOU recommendations 3 . At the time of this publication, there was no final legal ruling on the CPUC Rulemaking (R.) 11-09-011 ("Order Instituting Rulemaking on the Commission's Own Motion to improve distribution level interconnection rules and regulations for certain classes of electric generators and electric storage resources"). In the U.S., Nationally Recognized Test Laboratories (NRTLs) independently verify products to safety and functional standards. PV inverters are certified to Underwriters Laboratories (UL) Standard 1741 4 . However, new advanced inverter functions described in the SIWG and IOU proposals are not included in this standard, so there is a critical need to develop test protocols for these functions in preparation of a positive ruling by the CPUC. Through a California Solar Initiative Grant, Sandia National Laboratories (Sandia or SNL), Underwriters Laboratories (UL), Electric Power Research Institute, Inc. (EPRI), Xanthus Consulting, SunSpec Alliance, Loggerware, and utility and PV inverter manufacturers have collaborated to draft this certification protocol for the Rule 21 SIWG Phase 1 advanced inverter functions. This report also includes test procedures for Phase 2 and Phase 3 functions that will be demonstrated later in the CSI4 project. This collaborative effort was performed in close junction with the UL 1741 Standards Technical Panel (STP) working group. This document represents a snapshot of the draft testing protocols at the time of publication and not a consensus certification protocol for advanced inverters. 1 California Public Utilities Commission, "Recommendations for Updating the Technical Requirements for Inverters in Distributed Energy Resources, Smart Inverter Working Group Recommendations," Filed 7 Feb 2014. 2 J.J. Newlander, R.G. Litteneker, S.W. Walter, M. Dwyer, Joint motion of Pacific Gas and Electric Company (U 39 E), Southern California Edison Company (U 338 E) and San Diego Gas & Electric Company (U 902 E) regarding implementation of smart inverter functionalities, Rulemaking 11-09-011 Advice Letter, 18 July, 2014. 3 J.T. Sullivan, CPUC Rulemaking 11-09-011 Agenda ID #13460, 13 Nov, 2014. 4 Underwriters Laboratories Std. 1741 Ed. 2, "Inverters, Converters, Controllers and Interconnection System Equipment for use with Distributed Energy Resources," 2010.

42 ENGINEERING↗
DOE OSTI2026

Data from: "Towards CONUS-Wide ML-Augmented Conceptually-Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics"

This data package was generated to support the manuscript “Towards CONUS-Wide Machine Learning-Augmented Conceptually Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics.” It provides input files, model outputs, plotting data, scripts, notebooks, and documentation used to develop, evaluate, and reproduce Mass-Conserving Perceptron (MCP)-based hydrologic modeling experiments across 513 selected Catchment Attributes and Meteorology for Large-sample Studies in the United States (CAMELS-US) basins. The files are organized by modeling component and analysis purpose, including rainfall–runoff experiments, snow module experiments, coupled hydrologic-snow experiments, Long Short-Term Memory (LSTM) benchmark results, model skill metrics, initialization and epoch records, cell-state normalization files, Akaike Information Criterion (AIC)-based model comparison files, and data used to generate manuscript figures. Tabular files can be opened using standard spreadsheet software or Python/R data-analysis tools. Python scripts, Jupyter notebooks, and selected MATLAB scripts are included for model execution, postprocessing, plotting, and statistical analysis. Quality assurance and quality control were conducted through the source-data selection and modeling workflow. Meteorological forcing, streamflow, and static catchment attributes were derived from the CAMELS-US dataset, and snow water equivalent data were derived from the University of Arizona (UA) Snow Water Equivalent dataset. Selected basins and time periods were screened during the associated research workflow to avoid missing observations or poor-quality cases. Static geospatial features were processed primarily using Quantum Geographic Information System (QGIS) and Geospatial Data Abstraction Library (GDAL) workflows. Additional details are provided in the associated manuscript and documentation.

ESS-DIVE CSV File Formatting Guidelines Reporting ↗
DOE OSTI2015

SU-E-T-773: Use of a Commercial TPS for Deriving Exit Dose Distribution for Patient Specific QA with An EPID

Purpose: A method to derive exit dose distributions with a commercial treatment planning system for comparison to Epid measured doses. Methods: In contrast to published methods, we present an approach that uses a treatment planning system (Raystation, Raysearch) to calculate exit dose patterns based on a modified patient CT dataset. The EPID is assumed as water equivalent and therefore is represented as water filled ring in the images. This is done by: -Export a renamed copy of the patient CT and plan data set. —Manipulate the CT-data to a field of view of 1000 mm. — Insert the water filled cylinder ring, centered to the target point. — Manipulate plan file with the new target and dose prescription point. — Import and recalculate dose on the manipulated data set. — Export and extract exit field dose matrix from the cylinder (optional entrance dose). — Calibrate matrix to size and rebin data from the cylinder to EPID equivalent data. — Correct Epid measurement for scatter, position and field size. -Evaluate and compare data in Verisoft (PTW). Data manipulation and extraction is done by a simple tool (IDL, Exelis). Results: This method was tested on a Siemens Artiste 6MV for field sizes up to 27×27cm2 limited by used geometry and EPID size. First phantom measurements show good results for fields up to 20×20cm (pass rate > 95% for 3%, 3mm Gamma index) while larger fields have higher discrepancies towards the field edges. This might Result from off axis softening of the beam and the higher sensitivity of the detector to beam scatter. Conclusion: This method might simplify the use of exit dosimetry with the EPID for patient specific QA as it uses the dose calculation of a commercial treatment planning system. The concept was proven by phantom data sets, giving acceptable results.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

MO-G-BRE-01: A Real-Time Virtual Delivery System for Photon Radiotherapy Delivery Monitoring

Purpose: Treatment delivery monitoring is important for radiotherapy, which enables catching dosimetric error at the earliest possible opportunity. This project develops a virtual delivery system to monitor the dose delivery process of photon radiotherapy in real-time using GPU-based Monte Carlo (MC) method. Methods: The simulation process consists of 3 parallel CPU threads. A thread T1 is responsible for communication with a linac, which acquires a set of linac status parameters, e.g. gantry angles, MLC configurations, and beam MUs every 20 ms. Since linac vendors currently do not offer interface to acquire data in real time, we mimic this process by fetching information from a linac dynalog file at the set frequency. Instantaneous beam fluence map (FM) is calculated. A FM buffer is also created in T1 and the instantaneous FM is accumulated to it. This process continues, until a ready signal is received from thread T2 on which an inhouse developed MC dose engine executes on GPU. At that moment, the accumulated FM is transferred to T2 for dose calculations, and the FM buffer in T1 is cleared. Once the calculation finishes, the resulting 3D dose distribution is directed to thread T3, which displays it in three orthogonal planes overlaid on the CT image for treatment monitoring. This process continues to monitor the 3D dose distribution in real-time. Results: An IMRT and a VMAT cases used in our patient-specific QA are studied. Maximum dose differences between our system and treatment planning system are 0.98% and 1.58% for the two cases, respectively. The average time per MC calculation is 0.1sec with <2% relative uncertainty. The update frequency of ∼10Hz is considered as real time. Conclusion: By embedding a GPU-based MC code in a novel data/work flow, it is possible to achieve real-time MC dose calculations to monitor delivery process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-J-72: Geant4 Simulations of Spot-Scanned Proton Beam Treatment Plans

Purpose: To evaluate density inhomogeneities which can effect dose distributions for real-time image gated spot-scanning proton therapy (RGPT), a dose calculation system, using treatment planning system VQA (Hitachi Ltd., Tokyo) spot position data, was developed based on Geant4. Methods: A Geant4 application was developed to simulate spot-scanned proton beams at Hokkaido University Hospital. A CT scan (0.98 × 0.98 × 1.25 mm) was performed for prostate cancer treatment with three or four inserted gold markers (diameter 1.5 mm, volume 1.77 mm3) in or near the target tumor. The CT data was read into VQA. A spot scanning plan was generated and exported to text files, specifying the beam energy and position of each spot. The text files were converted and read into our Geant4-based software. The spot position was converted into steering magnet field strength (in Tesla) for our beam nozzle. Individual protons were tracked from the vacuum chamber, through the helium chamber, steering magnets, dose monitors, etc., in a straight, horizontal line. The patient CT data was converted into materials with variable density and placed in a parametrized volume at the isocenter. Gold fiducial markers were represented in the CT data by two adjacent voxels (volume 2.38 mm3). 600,000 proton histories were tracked for each target spot. As one beam contained about 1,000 spots, approximately 600 million histories were recorded for each beam on a blade server. Two plans were considered: two beam horizontal opposed (90 and 270 degree) and three beam (0, 90 and 270 degree). Results: We are able to convert spot scanning plans from VQA and simulate them with our Geant4-based code. Our system can be used to evaluate the effect of dose reduction caused by gold markers used for RGPT. Conclusion: Our Geant4 application is able to calculate dose distributions for spot scanned proton therapy.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

Tool for Developing Integrated Strategies for Decontamination and Waste Management - 20291

Management of waste from chemical, biological, and radiological incidents is complicated by the fact that decontamination decisions have a profound impact on the characteristics of resulting waste streams. Wide-area biological and radiological incidents could produce massive quantities of waste that may need to be treated on-site and sent for subsequent disposal as non-contaminated materials, or else be directly disposed of as contaminated materials. The EPA has developed the Waste Estimation Support Tool (WEST) for characterizing and quantifying biological and radiological waste that may be generated from decontamination efforts. This paper focuses on the WEST's uses for radiological incidents. WEST combines Geographic Information System (GIS)-based analysis of externally-supplied plume data, infrastructure databases derived from the Federal Emergency Management Agency's (FEMA's) Hazus tool [1], and satellite imagery surface recognition algorithms to combine the composition and square footage of the buildings in the plume with estimates of the materials between the buildings in the plume. The resulting GIS data files are then imported into a Microsoft Access database application, where they are combined with information about the nature and concentration of contaminants, and then subjected to decontamination strategies for different contaminated surfaces. The tool provides estimates of the type and quantities of potential wastes resulting from simulated decontamination and/or demolition activities and includes estimates of the remaining contamination levels including residual contamination contained within each waste stream. Estimates are presented at several levels of detail, allowing users to obtain needed data at the desired resolution. These include estimates for the total affected area, estimates by contamination zone, estimates by decontamination method(s), and estimates by building type (occupancy classification). EPA is currently developing the next version of WEST which will include several substantial enhancements. The most significant improvement for the next version of WEST will include the ability for users to develop contamination scenarios and waste estimates based on previously developed, readily available, and geographically specific infrastructure data. Instead of using WEST's default infrastructure data based on FEMA's Hazus tool, users will be able to import their own building data specific to the geographically affected area. This capability may substantially decrease uncertainties in the resulting waste estimates because the results will be based on actual building data (numbers of each type, square footage, building height, etc.). Two other significant enhancements will be the ability to generate waste estimates for vehicles and biomass. In addition to building debris and building decontamination waste, vehicles and biomass will likely constitute a significant percentage of the total waste which may result from wide area contamination events. This presentation will present the most recent version of WEST, which includes such considerations as affected biomass (e.g., trees), vehicles, and the ability to replace the default Hazus infrastructure databases with custom infrastructure databases. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Plug-and -Play Model Architecture and Development Environment for Powertrain/Propulsion System - Final CRADA Report

Several tools already exist to develop detailed plant model, including GT-Power, AMESim, CarSim, and SimScape. The objective of Autonomie is not to provide a language to develop detailed models; rather, Autonomie supports the assembly and use of models from design to simulation to analysis with complete plug-and-play capabilities. Autonomie provides a plug-and-play architecture to support this ideal use of modeling and simulation for math-based automotive control system design. Models in the standard format create building blocks, which are assembled at runtime into a simulation model of a vehicle, system, subsystem, or component to simulate. All parts of the graphical user interface (GUI) are designed to be flexible to support architectures, systems, components, and processes not yet envisioned. This allows the software to be molded to individual uses, so it can grow as requirements and technical knowledge expands. This flexibility also allows for implementation of legacy code, including models, controller code, processes, drive cycles, and post-processing equations. A library of useful and tested models and processes is included as part of the software package to support a full range of simulation and analysis tasks, immediately. Autonomie also includes a configuration and database management front end to facilitate the storage, versioning, and maintenance of all required files, such as the models themselves, the model’s supporting files, test data, and reports. During the duration of the CRADA, Argonne has worked closely with GM to implement and demonstrate each one of their requirements. A use case was developed by GM for every requirement and demonstrated by Argonne. Each of the new features were verified by GM experts through a series of Gate. Once all the requirements were validated they were presented to the directors as part of GM Gate process.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Vegetation Warming Experiment: Environmental Conditions, Utqiagvik (Barrow), Alaska, 2018

Environmental conditions measured in five warming chambers and paired ambient control plots located on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska from 16 June - 24 September, 2018. These data were recorded in support of the Zero Power Warming (ZPW) vegetation warming experiment, a series of single season vegetation warming treatments conducted over four years from 2017-2021 (no experiment in 2020). Air temperature and humidity, infrared surface (canopy) temperature, soil temperature, soil moisture, NDVI (normalized difference vegetation index), PRI (photochemical reflectance index), solar radiation and chamber venting were recorded in each chamber at 1 minute intervals. Ambient air temperature, humidity, solar radiation and uplooking PRI and NDVI were measured at a centrally located meteorology station. Vapor pressure deficit (VPD) was calculated and included in the final processed data products. Data has undergone full QA/QC and is presented as 1 minute data, and hourly and daily aggregate data products. This data package includes unprocessed raw data (*.dat files), processed data (*.csv) and metadata including a full description of sensors, calculations and processing (*.csv, *.pdf). See related NGEE-Arctic "Vegetation Warming Experiment" data packages for leaf-level gas exchange and other leaf trait data; chamber, plot and landscape phenocamera images; thaw depth, and GPS locations of chambers and ambient plots.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 OSTI2017

Using Systems Approaches to Improve Photosynthesis and Water Use Efficiency in Sorghum

Throughout the 5 years of this project, 8 labs coordinated to make significant progress on our understanding of how C4 grasses, which include some of our most important crops, can use water more efficiently. We took an integrated approach, using physiology to study how to improve photosynthesis, how to manipulate the pores that move water and CO2 in and out of leaves and the formation and structure of roots. We coupled that with genetic and computational approaches to identify the genes controlling these traits and then used modeling to understand how the genomic system integrates the functions of the genes. We then developed synthetic biology approaches to manipulate the genes and gene editing and plant transformation approaches to insert those traits into a model C4 grass and the bioenergy crop Sorghum bicolor. Through the course of the project, we developed new software tools and genetic resources, filed 5 patents and published 46 papers. We made significant contributions to the bioeconomy through the training of undergraduates, graduate students and postdoctoral researchers. In addition to addressing the specific objectives, our work on this project led to several new and exciting outcomes. We were also to use our expertise and efforts to contribute to many community efforts.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Code Coverage Status of the ARC Code RCT

The Argonne Reactor Code (ARC) software system supports users in their fast reactor design goals by providing neutronic, thermal-hydraulic, and structural analysis capabilities. REBUS plays a pivotal role in the ARC system as the primary fuel cycle analysis capability for fast reactor problems. Over its 60 year history, ARC software usage with REBUS has been applied to numerous fast and thermal spectrum reactor analysis projects with good to excellent comparison against experiments. The RCT code is a later addition and uses the REBUS restart files to define its input. The RCT code was built to provide pin depletion details on EBR-II models and thus many features of RCT were specifically tailored to the needs of EBR-II models. Additional approximations were invoked which are likely only valid for the EBR-II reactor and the particular fuel management that was done for it. The purpose of the present work is to identify a set of test problems for RCT and assess the code coverage for those test problems. The goal is to document what parts of the existing RCT code are touched by the set of test problems and which are not. Because no detailed verification work has been done on RCT, the existing regression testing suite was chosen for the code coverage assessment. The code coverage analysis of RCT was performed with the Code Coverage Tool of the Intel Fortran compiler which requires modifications to the compilation of RCT. The detailed coverage tables are given for each part of RCT. As will be discussed and shown, some parts of the RCT capability that are known to be used by the EBR-II analysis work are not tested by the regression testing suite. These aspects should be resolved before major source code changes are taken for the RCT software. Because REBUS and DIF3D are not subroutines of RCT, the coverage changes in both of those codes is not altered by RCT. The same is true for all of the modules of DIF3D that are used by RCT such as SYSLIB and SEGLIB.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Mosaicked Historic Airborne Imagery from Seward Peninsula, Alaska, Starting in the 1950's

Historical airborne imagery for each Seward Peninsula NGEE Arctic site - Teller, Kougarok, Council - were obtained with multiple years for each site in order to identify landscape changes over time when compared to more recent imagery. This dataset includes mosaicked, geolocated and, where possible, orthorectified, historic airborne and recent satellite imagery. The older photos were sourced from the USGS Earth Explorer site and the newer, satellite imagery is from the Statewide Digital Mapping Initiative (SDMI) project managed by the Geographic Information Network of Alaska on behalf of the state of Alaska. Check with Jessica Cherry (jessica.cherry@alaska.edu), University of Alaska, and Lisa Wirth (lisa@gina.alaska.edu), Geographic Information Network of Alaska (GINA) and the Statewide Digital Mapping Initiative (SDMI), for citation information and redistribution restrictions of the imagery. Dataset includes the following types of files: *.tif, *.txt, *.pdf, *.jpg, *.xml. Please refer to metadata files included inside the data folders for detailed descriptions of the data products. The use of historical aerial photographs in combination with more recent satellite imagery has proven effective at detecting and quantifying landscape changes. 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↗
DOE OSTI2025

NLR HPC Kestrel Jobs Data

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

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