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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Utah FORGE Phase Native State FALCON Model Files

The submission includes FALCON input file and mesh for the an initial pressure-temperature simulation, and a second set for pressure-temperature-displacement simulation. All simulations are steady state. Data and input for the FORGE Phase 2 native state model were compiled from historic and FORGE specific characterization efforts. The simulations look to find the in-situ pressure, temperature, and stress distributions in the vicinity of the Utah FORGE site. The purpose of the native state model was to establish a baseline for all other modeling analyses—so that the whole modeling team can start from a consistent set of conditions which makes for comparison of results more straightforward and meaningful. Requires the FALCON simulator which can be found in the "FALCON Simulator" link in the submission. Instructions for compiling and running FALCON can be found on the FORGE branch of the FALCON git repository in the README.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

SU-F-T-233: Evaluation of Treatment Delivery Parameters Using High Resolution ELEKTA Log Files

Purpose: As modern linacs have become more technologically advanced with the implementation of IGRT and IMRT with HDMLCs, a requirement for more elaborate tracking techniques to monitor components’ integrity is paramount. ElektaLog files are generated every 40 milliseconds, which can be analyzed to track subtle changes and provide another aspect of quality assurance. This allows for constant monitoring of fraction consistency in addition to machine reliability. With this in mind, it was the aim of the study to evaluate if ElektaLog files can be utilized for linac consistency QA. Methods: ElektaLogs were reviewed for 16 IMRT patient plans with >16 fractions. Logs were analyzed by creating fluence maps from recorded values of MLC locations, jaw locations, and dose per unit time. Fluence maps were then utilized to calculate a 2D gamma index with a 2%–2mm criteria for each fraction. ElektaLogs were also used to analyze positional errors for MLC leaves and jaws, which were used to compute an overall error for the MLC banks, Y-jaws, and X-jaws by taking the root-meansquare value of the individual recorded errors during treatment. Additionally, beam on time was calculated using the number of ElektaLog file entries within the file. Results: The average 2D gamma for all 16 patient plans was found to be 98.0±2.0%. Recorded gamma index values showed an acceptable correlation between fractions. Average RMS values for MLC leaves and the jaws resulted in a leaf variation of roughly 0.3±0.08 mm and jaw variation of about 0.15±0.04 mm, both of which fall within clinical tolerances. Conclusion: The use of ElektaLog files for day-to-day evaluation of linac integrity and patient QA can be utilized to allow for reliable analysis of system accuracy and performance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Deploying and Tracking Software with NCCS Software Provisioning

The National Center for Computational Sciences (NCCS) at Oak Ridge National Laboratory has a long history of deploying ground-breaking leadership-class supercomputers for the U.S. Department of Energy. The latest in this line of supercomputers is Frontier, the first supercomputer to break the exascale barrier (1018 floating-point operations per second) on the TOP500 list. Frontier serves a wide array of scientific domains, from traditional simulation-based workloads to newer AI and Machine Learning workloads. To best serve the NCCS user community, NCCS uses Spack to deploy a comprehensive software stack of scientific software packages, providing straightforward access to these packages through Lmod Environment Modules. Maintaining a large software stack while also including multiple new compiler releases each year is a very time-consuming task. Additionally, it is not straightforward to provide a software stack alongside existing vendor-provided software such as the HPE/Cray Programming Environment (CPE), and existing CPE, Spack, and Lmod integration does not allow for multiple versions of GPU libraries such as AMD’s ROCm to be used. To address these challenges and shortcomings, NCCS has developed the NCCS Software Provisioning tool (NSP)1, a tool for deploying and monitoring software stacks on HPC systems. NSP allows NCCS to quickly and effectively provision software stacks from the ground up using template-driven recipes and configuration files. NSP is successfully deployed on Frontier and several other NCCS clusters, enabling the NCCS software team to quickly deploy software stacks for newly-released compilers, expand current software offerings, better support GPU-based software, and monitor Lmod module usage to identify unused software packages that can be removed from the software stack. In this work, we discuss the shortcomings of the previous CPE, Spack, and Lmod usage at NCCS, provide further details on the implementation and structure of NSP, then discuss the benefits that NSP provides.

Rentschler, Asa [ORNL] (ORCID:0009000597694743)↗
DOE OSTI2020

Data for Robust Paths to Net Greenhouse Gas Mitigation and Negative Emissions via Advanced Biofuels

This zip file contains a UNIX-format DayCent model executable, input files, automation code, and associated directory structure necessary to re-produce the DayCent analysis underlying the manuscript. The main script “autodaycent.py” (written for Python 2.7) opens an interactive command line routine that facilitates: Calibrating the DayCent pine growth model; Initializing DayCent for a set of case studies sites; Executing an ensemble of model runs representing case study site reforestation, grassland restoration, or conversion to switchgrass cultivation; and Results analysis & generation of manuscript Fig. 3. Note that the interactive analysis code requires that all input files to be contained in the directory structure as uploaded, without modification. Executable versions of the DayCent model compatible with other operating systems are available upon request.

Feedstock Production↗
DOE OSTI2021

UNH TDP - Concurrent Measurements of Inflow, Power Performance, and Loads for a Grid-Synchronized Vertical Axis Cross-Flow Turbine Operating in a Tidal Estuary

This data was collected between October 12 and December 15 of 2021 at the University of New Hampshire (UNH) and Atlantic Marine Energy Center (AMEC) turbine deployment platform (TDP). This data set includes over 29 days of grid connected turbine operation during this 65 day time frame. The priority for this measurement campaign was to collect data while the turbine was electrically connected to the grid by means of a rectifier and inverter. The Fall_2021_UNH_Measurement_Timeline.png highlights when each instrument was functioning and the Fall_2021_UNH_Test_Log.jpg indicates the four main regions for analysis available from this measurement campaign. The TDP is a floating structure moored on the Portsmouth facing side of Memorial Bridge pier #2, which spans the Piscataqua River between Portsmouth, NH and Kittery, ME. The Piscataqua River connects the Great Bay Estuary to the Gulf of Maine and the river currents are dominated by tidal forcing with water velocities exceeding 2.5 m/s during spring ebb tides at this site which were previously characterized by Kaelin Chancey (Assessment Of The Localized Flow And Tidal Energy Conversion System At An Estuarine Bridge - UNH MS Thesis 2019). The turbine under test was a modified New Energy Corporation (Calgary, CA) model EVG-025 4-blade H-Darrius type vertical axis cross flow turbine that rotates in the clockwise direction with a rotor diameter of 3.2m and blade length of 1.7m. The hydro-foil profile was a NACA 0021 with a 10 inch chord length and a blade preset pitch angle of +4deg with a positive angle corresponding with the toe in direction. The standard EVG-025 has a rotor diameter of 3.4m and its rated power output is 25kW at 3 m/s. The rotor diameter was reduced to accommodate the size of the existing TDP moon-pool. This project was pursued to quantify device performance for cross flow turbines operating in a marine environment. Accurate physical models, to characterize cross flow turbine performance, require real operational data sets due to the complexity of blade fluid interactions. This data can help support model development which will help predict turbine performance when analyzing perspective project locations in the future. Instrumentation was deployed to measure; water speed/direction, electrical power output, turbine shaft speed, turbine thrust force, and platform motion. Concurrent measurements of these parameters allow for correlations (cause and affect) to be inferred, allowing for characterization of device performance over a range of operating conditions. Water currents were measured using Acoustic Doppler Current Profilers (ADCP's) and Acoustic Doppler Velocimeters (ADV's) directly upstream and downstream of the turbine for inflow, wake and turbulence measurements. Electrical power output was measured using the Voltsys rectifier and the Shark power meter. Shaft speed was calculated based on the Voltsys measurements of the permanent magnet three phase generator AC generation frequency, coupled directly to the cross flow turbine under test (i.e., no gear box). Platform motions were captured using a Yost IMU (inertial measurement unit). Turbine thrust loading was measured using a reaction arm about the turbine deployment platform spanning beam, where two bi-directional load cells were connected to the system via a pinned connection. This submission includes zipped folders for each instrument containing quality controlled (QC'd) data in daily .csv files for the relevant duration specific to each instrument, along with separate .csv file that contains the units for each variable. Some instrument daily files are quite large and can pose a challenge for a visual spreadsheet editor to open. A processing software like MATLAB or Python is recommended. Note the degree of QC varied between each instrument due to time constraints. Particular time and attention was given to perform quality control tests on the acoustic based instruments that are particularly suscep...

16 TIDAL AND WAVE POWER↗
DOE OSTI2015

Comparison of Ring-Buffer-Based Packet Capture Solutions

Traditional packet-capture solutions using commodity hardware incur a large amount of overhead as packets are copied multiple times by the operating system. This overhead slows sensor systems to a point where they are unable to keep up with high bandwidth traffic, resulting in dropped packets. Incomplete packet capture files hinder network monitoring and incident response efforts. While costly commercial hardware exists to capture high bandwidth traffic, several software-based approaches exist to improve packet capture performance using commodity hardware.

Barker, Steven Andrew↗
DOE OSTI2017

Development of Automated Procedures to Generate Reference Building Models for ASHRAE Standard 90.1 and India’s Building Energy Code and Implementation in OpenStudio

This paper describes a software system for automatically generating a reference (baseline) building energy model from the proposed (as-designed) building energy model. This system is built using the OpenStudio Software Development Kit (SDK) and is designed to operate on building energy models in the OpenStudio file format.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

TF9 Dataset Analysis

Incident Overview: In the time between November 2, 2019 and November 11, 2019, WheelByte was plagued by breaches in security. These insecurities led to breaches in customer data, company data, and even the death of an employee, Matthew Swift. They have launched an investigation into the company’s computer systems in hopes to find the root cause. We have been provided with the following artifacts from WheelByte: memory images, disk images, network packet captures, and emails. We have found multiple cyber-system attacks against WheelByte. Our investigation lasted from July 13th - August 3rd, 2023. WheelByte allowed us to look at any and every file, and there were no restrictions on what we could or could not use in our investigation. By the end of our investigation, we have been able to deduce who is behind the attack, what they have done, and why they did it. A company that is closely related to WheelByte is called Slyde. Slyde sells electric scooters and it is known that the Chief Executive Officer (CEO) of Slyde, Kimberly Holmes, sees WheelByte as a threat to business, as Wheelbyte sells electric skateboards. We have been able to deduce that Slyde is likely behind many of the malicious attacks. We have seen exfiltration addresses to Slyde domains, along with other Slyde information within their malware. We can see lots of traffic to and from Slyde Internet Protocol (IP) addresses. This may be an attempt to cripple WheelByte’s productivity to remove Slyde’s competitor from the market.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecasting

The BuildingsBench datasets consist of: - Buildings-900K: A large-scale dataset of 900K buildings for pretraining models on the task of short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock. - 7 real residential and commercial building datasets for benchmarking two downstream tasks evaluating generalization: zero-shot STLF and transfer learning for STLF. Buildings-900K can be used for pretraining models on day-ahead STLF for residential and commercial buildings. The specific gap it fills is the lack of large-scale and diverse time series datasets of sufficient size for studying pretraining and finetuning with scalable machine learning models. Buildings-900K consists of synthetically generated energy consumption time series. It is derived from the NREL End-Use Load Profiles (EULP) dataset (see link to this database in the links further below). However, the EULP was not originally developed for the purpose of STLF. Rather, it was developed to "...help electric utilities, grid operators, manufacturers, government entities, and research organizations make critical decisions about prioritizing research and development, utility resource and distribution system planning, and state and local energy planning and regulation." Similar to the EULP, Buildings-900K is a collection of Parquet files and it follows nearly the same Parquet dataset organization as the EULP. As it only contains a single energy consumption time series per building, it is much smaller (~110 GB). BuildingsBench also provides an evaluation benchmark that is a collection of various open source residential and commercial real building energy consumption datasets. The evaluation datasets, which are provided alongside Buildings-900K below, are collections of CSV files which contain annual energy consumption. The size of the evaluation datasets altogether is less than 1GB, and they are listed out below: 1. ElectricityLoadDiagrams20112014 2. Building Data Genome Project-2 3. Individual household electric power consumption (Sceaux) 4. Borealis 5. SMART 6. IDEAL 7. Low Carbon London A README file providing details about how the data is stored and describing the organization of the datasets can be found within each data lake version under BuildingsBench.

Array↗
DOE OSTI2013

Representativeness-based Sampling Network Design for the State of Alaska

This data set collection consists of data products described in Hoffman et. al., 2013. Resource and logistical constraints limit the frequency and extent of environmental observations, particularly in the Arctic, necessitating the development of a systematic sampling strategy to maximize coverage and objectively represent environmental variability at desired scales. A quantitative methodology for stratifying sampling domains, informing site selection, and determining the representativeness of measurement sites and networks is described here. Multivariate spatiotemporal clustering was applied to down-scaled general circulation model results and data for the State of Alaska at 4 km2 resolution to define multiple sets of ecoregions across two decadal time periods. Maps of ecoregions for the present (2000-2009) and future (2090-2099) were produced, showing how combinations of 37 characteristics are distributed and how they may shift in the future. Representative sampling locations are identified on present and future ecoregion maps. A representativeness metric was developed, and representativeness maps for eight candidate sampling locations were produced. This metric was used to characterize the environmental similarity of each site. This analysis provides model-inspired insights into optimal sampling strategies, offers a framework for up-scaling measurements, and provides a down-scaling approach for integration of models and measurements. These techniques can be applied at different spatial and temporal scales to meet the needs of individual measurement campaigns. This dataset contains one zipped file, one .txt file, and one .sh file.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 OSTI2014

Soil Physicochemical Characteristics from Ice Wedge Polygons, Utqiagvik (Barrow), Alaska, Ver. 1

This dataset provides details about soil cores (active layer and permafrost) collected from ice-wedge polygons during field expeditions to Barrow Environmental Observatory, Alaska in April, 2012 and 2013. Core information available are exact core locations; soil horizon descriptions and characteristics; and fundamental soil physico-chemical properties. In February 2016, two columns (carbon and carbon:nitrogen in soil layer) were added to the data but no existing data values changed. See documentation. The new filename is version 2. In July 2016, data for two soil cores were added. The new filename is version 3. This dataset includes two pdf files, one csv file, and one xlsx file.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 OSTI2017

Plant Available Nutrients, Utqiagvik (Barrow), Alaska, Ver. 1

This dataset reports measurements of plant available nutrients made using Plant Root Simulator (PRS) probes (Western Ag Innovations Inc.) during 2012 and 2013 at the Next-Generation Ecosystem Experiments (NGEE) Arctic field site near Utqiagvik (Barrow), Alaska. In 2012, Ca, Mg, K, P, Fe, Mn, Cu, Zn, B, S, Pb, Al, Cd, NO3-N and NH4-N availability were measured during spring, summer and winter from probes installed in the centers, edges and troughs of four polygons in each of four areas of contrasting moisture regime and polygon type (Areas A, B, C, and D of Intensive Site 1). In 2013, probes were installed in centers, edges and troughs of four polygons in each of two areas (A and B, with low-centered and high-centered polygons respectively) at two-week intervals and at 3 soil depths to capture fine-scale seasonal dynamics of NO3-N and NH4-N. PRS probes are ion exchange resin membranes held in plastic supports that are inserted into soil to measure ion supply in situ. The anion and cation exchange with the membrane is intended to mimic plant uptake and thus provide a relevant measure of soil nutrient bioavailability. Measurements are made per area of probe membrane and cannot be converted to concentrations or related to soil volume. This dataset includes five csv files and three pdf files.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 OSTI2017

Soil Organic Carbon Degradation during Incubation, Utqiagvik (Barrow), Alaska, 2012

This dataset provides information about soil organic carbon decomposition in Barrow soil incubation studies. The soil cores were collected from low-center polygon (Area A) and were incubated in the laboratory at different temperatures for up to 60 days. Transformations of soil organic carbon were characterized by UV and FT-IR, and small organic acids in water-soluble carbons were quantified by ion chromatography during the incubation (Herndon et al., 2015). This dataset includes one pdf file and four csv files.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 OSTI2016

Modeling the Spatio-Temporal Variability in Subsurface Thermal Regimes Across a Low-Relief Polygonal Tundra Landscape: Modeling Archive

Vast carbon stocks stored in permafrost soils of Arctic tundra are under risk of release to atmosphere under warming climate. Ice--wedge polygons in the low-gradient polygonal tundra create a complex mosaic of microtopographic features. The microtopography plays a critical role in regulating the fine scale variability in thermal and hydrological regimes in the polygonal tundra landscape underlain by continuous permafrost. Modeling of thermal regimes of this sensitive ecosystem is essential for understanding the landscape behaviour under current as well as changing climate. We present here an end-to-end effort for high resolution numerical modeling of thermal hydrology at real-world field sites, utilizing the best available data to characterize and parameterize the models. We develop approaches to model the thermal hydrology of polygonal tundra and apply them at four study sites at Barrow, Alaska spanning across low to transitional to high-centered polygon and representative of broad polygonal tundra landscape. A multi--phase subsurface thermal hydrology model (PFLOTRAN) was developed and applied to study the thermal regimes at four sites. Using high resolution LiDAR DEM, microtopographic features of the landscape were characterized and represented in the high resolution model mesh. Best available soil data from field observations and literature was utilized to represent the complex heterogeneous subsurface in the numerical model. This data collection provides the complete set of input files, forcing data sets and computational meshes for simulations using PFLOTRAN for four sites at Barrow Environmental Observatory. It also documents the complete computational workflow for this modeling study to allow verification, reproducibility and follow up studies. This dataset includes two zipped files and one .pdf file.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 OSTI2017

Surface and Active Layer Pore Water Chemistry from Ice Wedge Polygons, Utqiagvik (Barrow), Alaska, 2013-2014

This data set reports the results of spatial surveys of aqueous geochemistry conducted at Intensive Site 1 of the Barrow Environmental Observatory in 2013 and 2014 (Herndon et al., 2015). Surface water and soil pore water samples were collected from multiple depths within the tundra active layer of different microtopographic features (troughs, ridges, center) of a low-centered polygon (Area A), high-centered polygon (Area B), flat-centered polygon (Area C), and transitional polygon (Area D). Reported analytes include dissolved organic and inorganic carbon, dissolved carbon dioxide and methane, major inorganic anions, and major and minor cations. This dataset contains one csv file and one pdf file.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

CO2 and CH4 Surface Flux, Soil Profile Concentrations, and Stable Isotope Composition, Utqiagvik (Barrow), Alaska, 2012-2013

In August-October 2012 and June-October 2013, co-located measurements were made of surface CH4 and CO2 flux, soil pore space concentrations and stable isotope compositions of CH4 and CO2, and subsurface temperature and soil moisture. Measurements were made in intensive study site 1 areas A, B, and C, and from the site 0 and AB transects, from high-centered, flat-centered, and low-centered polygons, from the center, edge, and trough of each polygon. This dataset includes one pdf file and four csv files.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 OSTI2016

Addressing numerical challenges in introducing a reactive transport code into a land surface model: A biogeochemical modeling proof-of-concept with CLM-PFLOTRAN 1.0: Modeling Archive

This Modeling Archive is in support of an NGEE Arctic discussion paper available at doi:10.5194/gmd-9-927-2016. The purpose is to document the simulations to allow verification, reproducibility, and follow-up studies. This dataset contains shell scripts to create the CLM-PFLOTRAN cases, specific input files for PFLOTRAN and CLM, outputs, and python scripts to make the figures using the outputs in the publication. Through these results, we demonstrate that CLM-PFLOTRAN can approximately reproduce CLM results in selected cases for the Arctic, temperate and tropic sites. In addition, the new framework facilitates mechanistic representations of soil biogeochemistry processes in the land surface model. This dataset includes four zipped files and one .pdf file.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 OSTI2019

NGEE Arctic Plant Traits: Soil Depth, Kougarok Road Mile Marker 64, Seward Peninsula, Alaska, Beginning 2016

Soil depth was measured at the Kougarok site located on the Seward Peninsula using a thaw depth probe inserted into the ground until the point of contact with frozen soil or rock. Measurements were made at vegetation plots, intensive stations, and reference points. Three to four replicate measurements were made during the observation period and the presence of rocks was noted when possible. This dataset includes one pdf file and one csv file.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↗