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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Deliver Signal Phase and Timing (SPAT) for Energy Optimization of Vehicle Cohort Via Cloud-Computing and LTE Communications

Predictive Signal Phase and Timing (SPAT) message set is one fundamental building block for vehicle-to-infrastructure (V2I) applications such as Eco-Approach and Departure (EAD) at traffic signal controlled urban intersections. Among the two complementary communication methods namely short-range sidelink (PC5) and long-range cellular radio link (Uu), this paper documents the work with long-range link: the complete data chain includes connecting to the traffic signals via existing backhaul communication network, collecting the raw signal phase state data, predicting the signal state changes and delivering the SPAT data via a geofenced service to requests over HTTP protocols. An Application Programming Interface (API) library is developed to support various cellular data transmission reduction and latency improvement techniques. An emulation-based algorithm is applied to predict the traffic signal state changes to provide adequate prediction horizon (e.g., at minimum 2 minutes) for the cohort energy optimization. In fact, the same connectivity and SPAT delivery methodology has been applied to traffic signalized intersections nationwide in the United States upon public agency approvals for access to their firewalled traffic control network and signal control systems or directly to individual controllers. This methodology proves its effectiveness and potential for rapid growth of such SPAT deliveries at mass production scale without needing infrastructure hardware retrofit or excessive communication means. To support the energy optimization of light and heavy-duty vehicle cohorts of mixed automation and propulsion systems (EV, ICE and hybrid), the connection and SPAT deliveries at two sites were completed, including public roads in Washtenaw County, Michigan and closed track test sites at American Center for Mobility (ACM) in Ypsilanti, Michigan. However, only closed test track results at ACM will be presented in this paper. A neuroevolution based optimizer is developed and implemented to control the speed of a vehicle cohort with different propulsion systems and automation levels. Closed track tests showed significant energy savings of the cohort operation.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1989

The effect of spatial resolution upon texture-based cloud field classifications

The loss of cloud-classification accuracy as a function of spatial resolution is assessed by investigating the variation of textural measures as a function of spatial resolution. Landsat MSS imagery is progressively averaged to produce degraded imagery of 1/8-km, 1/4-km, 1/2-km, and 1-km spatial resolution, and textural measures are computed from the Gray Level Difference Vector (GLDV) approach described by Chen et al. (1989). It is found that the classification accuracies obtained using the 1/8-km spatial resolution data are similar to those obtained using the full-resolution (1/16 km) texture measures, indicating that there is no advantage in using even higher spatial resolution 30-m Landsat Thematic Mapper and 10-m SPOT imagery for cloud classification.

Welch, R. M.↗
NASA NTRS2015

Clouds and the Earth's Radiant Energy System (CERES) Data Products for Climate Research

NASA's Clouds and the Earth's Radiant Energy System (CERES) project integrates CERES, Moderate Resolution Imaging Spectroradiometer (MODIS), and geostationary satellite observations to provide top-of-atmosphere (TOA) irradiances derived from broadband radiance observations by CERES instruments. It also uses snow cover and sea ice extent retrieved from microwave instruments as well as thermodynamic variables from reanalysis. In addition, these variables are used for surface and atmospheric irradiance computations. The CERES project provides TOA, surface, and atmospheric irradiances in various spatial and temporal resolutions. These data sets are for climate research and evaluation of climate models. Long-term observations are required to understand how the Earth system responds to radiative forcing. A simple model is used to estimate the time to detect trends in TOA reflected shortwave and emitted longwave irradiances.

Kato, Seiji↗
NASA NTRS2004

Analysis of TRMM Microphysical Measurements: Tropical Rainfall Measuring Mission (TRMM)

SPEC Incorporated participated in three of the four TRMM field campaigns (TEFLUN-A, TEFLUN-B and KWAJEX), installing and operating a cloud particle imager (CPI) and a high volume precipitation spectrometer (HVPS) on the SPEC Learjet in TEFLUN-A, the University of North Dakota Citation in TEFLUN-B and KWAJEX, and a CPI on the NASA DC-8 in KWAJEX. This report presents and discusses new software tools and algorithms that were developed to analyze microphysical data collected during these field campaigns, as well as scientific interpretations of the data themselves. Software algorithms were developed to improve the analysis of microphysical measurements collected by the TRMM aircraft during the field campaigns. Particular attention was paid to developing and/or improving algorithms used to compute particle size distributions and ice water content. Software was also developed in support of production of the TRMM Common Microphysical Product (CMP) data files. CMP data files for TEFLUN-A field campaign were produced and submitted to the DAAC. Typical microphysical properties of convective and stratiform regions from TEFLUN-A and KWAJEX clouds were produced. In general, it was found that in the upper cloud region near -20 to -25 C, stratiform clouds contain very high (greater than 1 per cubic centimeter) concentrations of small ice particles, which are suspected to be a residual from homogeneous freezing and sedimentation of small drops in a convective updraft. In the upper cloud region near -20 to -25 C, convective clouds contain aggregates, which are not found lower in the cloud. Stratiform clouds contain aggregates at all levels, with the majority in the lowest levels. Convective cloud regions contain much higher LWC and drop concentrations than stratiform regions at all levels, and higher LWC in the middle and upper regions. Stratiform clouds contain higher IWC than convective clouds only at the lowest level. Irregular shaped ice particles are found in very high concentrations throughout both convective and stratiform cloud regions. A striking difference in particle shape in cirrus formed in situ, cirrus formed from maritime anvils and cirrus formed from continental anvils. Over 50% of the mass of in situ cirrus ice particles is composed of bullet rosettes, while bullet rosettes are virtually non-existent in maritime and tropical anvils. Tropical anvils are composed of mostly singular, plates, capped columns, and blocky irregular shapes, while continental anvils have a much higher percentage of aggregates, some of which are chains of small spheroidal particles that appear to result from homogeneous freezing of drops. A correlation between high electric fields in continental anvils and the formation of aggregates is hypothesized.

Source record↗
NASA NTRS1992

A technique for global monitoring of net solar irradiance at the ocean surface. I - Model

An accurate long-term (84-month) climatology of net surface solar irradiance over the global oceans from Nimbus-7 earth radiation budget (ERB) wide-field-of-view planetary-albedo data is generated via an algorithm based on radiative transfer theory. Net surface solar irradiance is computed as the difference between the top-of-atmosphere incident solar irradiance (known) and the sum of the solar irradiance reflected back to space by the earth-atmosphere system (observed) and the solar irradiance absorbed by atmospheric constituents (modeled). It is shown that the effects of clouds and clear-atmosphere constituents can be decoupled on a monthly time scale, which makes it possible to directly apply the algorithm with monthly averages of ERB planetary-albedo data. Compared theoretically with the algorithm of Gautier et al. (1980), the present algorithm yields higher solar irradiance values in clear and thin cloud conditions and lower values in thick cloud conditions.

Frouin, Robert↗
DOE OSTI2021

Performance Profile of Transformer Fine-Tuning in Multi-GPU Cloud Environments

The study presented here focuses on performance characteristics and trade-offs associated with running machine-learning tasks in multi-GPU environments on both on-site cloud computing resources and commercial cloud services (Azure). Specifically, this study examines these tradeoffs by examining the performance of training and fine-tuning of transformer-based deep-learning (DL) networks on clinical notes and data, a task of critical importance in the medical domain. To this end, we perform DL-related experiments on the widely deployed NVIDIA V100 GPUs and on the newer A100 GPUs connected via NVLink or PCIe. This study analyzes the execution time of major operations to train DL models and investigate popular options to optimize each of them. We examine and present the findings on the impacts that various operations (e.g. data loading into GPUs, training, fine-tuning), optimizations, and system configurations (single vs. multi-GPU, NVLink vs. PCIe) have on the overall training performance.

Begoli, Edmon↗
DOE OSTI2022

Adaptive stabilization of quantum circuits executed on unstable devices

Conventional computers have evolved to device components that demonstrate failure rates of 10 −17 or less, while current quantum computing devices typically exhibit error rates of 10 −2 or greater. This raises concerns about the reliability and reproducibility of the results obtained from quantum computers. The problem is highlighted by experimental observation that today’s NISQ devices are inherently unstable. Remote quantum cloud servers typically do not provide users with an ability to calibrate the device themselves. Using inaccurate characterization data for error mitigation can have devastating impact on reproducibility. In this study, we investigate if one can infer the critical channel parameters dynamically from the noisy binary output of the executed quantum circuit and use it to improve program stability. An open question however is how well does this methodology scale. We discuss the efficacy and efficiency of our adaptive algorithm using canonical quantum circuits such as the uniform superposition circuit. Our metric of performance is the Hellinger distance between the post-stabilization observations and the reference (ideal) distribution.

Dasgupta, Samudra↗
NASA NTRS1978

Rain scavenging of solid rocket exhaust clouds

An explicit model for cloud microphysics was developed for application to the problem of co-condensation/vaporization of HCl and H2O in the presence of Al2O3 particulate nuclei. Validity of the explicit model relative to the implicit model, which has been customarily applied to atmospheric cloud studies, was demonstrated by parallel computations of H2O condensation upon (NH4)2 SO4 nuclei. A mesoscale predictive model designed to account for the impact of wet processes on atmospheric dynamics is also under development. Input data specifying the equilibrium state of HC1 and H2O vapors in contact with aqueous HC1 solutions were found to be limited, particularly in respect to temperature range.

Dingle, A. N.↗
NASA NTRS1985

Spectroradiometric calibration of the Thematic Mapper and Multispectral Scanner system

The results of analyses of Thematic Mapper (TM) images acquired on July 8 and October 28, 1984, and of a check of the calibration of the 1.22-m integrating sphere at Santa Barbara Research Center (SBRC) are described. The results obtained from the in-flight calibration attempts disagree with the pre-flight calibrations for bands 2 and 4. Considerable effort was expended in an attempt to explain the disagreement. The difficult point to explain is that the difference between the radiances predicted by the radiative transfer code (the code radiances) and the radiances predicted by the preflight calibration (the pre-flight radiances) fluctuate with spectral band. Because the spectral quantities measured at White Sands show little change with spectral band, these fluctuations are not anticipated. Analyses of other targets at White Sands such as clouds, cloud shadows, and water surfaces tend to support the pre-flight and internal calibrator calibrations. The source of the disagreement has not been identified. It could be due to: (1) a computational error in the data reduction; (2) an incorrect assumption in the input to the radiative transfer code; or (3) incorrect operation of the field equipment.

Slater, P. N.↗
NASA NTRS1994

Design of orbital debris shields for oblique hypervelocity impact

A new impact debris propagation code was written to link CTH simulations of space debris shield perforation to the Lagrangian finite element code DYNA3D, for space structure wall impact simulations. This software (DC3D) simulates debris cloud evolution using a nonlinear elastic-plastic deformable particle dynamics model, and renders computationally tractable the supercomputer simulation of oblique impacts on Whipple shield protected structures. Comparison of three dimensional, oblique impact simulations with experimental data shows good agreement over a range of velocities of interest in the design of orbital debris shielding. Source code developed during this research is provided on the enclosed floppy disk. An abstract based on the work described was submitted to the 1994 Hypervelocity Impact Symposium.

Fahrenthold, Eric P.↗
NASA NTRS2001

A New "Cloud Mode" for the AERONET

A key to predicting climate change is to observe an understand the global distribution of clouds and their physical properties such as optical thickness and droplet size. Since clouds change rapidly over short time and space intervals, they are difficult to simulate in computer models. But it is essential that global climate models predict realistic spatial and temporal distribution of cloud optical depth. The best way to verify these distributions is to infer optical depth from global coverage satellite data. However, satellite methods have many sources of uncertainty; thus, independent and reliable ground-based estimates are essential for validation. For aerosol, there is the AERONET - a ground based monitoring network that consists of identical multi-channel radiometers for assessing aerosol optical properties and validating their satellite retrievals. In addition to AEROSOL, we want the network monitoring CLOUD optical properties. It will use AERONET "time" (inappropriate for aerosol studies) to make basic new measurements related to cloud physics. In the presentation we will report on a new technique that retrieves cloud optical thickness for even broken clouds above green vegetation from surface measurements of zenith radiance in the visible (VIS) and near-IR (NIR) spectral regions. The idea of the method is simple: since green vegetation reflects 40-50% of incoming radiation in the NIR and only 5-10% in the VIS region, ground measurements under thin clouds have little spectral contrast between VIS and NIR, while thick clouds reflect much more of the surface-reflected radiation in the NIR than in VIS. Based on this idea, we use a combination of measurements (spectral indices) in VIS and NIR to estimate cloud optical thickness. By analogy with NDVI, the simplest index that can be defined is the Normalized Difference Cloud Index (NDCI) which is a ratio between the difference and the sum of two radiances measured for two narrow spectral bands in VIS and NIR.

Marshak, Alexander↗
NASA NTRS2002

Cloud Microphysics and Absorption Validation

Vertical distributions of particle size and habit were developed from in-situ data collected from three midlatitude cirrus field campaigns (FIRE-1, FIRE-2, and ARM IOP). These new midlatitude microphysical models were used to develop new cirrus scattering models at a number of wavelengths appropriate for use with the MODIS imager (Nasiri et al. 2002). This was the first successful collaborative effort between all the investigators on this proposal. Recent efforts have extended the midlatitude cirrus cloud analyses to tropical cirrus, using in-situ data collected during the Tropical Rainfall Measurement Mission (TRMM) Kwajalein field campaign in 1999. We note that there are critical aspects to the work: a) Improvement in computing the scattering and radiative properties of ice crystals; b) Requirement for copious amounts of cirrus in-situ data, presented in terms of both particle size and habit distributions; c) Development of cirrus microphysical and optical models for various satellite, aircraft, and ground-based instruments based on the theoretical calculations and in-situ measurements; d) Application to satellite data.

Ackerman, Steven↗
NASA NTRS2010

Satellite Gravimetry Applied to Drought Monitoring

Near-surface wetness conditions change rapidly with the weather, which limits their usefulness as drought indicators. Deeper stores of water, including root-zone soil wetness and groundwater, portend longer-term weather trends and climate variations, thus they are well suited for quantifying droughts. However, the existing in situ networks for monitoring these variables suffer from significant discontinuities (short records and spatial undersampling), as well as the inherent human and mechanical errors associated with the soil moisture and groundwater observation. Remote sensing is a promising alternative, but standard remote sensors, which measure various wavelengths of light emitted or reflected from Earth's surface and atmosphere, can only directly detect wetness conditions within the first few centimeters of the land s surface. Such sensors include the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) C-band passive microwave measurement system on the National Aeronautic and Space Administration's (NASA) Aqua satellite, and the combined active and passive L-band microwave system currently under development for NASA's planned Soil Moisture Active Passive (SMAP) satellite mission. These instruments are sensitive to water as deep as the top 2 cm and 5 cm of the soil column, respectively, with the specific depth depending on vegetation cover. Thermal infrared (TIR) imaging has been used to infer water stored in the full root zone, with limitations: auxiliary information including soil grain size is required, the TIR temperature versus soil water content curve becomes flat as wetness increases, and dense vegetation and cloud cover impede measurement. Numerical models of land surface hydrology are another potential solution, but the quality of output from such models is limited by errors in the input data and tradeoffs between model realism and computational efficiency. This chapter is divided into eight sections, the next of which describes the theory behind satellite gravimetry. Following that is a summary of the GRACE mission and how hydrological information is gleaned from its gravity products. The fourth section provides examples of hydrological science enabled by GRACE. The fifth and sixth sections list the challenging aspects of GRACE derived hydrology data and how they are being overcome, including the use of data assimilation. The seventh section describes recent progress in applying GRACE for drought monitoring, including the development of new soil moisture and drought indicator products, and that is followed by a discussion of future prospects in satellite gravimetry based drought monitoring.

Rodell, Matthew↗
NASA NTRS2017

Development of Multi-Sensor Global Cloud and Radiance Composites for Earth Radiation Budget Monitoring from DSCOVR

The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). Radiance observations and cloud property retrievals from low earth orbit and geostationary satellite imagers have to be co-located with EPIC pixels to provide scene identification in order to select anisotropic directional models needed to calculate shortwave and longwave fluxes. A new algorithm is proposed for optimal merging of selected radiances and cloud properties derived from multiple satellite imagers to obtain seamless global hourly composites at 5-kilometer resolution. An aggregated rating is employed to incorporate several factors and to select the best observation at the time nearest to the EPIC measurement. Spatial accuracy is improved using inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling. The composite data are subsequently remapped into EPIC-view domain by convolving composite pixels with the EPIC point spread function (PSF) defined with a half-pixel accuracy. PSF-weighted average radiances and cloud properties are computed separately for each cloud phase. The algorithm has demonstrated contiguous global coverage for any requested time of day with a temporal lag of under 2 hours in over 95 percent of the globe.

Khlopenkov, Konstantin↗
NASA NTRS2023

EVA Task and 3D Pose Recognition from Video

Extravehicular Activity (EVA) has been known to involve potential risks of biomechanical stresses and injuries to crewmembers. Gathering of EVA motion patterns is necessary for risk analysis and mitigation. However, many existing techniques, such as motion capture systems, are not only cost-prohibitive but are impractical for retrospective analysis of past missions. In this work, a software tool was developed, which can estimate the 3D poses of a spacesuit from photographs or videos, without using special sensors or equipment. The tool is based on the state-of-the-art artificial intelligence and machine learning (AI/ML) system, which was trained by studying and capturing motion patterns of past and current spacesuit test data. The AI/ML tool was further enhanced using synthetically generated data, in which the suit postures, backgrounds, camera angles and illumination conditions were parametrically adjusted and rendered for training. The tool, incorporated the methodologies of Convolutional Neural Network (CNN), was trained, and tested in the cloud computing environment. The trained model was then applied on new imagery and video to extract estimated joint positions and suit outlines. The joint positions were further processed to capture activity (“digging”), pose labels (“bending”), and other useful downstream information. The model performance on new imagery and video was successfully assessed for accuracy and reliability. This AI/ML based posture recognition tool thus allows for the quantification of injury risk and task performance characterization for both current and past missions and training, which can immensely help to improve EVA task and suit design.

Kyung Han Kim↗
DOE OSTI2020

Progress on the National Solar Radiation Data Base (NSRDB): A new DNI computation

This study introduces a new technique to compute direct normal irradiance (DNI) for improving the National Solar Radiation Data Base (NSRDB). A finite-surface integration algorithm is developed to compute solar radiation in differential solid angles and efficiently infer its contribution to a surface perpendicular to the solar direction. A lookup table of cloud bi-directional transmittance distribution function (BTDF) is developed by use of the discrete ordinates radiative transfer (DISORT) model for possible solar and observing directions and various cloud optical and microphysical properties. In each solar incident direction, DNI is given by the cloud BTDFs from approximately 200 differential solid angles. The simulated DNI is calibrated and evaluated using surface observations at the National Renewable Energy Laboratory's (NREL's) Solar Energy Research Laboratory (SRRL) and the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) facility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Robust implementation of generative modeling with parametrized quantum circuits

Although the performance of hybrid quantum-classical algorithms is highly dependent on the selection of the classical optimizer and the circuit ansätze, a robust and thorough assessment on-hardware of such features has been missing to date. From the optimizer perspective, the primary challenge lies in the solver’s stochastic nature, and their significant variance over the random initialization. Therefore, a robust comparison requires one to perform several training curves for each solver before one can reach conclusions about their typical performance. Since each of the training curves requires the execution of thousands of quantum circuits in the quantum computer, such a robust study remained a steep challenge for most hybrid platforms available today. In this work, we leverage on Rigetti’s Quantum Cloud Services (QCS™) to overcome this implementation barrier, and we study the on-hardware performance of the data-driven quantum circuit learning (DDQCL) for three different state-of-the-art classical solvers, and on two-different circuit ansätze associated to different entangling connectivity graphs for the same task. Additionally, we assess the gains in performance from varying circuit depths. To evaluate the typical performance associated with each of these settings in this benchmark study, we use at least five independent runs of DDQCL towards the generation of quantum generative models capable of capturing the patterns of the canonical Bars and Stripes dataset. In this experimental benchmarking, the gradient-free optimization algorithms show an outstanding performance compared to the gradient-based solver. In particular, one of them had better performance when handling the unavoidable noisy objective function to be minimized under experimental conditions.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1975

The LANDSAT-1 multispectral scanner as a tool in the classification of inland lakes

Relationships between LANDSAT-1 multispectral scanner (MSS) data and the trophic status of a group of lakes in the north-northeastern part of the United States were studied by predicting the magnitudes of two trophic state indicators, estimating lake position on a multivariate trophic scale, and automatically classifying lakes according to their trophic state. Initially, the principal component ordination was employed with 100 lakes. MSS data for some 20 lakes was then extracted from computer-compatible tapes (CCT) using a binary marking technique. The output was in the form of descriptive statistics and photographic concatenations. Color ratios were incorporated into regression models for the prediction of Secchi disc transparency, chlorophyll a, and lake position on the tropic scale. Results indicate that the LANDSAT-1 system, although handicapped by low spectral and spatial resolutions as well as excessive cloud cover, can be used as a supplemental data source in lake survey programs.

Boland, D. H. P.↗