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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Physics-Infused AI/ML Based Digital-Twin Framework for Flow-Induced-Vibration Damage Prediction in a Nuclear Reactor Heat Exchanger

This report summarizes some of the ongoing work related to the development of an expert-elicitation-digital-twin framework for real time damage state prediction in heat exchanger components of a nuclear reactor. The framework is targeted towards predicting damage associated with coupled low cycle fatigue (associated with regular heat-up, cool-down and power operation transients) and high cycle fatigue (associated with flow induced vibration transients). The overall framework will be based on a NoSQL based database, physics-infused-geometry-dependent virtual-sensor data, different AI/ML techniques-based data-driven-predictive-model applications (Apps) and real-time plant sensor measurements available through few existing sensors. Towards this overall goal, this report updates some of the ongoing work, such as on implementation of a NoSQL Database (such as MongoDB), FE based heat transfer analysis of a heat exchanger (e.g. of a PWR steam generator) for generating geometry-dependent virtual sensor data and evaluation of various AI/ML models such as based on multivariate linear regression, ensembled decision-tree based Random-Forest and Gradient-Boosting regression and high-dimensional-kernel-function-transformation based Support-Vector-Machine regression models. The AI/ML models were evaluated for predicting multi-time-series thermal states at thousands of 3D point-clouds

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2014

Characteristics of Precipitation Features and Annual Rainfall during the TRMM Era in the Central Andes

The central Andes extends from 7 deg to 21 deg S, with its eastern boundary defined by elevation (1000m and greater) and its western boundary by the coastline. The authors used a combination of surface observations, reanalysis, and the University of Utah Tropical Rainfall Measuring Mission (TRMM) precipitation features (PF) database to understand the characteristics of convective systems and associated rainfall in the central Andes during the TRMM era, 1998-2012. Compared to other dry (West Africa), mountainous (Himalayas), and dynamically linked (Amazon) regions in the tropics, the central Andes PF population was distinct from these other regions, with small and weak PFs dominating its cumulative distribution functions and annual rainfall totals. No more than 10% of PFs in the central Andes met any of the thresholds used to identify and define deep convection (minimum IR cloud-top temperatures, minimum 85-GHz brightness temperature, maximum height of the 40-dBZ echo). For most of the PFs, available moisture was limited (less than 35mm) and instability low (less than 500 J kg(exp -1)). The central Andes represents a largely stable, dry to arid environment, limiting system development and organization. Hence, primarily short-duration events (less than 60 min) characterized by shallow convection and light to light-moderate rainfall rates (0.5-4.0 mm h(exp -1)) were found.

Andes Mountains↗
DOE OSTI2019

Development of a Real-Time Mobility Control and Visualization System with Predictive Vehicle Speed Control for Connected and Automated Vehicles (CAVs)

The Oak Ridge National Laboratory is developing a cloud-based platform for the deployment and evaluation of mobility- and efficiency-oriented connected and automated vehicles (CAVs) applications that will include fundamental data management, communications and visualization elements. This system will serve as a data portal, providing indirect communications that are relevant to efficiency applications, and can be used for both real world CAVs deployments and virtual systems that allow evaluation of new CAVs applications and algorithms. Predictive vehicle speed control algorithms have been developed and will be integrated with the real-time queryable transportation database and geospatial platform to achieve optimized energy efficiency and improved traffic flow in off-freeway driving. This paper describes the technology and summarizes evaluations being conducted for a corridor in Chattanooga, Tennessee, where the technology is expected to be initially deployed. To evaluate the fuel savings resulting from the vehicle speed control and the RTMCS data framework, vehicle powertrain simulations will be completed using results from a calibrated microscopic traffic model.

Laclair, Tim↗
DOE OSTI2019

A Scalable Online Monitoring System Based on Elasticsearch for Distributed Data Acquisition in Cms

The part of the CMS Data Acquisition (DAQ) system responsible for data readout and event building is a complex network of interdependent distributed applications. To ensure successful data taking, these programs have to be constantly monitored in order to facilitate the timeliness of necessary corrections in case of any deviation from specified behaviour. A large number of diverse monitoring data samples are periodically collected from multiple sources across the network. Monitoring data are kept in memory for online operations and optionally stored on disk for post-mortem analysis. We present a generic, reusable solution based on an open source NoSQL database, Elasticsearch, which is fully compatible and non-intrusive with respect to the existing system. The motivation is to benefit from an off the-shelf software to facilitate the development, maintenance and support efforts. Elasticsearch provides failover and data redundancy capabilities as well as a programming language independent JSON-over-HTTP interface. The possibility of horizontal scaling matches the requirements of a DAQ monitoring system. The data load from all sources is balanced by redistribution over an Elasticsearch cluster that can be hosted on a computer cloud. In order to achieve the necessary robustness and to validate the scalability of the approach the above monitoring solution currently runs in parallel with an existing in-house developed DAQ monitoring system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

C-SAPR2 Convective Cell Tracking Database during CACTI

The convective cell tracking database V1 using the ARM CSAPR2 radar data set at the Sierras de Córdoba (COR) AMF site in Argentina consists of individually tracked convective cells during the entire CACTI field campaign from 15 October 2018 to 3 March 2019. The cell tracking database is produced by applying an updated version of the FLEXible object TRaKeR (FLEXTRKR) (Feng et al. 2022) to the Cartesian gridded CSAPR2 PPI data set produced by the Taranis radar processing package (Hardin et al. 2020).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2002

A Multi-Wavelength Study of the Hot Component of the Interstellar Medium

The goals of this research are as follows: (1) Using the large number of lines of sight available in the ME database, identify the lines of sight with high-velocity components in interstellar lines, from neutral species through Si VI, C IV, and N V; (2) Compare the column density of the main components (i.e. low velocity components) of the interstellar lines with distance, galactic longitude and latitude, and galactic radial position. Derive statistics on the distribution of components in space (e.g. mean free path, mean column density of a component). Compare with model predictions for the column densities in the walls of old SNR bubbles and superbubbles, in evaporating cloud boundaries and in turbulent mixing layers; (3) For the lines of sight associated with multiple high velocity, high ionization components, model the shock parameters for the associated superbubble and SNR to provide more accurate energy input information for hot phase models and galactic halo models. Thus far 49 lines of sight with at least one high velocity component to the C IV lines have been identified; and (4) Obtain higher resolution data for the lines of sight with high velocity components (and a few without) to further refine these models.

Nichols, Joy↗
NASA NTRS2013

Impact of Radiatively Interactive Dust Aerosols in the NASA GEOS-5 Climate Model: Sensitivity to Dust Particle Shape and Refractive Index

We investigate the radiative effects of dust aerosols in the NASA GEOS-5 atmospheric general circulation model. GEOS-5 is improved with the inclusion of a sectional aerosol and cloud microphysics module, the Community Aerosol and Radiation Model for Atmospheres (CARMA). Into CARMA we introduce treatment of the dust and sea salt aerosol lifecycle, including sources, transport evolution, and sinks. The aerosols are radiatively coupled to GEOS-5, and we perform a series of multi-decade AMIP-style simulations in which dust optical properties (spectral refractive index and particle shape distribution) are varied. Optical properties assuming spherical dust particles are from Mie theory, while those for non-spherical shape distributions are drawn from a recently available database for tri-axial ellipsoids. The climatologies of the various simulations generally compare well to data from the MODIS, MISR, and CALIOP space-based sensors, the ground-based AERONET, and surface measurements of dust deposition and concentration. Focusing on the summertime Saharan dust cycle we show significant variability in our simulations resulting from different choices of dust optical properties. Atmospheric heating due to dust enhances surface winds over important Saharan dust sources, and we find a positive feedback where increased dust absorption leads to increased dust emissions. We further find that increased dust absorption leads to a strengthening of the summertime Hadley cell circulation, increasing dust lofting to higher altitudes and strengthening the African Easterly Jet. This leads to a longer atmospheric residence time, higher altitude, and generally more northward transport of dust in simulations with the most absorbing dust optical properties. We find that particle shape, although important for radiance simulations, is a minor effect compared to choices of refractive index, although total atmospheric forcing is enhanced by greater than 10 percent for simulations incorporating a spheroidal shape distribution versus ellipsoidal or spherical shapes.

radiative forcing↗
NASA NTRS2004

Accuracy Assessments and Validation of an Expanded UV Irradiance Database from Satellite Total Ozone Mapping Spectrometer (TOMS)

The TOMS UV irradiance database (1978 to 2000) has been expanded to include 5 new products (noon irradiance at 305, 310, 324, and 380 nm, and noon erythemal-weighted irradiance), in addition to the existing erythemal daily exposure, which permit direct Comparisons with ground-based measurements from UV spectrometers. Sensitivity studies are conducted to estimate uncertainties of the new TOMS UV irradiance data due to algorithm apriori assumptions. Comparisons with Brewer spectrometers as well as filter radiometers are used to review of the sources of known errors. Inability to distinguish between snow and cloud cover using only TOMS data results in large errors in estimating surface UV using snow climatology. A correction is suggested for the case when the regional snow albedo is known from an independent source. The summer-time positive bias between TOMS UV estimations and Brewer measurements can be seen at all wavelengths. This suggests the difference is not related to ozone absorption effects. We emphasize that uncertainty of boundary layer UV aerosol absorption properties remains a major source of error in modeling UV irradiance in clear sky conditions. Neglecting aerosol absorption by the present TOMS algorithm results in a positive summertime bias in clear-sky UV estimations over many locations. Due to high aerosol variability the bias is strongly site dependent. Data from UV-shadow-band radiometer and well-calibrated CIMEL sun-sky radiometer are used to quantify the bias at NASA/GSFC site in Greenbelt, MD. Recommendations are given to enable potential users to better account for local conditions by combining standard TOMS UV data with ancillary ground measurements.

Krotkov, N. A.↗
NASA NTRS2014

A Super-Resolution Laser Altimetry Concept

A super-resolution laser altimetry technique has been proposed to provide improved lidar altimetry from Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar data, and it is applicable to other similar atmospheric profiling lidar with low-pass filters. To achieve high altimetry resolution, the new technique relies on an empirical relationship between the peak signal ratio and the distance between land surface and the peak signal range bin center, which is directly derived from the CALIPSO lidar measurements and does not require the CALIPSO's transient response. The CALIPSO surface elevation results in Northern America retrieved by the new technique agree with the National Elevation Database high resolution elevation maps, and the comparisons suggest that the precision of the technique is much better than 1.4 m. The preliminary data product of land surface elevation retrieved by the new technique from CALIPSO lidar measurements is available to the altimetry community for evaluation.

Lu, Xiaomei↗
NASA NTRSDate not supplied

Toward The Development of Hailstorm Climatologies Derived From Reanalyses and Infared/Passive Microwave Satellite Imagers

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) and Meteosat series, provide both historical and near-real-time observations of cloud top patterns that are commonly associated with severe convection. Environmental conditions favorable for severe weather are thought to be represented well by reanalyses. Predicting exactly where convection and costly storm hazards like hail will occur using models or satellite imagery alone, however, is extremely challenging. The multivariate combination of satellite-observed cloud patterns with reanalysis environmental parameters, linked to United States Next Generation Weather Radar- (NEXRAD-) estimated Maximum Expected Size of Hail (MESH) using a deep neural network (DNN), enables estimation of potentially severe hail likelihood for any observed storm cell. These estimates are specifically designed to make hail likelihood distinctions based on satellite-indicated points of deep convection within environments favorable for storm development. We seek an approach that can be used to estimate climatological hailstorm frequency and risk throughout the historical satellite data record. This presentation demonstrates that statistical distributions of convective parameters from satellite and reanalysis show separation between non-severe/severe hailstorm classes for predictors including overshooting cloud top temperature and area characteristics, convective available potential energy, vertical wind shear, 500 hPa temperature, mid-level lapse rate, precipitable water, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN to produce a hail likelihood metric with a critical success index of 0.504 and Heidke skill score of 0.403, which is exceptional among recent analogous hail studies. Furthermore, applications of the DNN to select case studies demonstrate good qualitative agreement between hail likelihood and MESH. These hail classifications are aggregated across an 11-year GOES-12/13 image database to derive a hail frequency and severity climatology, which denotes the Central Plains, the Midwest, and northwestern Mexico as being the most hail-prone regions within the domain studied. Opportunities for training and applying DNN-based hailstorm predictions to recently developed GOES-8/10/12/13/16 and Meteosat Second Generation convective storm detection and characterization climatologies over South America and South Africa, respectively, will also be presented.

Kristopher Bedka↗
NASA NTRSDate not supplied

Retrieve Methane from IR sounder measurements Using Machine Learning-Enhanced Physical Inversion

The sensitivity of IR sounder measurements to atmospheric CH 4 is often limited due to interferences from signals of other trace gases, insufficient thermal contrast, and cloud blockage. In order to resolve the geographical and vertical distribution of atmospheric CH 4 profiles, accurate scene-dependent a priori information is critically needed to support an optimal estimation method-based physical inversion scheme. Following the principles of indexing, representation, and retrieval, a spectral fingerprinting methodology is developed to address the needs for both accuracy and computational efficiency in sounder-based CH 4 retrieval. Within this framework, a clustering method based on machine learning is first employed to stratify and identify the a priori state within the pre-constructed database, using optimized spectral radiances as predictors. The corresponding radiative kernel is then used to establish the physical inversion scheme for finding the solution. High-quality data from CH 4 data assimilation systems like the Carbon-Tracker and the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, as well as the state-of-art sounder products are used to build the training database, including radiative kernels. We will demonstrate the results retrieved from CrIS observations and the associated validation work.

Wan Wu↗
NASA NTRSDate not supplied

The effects of preconditioning on the summer sea ice thickness evolution during MOSAiC

The central role of sea ice within the Arctic climate system is clear, as is the importance of sea ice properties in the Arctic Ocean’s evolution and response to climate change. Less clear is how the history of the sea ice floe (in winter and spring) precondition the summer thickness evolution. Accurate accounting for the collective influences of processes that affect the summer sea ice thickness evolution is necessary for determining sea ice survivability and the rate of future sea ice loss. In this study, we use a satellite-derived sea ice parcel database co-located with the MOSAiC floe to explore the factors that precondition the summer sea ice thickness evolution. First. we compare the >500 collocated satellite-tracked sea ice parcels that intersected the MOSAiC drift track with observations to evaluate the satellite-derived surface energy budget and surface properties, finding agreement with correlations >0.9. Approximately 60% of the satellite tracked parcels survived the summer melt season. Secondly, we explore the spread in sea ice thickness and survivability across the satellite-tracked parcels, finding contributions from spring/summer surface albedo evolution, surface temperature, clouds, and parcel sea ice concentration. The satellite tracked sea ice parcel analysis provides insights into the spatial coherence of the factors that influence sea ice thickness evolution.

Arctic↗
NASA NTRSDate not supplied

Objectively Identifying Transverse Cirrus Bands in Tropical Cyclones using a Convolutional Neural Network

Transverse cirrus bands (TCBs) are bands of upper-level clouds that are regularly seen in mesoscale and synoptic-scale weather systems. In tropical cyclones, their appearance has been subjectively linked to intensification and the diurnal cycle, but these hypothesized relationships have not been rigorously tested due to the difficulty of objectively identifying TCBs in satellite images. This presentation describes a machine learning technique that successfully identifies TCBs in imagery from the GOES-16 Advanced Baseline Imager. The technique uses a convolutional neural network (CNN) that assigns a probability of each pixel in the image being associated with a TCB. Using the CNN, a database of TCBs from 2018 to 2022 was developed for the Atlantic basin. Statistics using this database will be presented, including the relationship between TCBs and deep-layer vertical wind shear, intensity change, and the TC diurnal cycle.

John Mark Mayhall↗
DOE OSTI2021

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.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2017

Framework for Analysis and Visualization of Simulation Data (VizAly)

As supercomputing resources increase, cosmological scientists are able to run more detailed and larger simulations generating massive amounts of data. Analyzing these simulations with an available open-source toolkit is important for collaborative Department of Energy scientific discovery across labs, universities, and other partners. Developed software as a part of this collection include: comparing data with other existing simulations, verifying and validating results with observation databases, new halo finder algorithms, and using analytical tools to get insights into the physics of the cosmological universe. The goal of this software project is to provide a set of open-source libraries, tools, and packages for large-scale cosmology that allows scientists to visualize, analyze, and compare large-scale simulation and observational data sets. Developed software will provide a variety of methods for processing, visualization, and analysis of astronomical observation and cosmological simulation data. These tools are intended for deployment on multiple scientific computing platforms, including but not limited to personal computers, cloud computing, experimental sites (telescopes) and high performance supercomputers.

Wendelberger, Joanne↗
NASA NTRS2013

Properties of Polycyclic Aromatic Hydrocarbons in the Northwest Photon Dominated Region of NGC 7023. I. PAH Size, Charge, Composition, and Structure Distribution

Polycyclic aromatic hydrocarbon (PAH) emission in the Spitzer Infrared Spectrograph spectral map of the northwest photon dominated region (PDR) in NGC 7023 was analyzed exclusively using PAH spectra from the NASA Ames PAH IR Spectroscopic Database (www.astrochem.org/pahdb). The 5-15 micron spectrum at each pixel is fitted using a non-negative-least-squares fitting approach. The fits are of good quality, allowing decomposition of the PAH emission into four subclasses: size, charge, composition, and hydrogen adjacency (structure). Maps tracing PAH subclass distributions across the region paint a coherent astrophysical picture. Once past some 20 seconds of arc from HD 200775, the emission is dominated by the more stable, large, symmetric, compact PAH cations with smaller, neutral PAHs taking over along the lines-of-sight toward the more distant molecular cloud. The boundary between the PDR and the denser cloud material shows up as a distinct discontinuity in the breakdown maps. Noteworthy is the requirement for PANH cations to fit the bulk of the 6.2 and 11.0 micron features and the indication of PAH photo-dehydrogenation and fragmentation close to HD 200775. Decomposition of the spectral maps into "principal" subclass template spectra provides additional insight into the behavior of each subclass. However, the general applicability of this computationally more efficient approach is presently undetermined. This is the first time the spectra of individual PAHs are exclusively used to fit the 5-15 micron region and analyze the spatial behavior of the aromatic infrared bands, providing fundamental, new information about astronomical PAH subpopulations including their dependence on, and response to, changes in local conditions.

northwest photon↗
NASA NTRS2020

A Flexible Parameterization for Shortwave and Longwave Optical Properties of Ice Crystals and Derived Bulk Optical Properties for Climate Models

We provide a parameterization of the extinction efficiency, single scattering albedo and asymmetry parameter of single ice crystals with any combination of particle volume, projected area, component aspect ratio and crystal distortion at any wavelength between 0.2 and 100 mm. The paramerization is an extension of the one previously published by van Diedenhoven et al. (2014, https://doi.org/10.1175/JAS-D-13-0205.1). In addition, the parameterized optical properties are integrated over size distributions yielding bulk extinction efficiencies, single scattering albedos and asymmetry parameters for large ranges of effective radii, particle component aspect ratios and crystal distortion values. The parameterization of single particle optical properties is evaluated with a reference database. The bulk optical properties are evaluated against the ice model selected for the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 products, for which accurate optical properties are available. Mean absolute errors in parameterized extinction efficiency, asymmetry parameter and single scattering albedo are shown to be 0.0272, 0.00890 and 0.00468, respectively for shortwave wavelengths, while they are 0.0641, 0.0368 and 0.0200 in the longwave. Shortwave and longwave asymmetry parameter and single scattering albedos are shown to vary strongly with particle component aspect ratio and distortion, resulting in substantial variation in shortwave fluxes, but relatively small variations in longwave cloud emissivity. The parameterization and bulk optical properties are made publicly available.

Bastiaan Van Diedenhoven↗
DOE OSTI2019

Striped Data Analysis Framework

Traditionally, High Energy data analysis is based on the model where data are stored in files and analyzed by running multiple analysis processes, each reading one or more of the data files. This process involves repeated data reduction step, which produces smaller files, which is time consuming and leads to data duplication. We propose an alternative approach to data storage and analysis, based on the Big Data technologies. The idea is to store each element of data once and only once in a distributed scalable database and analyze data by reading only "interesting" pieces of data from the database. To make this approach possible, we developed columnar Striped Data Representation Format as the basis of the framework. Traditional columnar approach allows for efficient analysis of complex data structures. While keeping all the benefits of columnar data representation, striped mechanism goes further by enabling efficient parallelization of computations and flexible dist ribution of data analysis. The framework includes scalable and elastic data storage, compute and user analysis backend components. The framework uses off-the shelf web services and data caching technologies as the compute/data co-location mechanism. Flexible architecture allows the framework run in the cloud using container technologies. The framework offers Python/Jupyter as the user analysis backend platform, but can also run in command line or batch mode. In the article, we will present the results of the FNAL-LDRD-2016-032 FNAL LDRD project, the design, implementation, features and performance characteristics of the Striped Data Analysis Framework.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗