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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Application Experiences on a GPU-Accelerated Arm-based HPC Testbed

This paper assesses and reports the experience of ten teams working to port, validate, and benchmark several High Performance Computing applications on a novel GPU-accelerated Arm testbed system. The testbed consists of eight NVIDIA Arm HPC Developer Kit systems, each one equipped with a server-class Arm CPU from Ampere Computing and two data center GPUs from NVIDIA Corp. The systems are connected together using InfiniBand interconnect. The selected applications and mini-apps are written using several programming languages and use multiple accelerator-based programming models for GPUs such as CUDA, OpenACC, and OpenMP offloading. Working on application porting requires a robust and easy-to-access programming environment, including a variety of compilers and optimized scientific libraries. The goal of this work is to evaluate platform readiness and assess the effort required from developers to deploy well-established scientific workloads on current and future generation Arm-based GPU-accelerated HPC systems. The reported case studies demonstrate that the current level of maturity and diversity of software and tools is already adequate for large-scale production deployments.

Elwasif, Wael↗
DOE OSTI2023

AmeriFlux FLUXNET-1F US-AR1 ARM USDA UNL OSU Woodward Switchgrass 1

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-AR1 ARM USDA UNL OSU Woodward Switchgrass 1. This is the FLUXNET version of the carbon flux data for the site US-AR1 ARM USDA UNL OSU Woodward Switchgrass 1 produced by applying the standard ONEFlux (1F) software. Site Description - The ARM USDA UNL OSU Woodward Switchgrass 1 tower is located on public land owned by the USDA-ARS Southern Plains Range Research Station in Woodward, Oklahoma. The site is on a former native prairie that is in the process of changing to switchgrass. A second companion site (ARM USDA UNL OSU Woodward Switchgrass 2) is on a former wheat field. In Spring 2009, the former native prairie site was burned, cattle were put on the pasture to graze down emergent grass, and broadleaf herbicide was sprayed. In Summer 2009, the cattle were removed from the pasture, and the site was sprayed with herbicide to kill all grass. In Spring 2010, prior to the planting of switchgrass, final herbicide was sprayed to kill cheat grass and to control broadleaf plants.

Billesbach, Dave↗
DOE OSTI2023

AmeriFlux FLUXNET-1F US-AR2 ARM USDA UNL OSU Woodward Switchgrass 2

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-AR2 ARM USDA UNL OSU Woodward Switchgrass 2. This is the FLUXNET version of the carbon flux data for the site US-AR2 ARM USDA UNL OSU Woodward Switchgrass 2 produced by applying the standard ONEFlux (1F) software. Site Description - The ARM USDA UNL OSU Woodward Switchgrass 2 tower is located on public land owned by the USDA-ARS Southern Plains Range Research Station in Woodward, Oklahoma. The site is on a former wheat field that is in the process of changing to switchgrass. A companion site (ARM USDA UNL OSU Woodward Switchgrass 1) is on a former native prairie. Previous wheat was planted in Fall 2008. In Spring 2009, herbicide was applied to kill the wheat prior to switchgrass planting. Later in the year, the site was sprayed with post-emergence herbicide. In 2010, fertilization occurred before herbicide was sprayed for broadleaf control.

Billesbach, Dave↗
DOE OSTI2020

Constraints on the Parameterization of Convective Cloud Systems from Analyses of ARM Observations and Models

Anthony D. Del Genio has served as principal investigator since the inception of ASR in 2009 as co-chair of the Cloud Lifecycle Working Group (CLWG) and in connection with that as a member of the ASR Science and Infrastructure Steering Committee. In this role he has been intimately involved with oversight of existing and proposed ASR Focus Groups (Vertical Velocity, QUICR, Ice Properties) and thematic interest groups (Mesoscale Organized Convection, Warm Low Clouds, Mixed-Phase Clouds, MJO, Entrainment) whose activities fall under the purview of CLWG. The PI actively participates himself in the Mesoscale Organized Convection and MJO groups, is a Co-Investigator on two successfully implemented IOPs (AMIE, MC3E), and was an unfunded Collaborator on the recently completed FASTER project at BNL under the DOE Modeling Program. The PI has also spent the past three years as a member of the ARM Science Board, which reviews proposals to conduct IOPs at the permanent ARM sites and to deploy the ARM Mobile Facilities. Finally, the PI was a participant in the first ARM-European workshop in 2012 that eventually led to the creation of the SGP supersite concept. This report includes an annotated list of published research results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

ARM Mobile Facility Leaving Alaska for U.S. Southeast

The Atmospheric Radiation Measurement (ARM) user facility will soon begin the process of moving its third mobile facility (AMF3) from Alaska to the Southeastern United States. A workshop in August 2018 identified the Southeast as a priority region of interest for further study by ARM. The Southeast is a region with abundant atmospheric convection, yet the land surface, boundary layer, vegetation, and aerosol properties are markedly different from those seen at ARM’s fixed observatory in Oklahoma, allowing opportunities to explore new research questions. To maximize the scientific value of the new site, DOE’s Biological and Environmental Research program selected a site science team that will help guide the move of AMF3. Brookhaven National Laboratory (BNL) in New York will lead the AMF3 relocation effort. Supported by ARM and DOE’s Atmospheric System Research (ASR), the multi-institutional team is responsible for developing a science plan and initial research project for the Southeastern U.S. deployment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Macro-physical Properties of Shallow Cumulus from Integrated ARM Observations (Final Report)

Fair-weather shallow cumuli (ShCu) play an important role in many climate-related processes. Irregular geometry of ShCu and their strong temporal and spatial variability make it challenging to observe ShCu holistically and to represent them correctly in climate models. To improve ShCu parameterizations, information on both vertically and horizontally resolved cloud properties is required. Commonly, the vertically resolved cloud properties are provided by zenith pointing lidar-radar observations with a very narrow field of view (FOV). Thus, these “pencil-beam” properties may not be representative of a larger surrounding area. Limited number of areal-averaged cloud properties, such as fractional sky cover (FSC), are offered typically by wide-FOV observations. The main goal of our project was to integrate advantages of the narrow-FOV (vertical structure of clouds) and wide-FOV (spatial arrangement of clouds) observations for an improved characterization of single-layer ShCu observed at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for an 18-yr period (2000-2017). There are four major accomplishments of our project, First, an updated operational cloud classification for days with ShCu has been suggested and evaluated through a detailed comparison with the manually curated records. Our classification extends successfully the latest ARM cloud type Value Added Product (VAP) based on the Active Remote Sensing of Clouds (ARSCL) cloud product by incorporating both cloud fraction (CF) provided by narrow-FOV ceilometer data and FSC from wide-FOV images offered by a Total Sky Imager (TSI). Moreover, our classification allows one to identify impact of instrumentation changes at the SGP site, namely the transition to KAZRARSCL with the updated cloud radar, on the identification of periods with single-layer ShCu. Second, a new approach that resolves cloud area distributions for a given region (up to 4x4 km 2 ) has been suggested and cloud equivalent diameters (CEDs) have been estimated for the first time. These estimations have been performed over a wide range of cloud sizes (about 0.01–3.5 km) with high temporal resolution (30s) using wide-FOV TSI images and cloud base height (CBH) provided by complementary narrow-FOV lidar measurements. Our simple and computationally inexpensive approach offers a previously unavailable dataset for process studies in the convective boundary layer and evaluation of ShCu parameterizations in cloud-resolving models. Third, a long-term integrated record of ShCu macrophysical properties has been developed. The developed record represents the longest available compilation of events with ShCu and includes (i) a novel visualization of the spatial variability in cloud cover both along- and across-wind directions, (ii) updated estimates of narrow-FOV CF and wide-FOV FSC, (iii) updated narrow-FOV CBH, and (iv) complementary data, such as wind speed and direction from the 915-MHz Radar Wind Profiler (RWP) data. The developed record has been used successfully to assess conventional observational estimates of cloud cover and their sensitivity to the following two factors: (i) instrument-dependent cloud detection and data merging criteria and (ii) FOV configuration. Fourth, co-variability of the ShCu macrophysical properties and environmental parameters has been analyzed for a 3-yr period (2016-2018). Our initial analysis includes diurnal changes of FSCs obtained for clouds with small, moderate and large CEDs and several environmental parameters, such as lifted condensation level (LCL) and mixed layer height (zi). Preliminary results of our analysis suggest that the horizontal extent of ShCu is controlled substantially by the sign and magnitude of difference between these two parameters (zi-LCL): the CED tends to grow with increase of this difference (zi exceeds LCL). We have initiated relationships between the ShCu and key atmospheric parameters that control both the development and evolution of ShCu using our new data product, which combines effectively the advantages of narrow-FOV data offered by zenith pointing cloud radars and lidars and wide-FOV TSI images. While the latest instrumentation at the ARM sites may address these challenging relationships in the future, we believe that the historical ARM data at the SGP site has not yet been fully utilized. Overall, our data product can be used by researchers working on a wide range of climate-related projects. These projects may include (i) a comprehensive evaluation of outputs from the Large-Eddy Simulation (LES) and single-column models for their future improvement, (ii) the representativeness of “short-period” results obtained from the previous model and observational studies and (iii) the planning of future field campaigns with focus on improved understanding of the diurnal cycle of cumulus convection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Atmospheric Radiation Measurement (ARM) User Facility 2020 Decadal Vision

The Atmospheric Radiation Measurement (ARM) user facility was established in 1989 by the U.S Department of Energy (DOE) Office of Biological and Environmental Research (BER) to provide an observational basis for studying the Earth’s climate. ARM began collecting observations in 1992 and was designated a user facility in 2003. The facility includes a network of extensively instrumented long-term fixed-location observatories and mobile facilities. The ARM facility also includes an aerial component to augment these ground-based measurements. Because of the diversity of in situ and remotely obtained observations, ARM’s data management infrastructure is designed to collect, process, and deliver data to the research community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

ARM FY2022 Radar Plan

The fundamental objective of the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) radar facility is to provide high-quality radar observations to the scientific user community with an overarching goal of improving the treatment of clouds and precipitation in climate models. ARM has a complement of 32 radars, including scanning and zenith pointing varieties (Table 1). The effort to maintain and operate these radars is significant and requires a large, distributed staffing commitment. However, at available staffing levels, ARM’s experience dictates that it is not feasible to ensure the continuous, high-level operations expected by the ARM user community for all radars at all locations at all times. Therefore, priorities must be established to routinely manage select radar operations every fiscal year.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Analysis of Three Types of Collocated Disdrometer Measurements at the ARM Southern Great Plains Observatory

To better provide U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s disdrometer deployment strategy and support ARM precipitation-related projects, this study analyzes 18 months of drop size distribution (DSD) observations from six collocated disdrometers deployed at the ARM Southern Great Plains (SGP) site. Emphasis is placed on quantifying the uncertainties related to DSD and rainfall properties using different types of disdrometers and different pairing concepts. The instrument sampling errors are also discussed, which improves our understanding of instrument-specific impacts on rainfall measurements. The key findings are as follows: 1) All the disdrometers show consistent behaviors overall regarding accumulated precipitation, mean rainfall rate, DSD parameters, and radar reflectivity values; and 2) Strong agreement is shown between three disdrometer types in terms of their DSDs for mid-size drop range (D = 1 mm-4 mm). The two-dimensional video disdrometer performance meets expectations as the most accurate unit among all. The paired disdrometer shows a reduced statistical sampling error in terms of drop counts and other DSD properties. A concept of deploying two or more collocated disdrometers side by side is recommended for future ARM field campaigns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

ARM FY2024 Radar Plan

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s radar capability aims to provide high-quality radar observations to the scientific community, advancing the understanding of clouds and precipitation processes and ultimately improving climate models. ARM operates radars across a spectrum of frequencies with diverse scanning capacities (Table 1) to meet these scientific goals. The maintenance, operations, and management of these distributed radars at sites in various climate regimes require a substantial staffing commitment. However, the existing staffing resources are constrained, making it impractical to sustain the consistent high-quality operations that are expected by the ARM user community. In response, ARM needs to develop priorities for the radar team each year. This report lays out the forthcoming priorities for Financial Year 2024 (FY24), aiming to establish a structure for the ongoing management of radar operations and data processing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

ARM Radiosondes for SNPP/JPSS Validation Field Campaign Report

This field campaign extension has been a coordinated effort involving the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility, the University of Wisconsin at Madison, and the Joint Polar Satellite System (JPSS) project to validate NOAA Unique Combined Atmospheric Processing System (NUCAPS) temperature and moisture sounding products from the Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS) instruments on board the NOAA-20 and NOAA-21 satellite platforms. In this arrangement, funding for radiosondes and balloons was provided by the JPSS project to ARM. These radiosondes were launched coincident with NOAA-20 and NOAA-21 satellite overpasses at the ARM field sites at Eastern North Atlantic (ENA), North Slope Alaska (NSA), and Southern Great Plains (SGP). Combined with other ARM data, an assessment of the radiosonde data quality was performed and post-processing corrections applied. The dedicated radiosondes were integrated into the NOAA Products Validation System (NPROVS+), which collocated the radiosondes with satellite products (NOAA, National Aeronautics and Space Administration [NASA], European Organisation for the Exploitation of Meteorological Satellites [EUMETSAT], Geostationary Operational Environmental Satellite [GOES], Constellation Observing System for Meteorology, Ionosphere, and Climate [COSMIC]) and numerical weather prediction (NWP) forecasts for use in product assessment and algorithm development. This work is a part of the NOAA-20 and NOAA-21 satellite retrieval validation efforts and provides critical accuracy assessments of the temperature and water vapor soundings.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A Novel Segmentation Algorithm for the ARM User Facility All-Sky Imagers Using Machine Learning Applications

Cloud cover plays a pivotal role in modulating the Earth's energy budget through the reflection of incoming solar radiation and the trapping of outgoing longwave radiation. Ground-based all-sky imagers offer an objective assessment of cloud cover that can be used to estimate solar irradiance, classify cloud types, track cloud movement, and serve as a benchmark 10 for the evaluation of satellite and reanalysis data products. The Atmospheric Radiation Measurement (ARM) user facility has utilized all-sky imagers for more than 25 years to monitor cloud cover and augment its comprehensive suite of atmospheric measurements. Following the retirement of its Total Sky Imager (TSI), ARM recently deployed the TSI’s successor, the All Sky Imager (ASI-16 camera systems). To provide a smooth transition and continuity to the vast amount of knowledge gathered by the TSI over the years, while addressing typical deployment issues, we developed a novel pixel segmentation algorithm, 15 the ASI Sky Cover (ASISKYCOVER). ASISKYCOVER builds on the different strengths and properties of the TSI processing algorithm while integrating machine learning techniques, ensuring data validity and accuracy across diverse atmospheric conditions. It enhances cloud cover characterization with new features such as artifact detection and uncertainty quantification. ASISKYCOVER also includes cloud cover estimates for near-zenith (narrow field-of-view) and reduces susceptibility to false detections. This study introduces ASISKYCOVER, details its algorithm framework, and demonstrates its capabilities using a 20 year-long dataset from the ARM Southern Great Plains site. Comparisons with co-located TSI data and other ARM measurements, such as zenith-pointing radars and lidars, are presented, underscoring the ASISKYCOVER’s potential to improve cloud cover analyses and data evaluation efforts, as well as to be integrated into higher-level data products that synergize instrument suites to generate new and insightful information

Silber, Israel↗
NASA NTRS1973

Do interstellar gas clouds exist between spiral arms.

This paper discusses what can happen to clouds as they move from arm regions to interarm regions, through a density-wave shock, and back to arm regions again. Shu et al. (1972) have taken the viewpoint that interstellar clouds will survive the trip between arms. Biermann et al. (1972) have taken the viewpoint that they will not. We shall point out that cloud-cloud collisions and other processes may lead to the destruction of interstellar clouds in less time than the 100 m.y. it takes clouds to travel across arms. A possible observational test is suggested to distinguish between the two possibilities.

Quirk, W. J.↗
NASA NTRS1973

Acceptance test report (MI-74067-009-00). SVWS access arm (Serial number AA-09-03) (drawing 75M08129-13)

Acceptance tests were conducted at Kennedy Space Center of the Saturn Vehicle Workshop Spacecraft Access Arm and related equipment. The tests were conducted to prove complete system capability to operate satisfactorily under conditions required to support spacecraft operations and activities. The SVWS Access Arm, serial number AA-09-03, is a Command Module Service Arm, S/A 9, which was removed from the mobile launcher and modified to support the SVWS operations. The C/M environmental chamber was removed and a completely new chamber was installed. The retract system was redesigned to remove the automatic/remote control capability and replaced with a local manual control. The SVWS Access Arm System was successfully tested and supported spacecraft processing without major problems.

Hagood, J. T.↗
NASA NTRS1974

Galactic arm structure and gamma ray astronomy

Unexpectedly high energy gamma radiation over a broad region of the galactic plane in the general direction of the galactic center was observed. A model is proposed wherein the galactic cosmic rays are preferentially located in the high matter density regions of galactic arm segments, as a result of the weight of the matter in these arms tieing the magnetic fields and hence the cosmic rays to these regions. The presently observed galactic gamma ray longitudinal distribution can be explained with the current estimate of the average galactic matter density: if the average arm to interarm matter ratio is five to one for the major arm segments toward the galactic center from the sun; and if the cosmic ray density normalized to its local value is assumed to be directly proportional to the matter density.

Bignami, G. F.↗
NASA NTRS1974

Modal analysis and control of flexible manipulator arms

The possibility of modeling and controlling flexible manipulator arms was examined. A modal approach was used for obtaining the mathematical model and control techniques. The arm model was represented mathematically by a state space description defined in terms of joint angles and mode amplitudes obtained from truncation on the distributed systems, and included the motion of a two link two joint arm. Three basic techniques were used for controlling the system: pole allocation with gains obtained from the rigid system with interjoint feedbacks, Simon-Mitter algorithm for pole allocation, and sensitivity analysis with respect to parameter variations. An improvement in arm bandwidth was obtained. Optimization of some geometric parameters was undertaken to maximize bandwidth for various payload sizes and programmed tasks. The controlled system is examined under constant gains and using the nonlinear model for simulations following a time varying state trajectory.

Neto, O. M.↗
NASA NTRS1974

Galactic arm structure and gamma-ray astronomy

In an attempt to explain the observed unexpectedly high-energy gamma-radiation over a broad region of the galactic plane in the general direction of the galactic center, a model is proposed wherein the galactic cosmic rays are preferentially located in the high-matter-density regions of galactic arm segments, as a result of the weight of the matter in these arms tying the magnetic fields and hence the cosmic rays to these regions. The presently observed longitudinal distribution of galactic gamma-rays can be explained with the current estimate of the average galactic matter density, if the average ratio of arm to interarm matter is 5:1 for the major arm segments toward the galactic center from the sun, and if the cosmic-ray density normalized to its local value is assumed to be directly proportional to the matter density.

Bignami, G. F.↗
NASA NTRS1983

Theoretical method for calculating relative joint geometry of assembled robot arms

Equations are developed to extract the relative joint parameters of an assembled robot arm. Specifically, the Denavit-Hartenberg parameters, which completely characterize the relative joint geometry, are calculated. These parameters are needed to control the hand of the robot arm by resolved rate. As an example, the parameter extraction equations are used with perfect simulated data (no measurement noise) obtained from a mathematical model of a six-degree-of-freedom robot arm. For an actual application, measurement data needed to estimate the relative joint parameters can be generated by moving a robot arm to different positions, measuring the location of the hand (or other extension) in base coordinates, and recording the corresponding joint angles.

Barker, L. K.↗