SEARCH · Engineering Papers

Results for “ArcGIS”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

61 records · Page 4

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Biomass Resources and Emission Reduction Potential of Agricultural and Livestock Residues in Mainland China from 2013 to 2022

Controlling carbon emissions is a global goal, and China is actively implementing carbon reduction measures. As a major agricultural nation, China has considerable potential for developing agricultural residues as renewable and environmentally friendly biomass energy. In this study, we obtained data on crop yields, crop-to-grain ratios, and livestock excretion coefficients to calculate the biomass resources of agricultural and livestock residues in Chinese provinces from 2013 to 2022. Crop residue biomass resources showed a distribution pattern with higher levels in the north than in the south and the east than in the west. Henan and Heilongjiang provinces consistently had the highest resource levels, exceeding 35 million tons annually for 10 years. The biomass resources from livestock residues were relatively abundant in Sichuan, Henan, Yunnan, Shandong, Hunan, and Inner Mongolia. Inner Mongolia, Sichuan, Shandong, and Henan had the greatest potential for CO2 emission reductions, primarily located in regions abundant in biomass resources and with high traditional energy consumption levels. ArcGIS was used to apply natural break classification to categorize the potential for emission reductions from agricultural and livestock residues across China from 2013 to 2022 into five classes. Based on factors such as crop planting area and livestock numbers, the spatiotemporal distribution of factors influencing the quantity of biomass resources was examined using Geographically and Temporally Weighted Regression. A tailored and integrated approach should be used for biomass, and the development of biomass energy should be promoted through policy support and technological innovation.

Environmental Sciences & Ecology↗

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 OSTI2022

Per- and Polyfluoroalkyl Substances: A Preliminary Evaluation of Groundwater Contamination in the Western States

Per- and Polyfluoroalkyl Substances (PFAS) are a manufactured group of chemicals that have been used since the 1940s. PFAS break down very slowly and studies show that exposure to these chemicals may cause negative reproductive effects, developmental delays in children, increased risk of some cancers, and interference with the body’s natural hormones. On June 15, 2022, EPA issued interim updated drinking water health advisories for the two most widely studied PFAS, perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS). The concentrations for the health advisory limits are 0.004 parts per trillion (ppt) and 0.02 ppt respectively. Because data is often reported in combined PFOA and PFOS concentrations, and for ease of data visualization, concentrations of the two chemicals will be discussed as a single, summed value and will be compared to the combined health advisory limits of 0.024 ppt. Due to the recently published near-zero health advisory limits and the known negative health effects, a map of estimated PFAS groundwater contamination was created using ArcGIS Pro and geostatistical tools to inform the public and determine which regions are most at-risk.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Hydropower Resilience Database for Assessing Microgrid Formation Capability and Enhancing Power Grid Resilience

In the face of increasing frequency of extreme events, enhancing resilience and reliability of energy infrastructure demands innovative solutions. Hydropower, with its inherent generation flexibility and grid-forming capability, offers significant potential for enhancing power grid resilience through the establishment of microgrids. To enable informed decision-making and strategic planning, we present the Hydropower Resilience Database (HRD) that integrates data from various sources such as Oakridge National Laboratory (ORNL) HydroSource, National Inventory of Dams, and US Western Grid database. By integrating relevant information on hydropower plant characteristics, including dams, reservoirs, and connected electrical grid, this resource enables hydropower plant owners, utilities, and community stakeholders to identify and evaluate the feasibility of using hydropower resources in microgrids to support nearby communities and critical infrastructure in various challenging scenarios. Using HRD, a set of metrics are evaluated to quantify the capability of hydropower plants to support essential microgrid functions. An interactive tool is developed using ArcGIS, enabling the visualization and analysis using HRD. Our research contributes to strategic planning efforts for fortifying energy infrastructure, ensuring reliable power supply during disruptions, and advancing the development of robust and resilient energy systems in regions susceptible to grid vulnerabilities.

13 HYDRO ENERGY↗

DOE OSTI2023

Hydropower Resilience Database for Assessing Microgrid Formation Capability and Enhancing Power Grid Resilience

13 HYDRO ENERGY↗

DOE OSTI2023

Progress in the Verification and Validation Efforts for START: A Spent Fuel Routing Tool

The U.S. Department of Energy’s (DOE) Office of Nuclear Energy (DOE-NE) is planning for an integrated waste management approach to transport, store, and dispose of spent nuclear fuel (SNF) and other high-level radioactive waste (HLW) as part of the Integrated Waste Management (IWM) program [1]. In support of this effort, the Stakeholder Tool for Assessing Radioactive Transportation (START) is being developed within the IWM program [2, 3]. This is a web-based decision support tool that can be used to analyze geospatial data related to the transportation of SNF and HLW. START is designed as a web-based application using an ArcGIS server through which the user can select the origin and destination of the route [4]. This is followed by selection of the mode of transportation of choice based on user preference. Some of the modes available to the user include rail, heavy haul truck, and barge. The option of utilizing more than one mode of transportation (intermodal transportation) is also implemented in START. A few examples of intermodal transportation include barge to rail, and heavy haul truck to rail. It must be noted that some routes might not have access to all modes of transportation, depending on the infrastructure availability at the origin and destination sites. Users can also select any stops or barriers they would like to introduce in the routes. This is followed by selection of the routing criteria of interest. Three primary routing options available to the user include minimum population, minimum distance, and minimum time. Apart from that, a few other options include accounting for a weighted average of the three aforementioned routing options. The next step involves the selection of the buffer distance of interest which includes the two choices available of 800 m and 2500 m, respectively. Finally, an option to select any prohibited rail carriers that the user does not wish to use is available. After making these selections, a route is ready to be created.

Gadey, Harish Reddy↗

DOE OSTI2021

Appendices for Geothermal Exploration Artificial Intelligence Report

The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especially regarding finding locations for viable EGS sites. This submission includes the appendices and reports formerly attached to the Geothermal Exploration Artificial Intelligence Quarterly and Final Reports. The appendices below include methodologies, results, and some data regarding what was used to train the Geothermal Exploration AI. The methodology reports explain how specific anomaly detection modes were selected for use with the Geo Exploration AI. This also includes how the detection mode is useful for finding geothermal sites. Some methodology reports also include small amounts of code. Results from these reports explain the accuracy of methods used for the selected sites (Brady Desert Peak and Salton Sea). Data from these detection modes can be found in some of the reports, such as the Mineral Markers Maps, but most of the raw data is included the DOE Database which includes Brady, Desert Peak, and Salton Sea Geothermal Sites.

15 GEOTHERMAL ENERGY↗