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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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This is the AmeriFlux version of the carbon flux data for the site US-INc INFLUX - Downtown Indianapolis (Site-3). Site Description - Urban site, tower is a lattice communications tower.
This is the AmeriFlux version of the carbon flux data for the site US-INf INFLUX - East 21st St (Site 2). Site Description - Urban site. Tower is a lattice communications tower.
This is the AmeriFlux version of the carbon flux data for the site US-INg INFLUX - Wayne Twp Comm (Site-7). Site Description - Urban site, tower is a lattice communications tower
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xBL NEON Blandy Experimental Farm (BLAN). This is the FLUXNET version of the carbon flux data for the site US-xBL NEON Blandy Experimental Farm (BLAN) produced by applying the standard ONEFlux (1F) software. Site Description - The Blandy Experimental Farm contains several land use types typically found in rural-suburban landscapes. This mix of land use types is typical and representative in the Middle Atlantic Domain. This site will be under increasing ecological pressure from urbanization within the rapidly growing megapolitan area. The amount of land cover and associated ecosystem processes in each of these land use types is expected to change over time.
This is the AmeriFlux version of the carbon flux data for the site US-DBk Berkeley Way West. Site Description - This tower is located in dowtown Berkeley, CA on the roof of Berkeley Way West, an 8 story building. The surrounding area is urbanized and includes a major commercial corridor, residential areas, and the UC Berkeley campus within 1 km of the site. Multi-unit residential construction activities took place near the site on an intermittent basis throughout tower operation.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site MX-PMm Puerto Morelos mangrove. This is the FLUXNET version of the carbon flux data for the site MX-PMm Puerto Morelos mangrove produced by applying the standard ONEFlux (1F) software. Site Description - The tower was located at the “Dr. Alfredo Barrera Marín” Botanical Garden. The surface monitored is covered with basin mangrove (sporadically flooded wetland) that grows in a fringe parallel to the northeastern coast (ca. 1 km) of the Yucatan Peninsula. The dominant species are Rhizophora mangle and Conocarpus erectus, which grow intertwined and relatively stunted (max tree height is 5m). Under the influence of trade winds and frequent stroms. The vegetation is regenerating after complete defoliation due to hurricane Wilma (cat. 4) passing in October 2005. The site is sporadically flooded by rainfall soil saturation excess; there are no marine surface water inputs but ground water level fluctuations show tidal signals. The terrain of the monitored surface is flat, while the area downwind of the tower presents a slope which corresponds to an ancient coast line. There is extensive urban development in the coastal dune along the coast mainly for tourism use (Mayan Riviera).
This is the AmeriFlux version of the carbon flux data for the site US-NYj NYSM - Queens. Site Description - Located in an urban setting. It is on a city rooftop. Obstructions within 100m include buildings, pavement. The soil type is N/A - Rooftop. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a NYC Profiler and Standard meteorological site.
This is the AmeriFlux version of the carbon flux data for the site US-NYk NYSM - Staten Island. Site Description - Located in an urban setting. It is on a rooftop. Obstructions within 100m include tall buildings. The soil type is N/A - Rooftop. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a NYC Profiler site and a Standard meteorological site.
This is the AmeriFlux version of the carbon flux data for the site US-NYn NYSM - Brooklyn. Site Description - Located in an urban setting. It is on a city rooftop. Obstructions within 100m include buildings, pavement. The soil type is N/A - Rooftop. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a NYC Standard meteorological site.
ABSTRACT Crustal deformation in the central Basin and Range between the Colorado plateau and the Eastern California Shear Zone is active but slow, making it a challenge to assess how strain is distributed and crustal motion transferred. However, knowledge of strain rates is very important, particularly for addressing the seismic hazard for both the Las Vegas urban area and the site of the proposed Yucca Mountain nuclear waste repository, in southern Nevada. Global Positioning System (GPS) data provide important constraints, particularly now that the GPS network in the area has substantially expanded in recent years. However, because deformation is slow, it is important to mitigate any transient tectonic and nontectonic signals to obtain the most accurate long-term interseismic motion and robust estimation of strain rates. We use data from all GPS stations in the region including both long-running continuous and semicontinuous stations. We model and remove postseismic displacements at these stations using source parameters for 41 events, dating back to the 1700 Cascadia megathrust earthquake, which contribute significantly to the deformation field within the central Basin and Range. We also remove correlated noise from the time series with the common-mode component imaging technique. We find that removal of both the postseismic transients and common-mode noise substantially reduces the uncertainties and spatial variation in the velocities. We find east–west extension across the Las Vegas Valley of 0.5–0.6 mm/yr. The interseismic strain rate field, calculated with the final velocities, reveals higher strain rates through southern Nevada than in previous studies, with rates within Las Vegas Valley of 8.5±2.4×10−9 yr−1. Our results also confirm shear along the Pahranagat shear zone, but the estimated amplitude is strongly affected by postseismic relaxation.
During the past few years, distributed acoustic sensing (DAS) has become an invaluable tool for recording high-fidelity seismic wavefields with great spatiotemporal resolutions. However, the considerable amount of data generated during DAS experiments limits their distribution with the broader scientific community. Such a bottleneck inherently slows down the pursuit of new scientific discoveries in geosciences. Here, we introduce PubDAS—the first large-scale open-source repository where several DAS datasets from multiple experiments are publicly shared. PubDAS currently hosts eight datasets covering a variety of geological settings (e.g., urban centers, underground mines, and seafloor), spanning from several days to several years, offering both continuous and triggered active source recordings, and totaling up to ~90 TB of data. Here this article describes these datasets, their metadata, and how to access and download them. Some of these datasets have only been shallowly explored, leaving the door open for new discoveries in Earth sciences and beyond.
Fermilab has developed a novel concept for an industrial electron linac using Nb₃Sn coating technology and conduction cooling. We will show the range of multi-cavity linac designs targeted toward various applications. We will also discuss technology development status with results on conduction cooling of SRF cavities based on cryocoolers, which removes the need for liquid Helium, thus making SRF technology accessible to industrial applications. These conduction-cooled linacs can generate electron beam energies up to 10 MeV in continuous-wave operation and can reach higher power (>=1 MW) by combing several modules. Compact and light enough to mount on mobile platforms, our machine is anticipated to enable new in-situ environmental remediation applications such as waste-water treatment for urban areas, X-ray medical device sterilization, and innovative pavement applications. We also show cost-economics and key R&D areas that much be addressed for a practical machine.
This paper describes the application of a large-scale active learning method to characterize the parameter space of a computational agent-based model developed to investigate the impact of CommunityRx, a clinical information-based health intervention that provides patients with personalized information about local community resources to meet basic and self-care needs. Additionally, the diffusion of information about community resources and their use is modeled via networked interactions and their subsequent effect on agents' use of community resources across an urban population. A random forest model is iteratively fitted to model evaluations to characterize the model parameter space with respect to observed empirical data. We demonstrate the feasibility of using high-performance computing and active learning model exploration techniques to characterize large parameter spaces; by partitioning the parameter space into potentially viable and non-viable regions, we rule out regions of space where simulation output is implausible to observed empirical data. We argue that such methods are necessary to enable model exploration in complex computational models that incorporate increasingly available micro-level behavior data. We provide public access to the model and high-performance computing experimentation code.
This report details modeled energy performance and savings from retrofit packages in prototypical school buildings in climate zones throughout the U.S. The information herein serves as a reference for elementary and secondary schools interested in implementing retrofit packages in their facilities for energy savings as well as health and safety benefits. School models developed for simulating package performance differentiated between rural and urban environments. Simulations were run for 10 distinct climate zones covering a range of climate conditions throughout the U.S. Results include savings estimates for electricity, natural gas, CO 2 emissions, and annual utility costs for nine different retrofit packages that combine energy conservation measures, including HVAC controls and equipment upgrades, lighting efficiency upgrades, and electrification technologies such as heat pumps for space conditioning and domestic hot water. Nine additional retrofit packages were also developed for elementary schools and modeled in two climate zones. Appendix B describes the additional retrofit packages and presents savings estimates.
The Renewable Energy Potential (reV) model is a platform for detailed assessment of renewable energy (RE) resources and their geospatial intersection with grid infrastructure and land use characteristics. The reV model currently supports photovoltaic (PV), concentrating solar power (CSP) and land-based wind turbine technologies. Modules in the reV framework function at different spatial and temporal resolutions, allowing for assessment of resource potential, technical potential and supply curves at varying levels of detail. The platform runs on NREL's High Performance Computing system, providing scalable and efficient performance from a single location all the way up to continental scales, for a single year or decades of time series resource data. Coupled with NREL's System Advisor Model (SAM), reV supports resource assessment from 5-minute to hourly temporal resolution and provides for analysis of long-term (i.e., year-on-year) variability of RE generation (e.g., interannual variability and exceedance probabilities). Technical potential is measured as a function of resource potential and limitations put on developable land area defined by the user. For example, the user can limit development by land ownership, terrain, land use/cover, and urban areas, as well as custom inputs. Technology, grid interconnection and operation costs, based on the latest market data and future projections, are also embedded in the model. The supply curve module is a spatial sorting algorithm based on plant siting, grid interconnection cost, and regional competition, which provides a geographically discrete estimate of levelized cost of electricity (LCOE) and supply (i.e., capacity) for specific renewable technologies. The reV model currently provides broad coverage across North America, South and Central Asia, South America and South Africa to inform national- and international-scale analyses as well as regional infrastructure and deployment planning.
The purpose of this project is to examine the energy impact of urban-scale traffic for the Los Angeles Basin by developing and implementing a scalable traffic assignment model. An energy optimization function will be posed and when integrated into the optimization code for travel assignment it can be mathematically proven to converge. The energy optimization function can then be compared to the typical travel time optimization that is traditionally used in traffic assignment models. The analysis will begin with static traffic assignment models with the routing for all origin and destinations computed in parallel on high performance computing facilities. Convergence of the numerical methods rely on the solution of convex programs (or extensions of these). This step will mostly consist of demonstrating the ability to parallelize the Frank Wolfe algorithm on various platforms.
The purpose of this project is to examine the energy impact of urban-scale traffic for the Los Angeles Basin by developing and implementing a scalable traffic assignment model. An energy optimization function will be posed and when integrated into the optimization code for travel assignment it can be mathematically proven to converge. The energy optimization function can then be compared to the typical travel time optimization that is traditionally used in traffic assignment models. The analysis will begin with static traffic assignment models with the routing for all origin and destinations computed in parallel on high performance computing facilities. Convergence of the numerical methods rely on the solution of convex programs (or extensions of these). This step will mostly consist of demonstrating the ability to parallelize the Frank Wolfe algorithm on various platforms. This work will contribute to LBNL’s efforts to develop new processes, analytical tools, program designs, and business models to advance the state of the art in next-generation sustainable transportation solutions.
The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019 and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Advanced Fueling Infrastructure Pillar. This Pillar investigated the charging infrastructure needs of electric ride-hailing and car-sharing vehicles, automated shuttle buses, and freight-delivery truck fleets. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant Pillar’s Capstone Report.