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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

An assessment of factors which determine the net heating rate of the middle atmosphere

The effects changes in stratospheric ozone concentrations have on photochemical models of the atmosphere are evaluated. The study was spurred by the appearance of excessive stratosphere heating above 40 km in radiative transfer computations. A 20 percent reduction in ozone in the 40-55 m altitude interval would offset the anomalous heating values. Comparisons were made between calculated heating rates and in-situ data that included ozone concentrations. Heating rates decreased 0.4 C/day when clouds were present at the 300 mb level, and increased by 0.3 C/day with clouds at the 80 mb level.

Rosenfield, J. E.↗
NASA NTRS2012

Low-Latitude Ethane Rain on Titan

Cassini ISS observed multiple widespread changes in surface brightness in Titan's equatorial regions over the past three years. These brightness variations are attributed to rainfall from cloud systems that appear to form seasonally. Determining the composition of this rainfall is an important step in understanding the "methanological" cycle on Titan. I use data from Cassini VIMS to complete a spectroscopic investigation of multiple rain-wetted areas. I compute "before-and-after" spectral ratios of any areas that show either deposition or evaporation of rain. By comparing these spectral ratios to a model of liquid ethane, I find that the rain is most likely composed of liquid ethane. The spectrum of liquid ethane contains multiple absorption features that fall within the 2-micron and 5-micron spectral windows in Titan's atmosphere. I show that these features are visible in the spectra taken of Titan's surface and that they are characteristically different than those in the spectrum of liquid methane. Furthermore, just as ISS saw the surface brightness reverting to its original state after a period of time, I show that VIMS observations of later flybys show the surface composition in different stages of returning to its initial form.

Cassini↗
DOE OSTI2017

EDGE COMPUTING AND CONTEXTUAL INFORMATION FOR THE INTERNET OF THINGS SENSORS

Interpreting sensor data require knowledge about sensor placement and the surrounding environment. For a single sensor measurement, it is easy to document the context by visual observation, however for millions of sensors reporting data back to a server, the contextual information needs to be automatically extracted from either data analysis or leveraging complimentary data sources. Data layers that overlap spatially or temporally with sensor locations, can be used to extract the context and to validate the measurement. To minimize the amount of data transmitted through the internet, while preserving signal information content, two methods are explored; computation at the edge and compressed sensing. We validate the above methods on wind and chemical sensor data (1) eliminate redundant measurement from wind sensors and (2) extract peak value of a chemical sensor measuring a methane plume. We present a general cloud based framework to validate sensor data based on statistical and physical modeling and contextual data extracted from geospatial data.

03 NATURAL GAS↗
DOE OSTI2017

EDGE COMPUTING AND CONTEXTUAL INFORMATION FOR THE INTERNET OF THINGS SENSORS

Interpreting sensor data require knowledge about sensor placement and the surrounding environment. For a single sensor measurement, it is easy to document the context by visual observation, however for millions of sensors reporting data back to a server, the contextual information needs to be automatically extracted from either data analysis or leveraging complimentary data sources. Data layers that overlap spatially or temporally with sensor locations, can be used to extract the context and to validate the measurement. To minimize the amount of data transmitted through the internet, while preserving signal information content, two methods are explored; computation at the edge and compressed sensing. We validate the above methods on wind and chemical sensor data (1) eliminate redundant measurement from wind sensors and (2) extract peak value of a chemical sensor measuring a methane plume. We present a general cloud based framework to validate sensor data based on statistical and physical modeling and contextual data extracted from geospatial data.

Klein, Levente↗
DOE OSTI2023

Advances in solar forecasting: Computer vision with deep learning

Renewable energy forecasting is crucial for integrating variable energy sources into the grid. It allows power systems to address the intermittency of the energy supply at different spatiotemporal scales. To anticipate the future impact of cloud displacements on the energy generated by solar facilities, conventional modeling methods rely on numerical weather prediction or physical models, which have difficulties in assimilating cloud information and learning systematic biases. Augmenting computer vision with machine learning overcomes some of these limitations by fusing real-time cloud cover observations with surface measurements acquired from multiple sources. This Review summarizes recent progress in solar forecasting from multisensor Earth observations with a focus on deep learning, which provides the necessary theoretical framework to develop architectures capable of extracting relevant information from data generated by ground-level sky cameras, satellites, weather stations, and sensor networks. Overall, machine learning has the potential to significantly improve the accuracy and robustness of solar energy meteorology; however, more research is necessary to realize this potential and address its limitations.

14 SOLAR ENERGY↗
DOE OSTI2023

Software Bill of Materials (SBOM) Sharing Lifecycle Report

As Software Bill of Materials (SBOM) adoption efforts mature, SBOM sharing continues to occur, but no single solution or set of solutions have become ubiquitous. The purpose of this report is to enumerate and describe the different parties and phases of the SBOM sharing lifecycle and assist readers in choosing suitable SBOM sharing solutions based on the amount of time, resources, subject-matter expertise, effort, and access to tooling that is available to the reader to implement a phase of the SBOM sharing lifecycle. The SBOM sharing lifecycle consists of the Discovery, Access, and Transport of an SBOM and this report details these individual phases and how an SBOM goes from author to the consumer. This report also details how potential enrichment activities may be performed on an SBOM to create a new product before or after it has been shared. The concept of a sophistication classification for SBOM sharing solutions is concurrently introduced with a focus on the inclusion or lack of certain features and effort associated with their implementation. Examples of low, medium, and high-sophistication solutions are provided; however, these examples and associated categorizations should not be seen as a qualitative judgment meant to push the reader towards a particular adoption strategy since sharing solutions are chosen based on the unique needs of the user. This report does recommend the SBOM community consider how to make current and future sharing solutions interoperable with each other as well as more automated methods to facilitate sharing and broader SBOM adoption. This report also highlights an SBOM sharing survey results obtained from interviews with stakeholders to understand the current SBOM sharing landscape. The categorized results of the survey suggest that SBOMs are currently transported directly to the receiver through email or similar informal communication mechanisms or alternatively the SBOM resides on a repository available to consumers. In addition to these transport methods, this report captures industry efforts to create private sharing solutions and services that can store and transport enrichment data and may use higher sophistication features that are cloud-based or using distributed ledger technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Proper Motions, Orbits, and Tidal Influences of Milky Way Dwarf Spheroidal Galaxies

We combine Gaia early data release 3 astrometry with accurate photometry and utilize a probabilistic mixture model to measure the systemic proper motion of 52 dwarf spheroidal (dSph) satellite galaxies of the Milky Way (MW). For the 46 dSphs with literature line-of-sight velocities we compute orbits in both a MW and a combined MW + Large Magellanic Cloud (LMC) potential and identify Car II, Car III, Hor I, Hyi I, Phx II, and Ret II as likely LMC satellites. 40% of our dSph sample has a >25% change in pericenter and/or apocenter with the MW + LMC potential. For these orbits, we use a Monte Carlo sample for the observational uncertainties for each dSph and the uncertainties in the MW and LMC potentials. We predict that Ant II, Boo III, Cra II, Gru II, and Tuc III should be tidally disrupting by comparing each dSph's average density relative to the MW density at its pericenter. dSphs with large ellipticity (CVn I, Her, Tuc V, UMa I, UMa II, UMi, Wil 1) show a preference for their orbital direction to align with their major axis even for dSphs with large pericenters. We compare the dSph radial orbital phase to subhalos in MW-like N-body simulations and infer that there is not an excess of satellites near their pericenter. With projections of future Gaia data releases, we find that dSph's orbital precision will be limited by uncertainties in the distance and/or MW potential rather than in proper motion precision. Finally, we provide our membership catalogs to enable community follow-up.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRSDate not supplied

TPSAS-NF1676L-10726-DND

The principal component radiative transfer model PCRTM (Xu 2006) simulates spectral longwave radiance at a 0.5cm-1 resolution over the wavelengths (50-2760cm-1) using 280 principal components. The CERES product SSF containing CERES field-of-view (FOV) cloud properties based on MODIS pixel information are used as inputs. NASA GMAO(Global Modeling and Assimilation Office)Geos-4 temperature and humidity profiles, as well as SMOBA (Stratosphere Monitoring Ozone Blended Analysis) ozone profiles allow computations for five years (Jan 2001- Dec 2006) of FOV nadir spectral radiance. The approximately twice daily FOV PCRTM simulations along with CERES broadband measurements are time-space averaged to 10deg zonal monthly data. Analysis of monthly seasonal and deseasonalized interannual variability in terms of brightness temperature are presented. Comparison of interannual variability in the portions of the spectrum seen by the AIRS instrument are shown. Comparisons of spectral integrated PCRTM simulations to CERES broadband longwave radiance measurements are made. In order to study the sensitivity of the spatially and temporally averaged TOA spectrum to various atmospheric properties, we perturbed atmospheric and surface temperatures, humidity, cloud height, cloud fraction, and particle size. The sensitivity of the spectrum to these properties depends on region and seasons. Month to month variability of the longwave spectra to these perturbations is 10 to 20% of the global monthly mean perturbation response. In addition, it depends on time period and size of the area used for averaging spectra. In this poster, the magnitude of sensitivity variability and how the sensitivity changes depending on temporal and spatial scale of averaging will be discussed.

Fred G. Rose↗
NASA NTRSDate not supplied

NASA Briefing to Unidata

The NASA report to Unidata for October, 2021 covers several topics of mutual interest to Unidata. These include plans for hosting NASA data in the cloud, data analysis-in-place, and NASA plans for Open Source Science. Major collaborations with other organizations are also discussed.

NetCDF↗
DOE OSTI2021

Improving the prediction of DNI with physics-based representation of all-sky circumsolar radiation

Direct normal irradiance (DNI) is often interpreted differently in ground measurements and forecasts using numerical weather prediction (NWP) models, leading to substantial bias in DNI forecasting especially under cloudy-sky conditions. To mitigate the bias, we use the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI) to conduct physics-based simulations of solar radiation in the circumsolar region. The DNI is quantified using the sum of the radiation along the sun direction and the scattered radiation within the circumsolar region. FARMS-DNI is coupled with the Weather Research and Forecasting model with solar extensions (WRF-Solar) to predict day-ahead DNI in 9-km pixels over an extended domain covering the contiguous United States. The DNI forecasts for the entire year of 2018 are validated using surface-based observations at the Atmospheric Radiation Measurement (ARM) and Surface Radiation Budget Network (SURFRAD) sites and a satellite-based solar radiation product developed by the procedure of the National Solar Radiation Data Base (NSRDB). The results show that the WRF-Solar coupled with the conventional Beer-Bouguer-Lambert law underestimes the overall DNI forecasts by 30%-60%, which is a joint effect of the uncertainties in computing the circumsolar radiation and forecasting cloud properties. This bias is significantly reduced by using FARMS-DNI.

14 SOLAR ENERGY↗
DOE OSTI2024

OpenCHAMI Developer Summit [Slides]

The mission of the OpenCHAMI consortium is to steward the collaborative development and continuous evolution of cloud-like software to manage High Performance Computing capacity regardless of the size or deployment platform. We are guided by the operators and practitioners who use modern tooling and concepts to address the needs of classical HPC applications and the growing AI/ML and Data Science community that wish to leverage HPC capacity within their own workflows, to meet their needs with their own tools.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Savanna burning and convective mixing in southern Africa - Implications for CO emissions and transport

Atmospheric data from both remote and direct measurements were used to compute the carbon monoxide (CO) emissions and mass transport from Africa south of the equator. The Measurement of Air Pollution from Satellites (MAPS) experiment flew on the Space Shuttle during October 5-13, 1984; the highest CO mixing ratios from the entire mission were measured over southern Africa. In addition to the MAPS data, surface CO mixing ratios were measured in the boundary layer at Cape Point, South Africa. A calibration factor for the remote MAPS CO measurements was determined by computing the ratio of the surface measurements from Cape Point during unpolluted periods to coincident MAPS measurements. The adjusted MAPS CO data were then used to compute the mass flux of CO from biomass burning in southern Africa during the MAPS mission. The transport calculations were compared to the estimated CO emissions from biomass burning to examine the efficiency in which the continental tropical clouds redistribute CO in the troposphere.

Connors, Vickie S.↗
DOE OSTI2025

A Typology of Quantum-Classical Faults

This paper introduces an extended taxonomy of faults specific to hybrid quantum-classical systems, addressing the unique challenges that arise from integrating quantum accelerators into high-performance computing (HPC) infrastructures. Building on the foundational fault classification by Avizienis et al., we incorporate fault types unique to quantum computing-such as qubit decoherence, spontaneous gate errors, and photon loss-alongside traditional and human-induced faults including development errors, operational mistakes, and malicious attacks. Our taxonomy classifies faults by their origin (natural vs. human-made), intent (accidental, deliberate non-malicious, or malicious), system boundaries (internal vs. external), and persistence (transient to permanent). We also explore how different architectural integration patterns-ranging from tight coupling to loose on-premise and cloud-based configurations-shape the manifestation and propagation of faults. These scenarios are analyzed in terms of timing mismatches, interface inconsistencies, and security threats such as data tampering and denial-of-service attacks. Through this fault-centric lens, we aim to support the co-design of dependable quantum-classical systems and highlight the critical role that integration strategies play in ensuring reproducibility, resilience, and security across hybrid computing platforms.

Giusto, Edorado [University of Naples Federico II,↗
DOE OSTI2023

Cloud condensation nuclei activity of internally mixed particle populations at a remote marine free troposphere site in the North Atlantic Ocean

Here, this study reports results from research conducted at the Observatory of Mount Pico (OMP), 2225 m above mean sea level on Pico Island in the Azores archipelago in June and July 2017. We investigated the chemical composition, mixing state, and cloud condensation nuclei (CCN) activities of long-range transported free tropospheric (FT) particles. FLEXible PARTicle Lagrangian particle dispersion model (FLEXPART) simulations reveal that most air masses that arrived at the OMP during the sampling period originated in North America and were highly aged (average plume age > 10 days). We probed size-resolved chemical composition, mixing state, and hygroscopicity parameter ($\mathcal{κ}$) of individual particles using computer-controlled scanning electron microscopy with an energy-dispersive X-ray spectrometer (CCSEM-EDX). Based on the estimated individual particle mass from elemental composition, we calculated the mixing state index, $χ$. During our study, FT particle populations were internally mixed ($χ$ of samples are between 53% and 87%), owing to the long atmospheric aging time. We used data from a miniature Cloud Condensation Nucleus Counter (miniCCNC) to derive the hygroscopicity parameter, $\mathcal{κ}$ CCNC . Combining $\mathcal{κ}$ CCNC and FLEXPART, we found that air masses recirculated above the North Atlantic Ocean with lower mean altitude had higher $\mathcal{κ}$ CCNC due to the higher contribution of sea salt particles. We used CCSEM-EDX and phase state measurements to predict single-particle $\mathcal{κ}$ ($\mathcal{κ}$ CCSEM-EDX ) values, which overlap with the lower range of $\mathcal{κ}$ CCNC measured below 0.15 % SS. Therefore, CCSEM-EDX measurements can be useful in predicting the lower bound of $\mathcal{κ}$, which can be used in climate models to predict CCN activities, especially in remote locations where online CCN measurements are unavailable.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗
DOE OSTI2021

Watermarks in stream processing systems: semantics and comparative analysis of Apache Flink and Google cloud dataflow

Streaming data processing is an exercise in taming disorder: from oftentimes huge torrents of information, we hope to extract powerful and timely analyses. But when dealing with streaming data, the unbounded and temporally disordered nature of real-world streams introduces a critical challenge: how does one reason about the completeness of a stream that never ends? In this paper, we present a comprehensive definition and analysis of watermarks, a key tool for reasoning about temporal completeness in infinite streams.First, we describe what watermarks are and why they are important, highlighting how they address a suite of stream processing needs that are poorly served by eventually-consistent approaches:• Computing a single correct answer, as in notifications.• Reasoning about a lack of data, as in dip detection.• Performing non-incremental processing over temporal subsets of an infinite stream, as in statistical anomaly detection with cubic spline models.• Safely and punctually garbage collecting obsolete inputs and intermediate state.• Surfacing a reliable signal of overall pipeline health.Second, we describe, evaluate, and compare the semantically equivalent, but starkly different, watermark implementations in two modern stream processing engines: Apache Flink and Google Cloud Dataflow.

Akidau, Tyler↗
NASA NTRS2012

Processing NASA Earth Science Data on Nebula Cloud

Three applications were successfully migrated to Nebula, including S4PM, AIRS L1/L2 algorithms, and Giovanni MAPSS. Nebula has some advantages compared with local machines (e.g. performance, cost, scalability, bundling, etc.). Nebula still faces some challenges (e.g. stability, object storage, networking, etc.). Migrating applications to Nebula is feasible but time consuming. Lessons learned from our Nebula experience will benefit future Cloud Computing efforts at GES DISC.

Chen, Aijun↗
DOE OSTI2018

Internet-of-Things Hardware-in-the-Loop Simulation Architecture for Providing Frequency Regulation With Demand Response

Following recent advances in network infrastructure, cloud computing, and embedded systems, fascinating work is underway exploring the utility of demand response in increasing grid stability while permitting high penetration of intermittent renewable distributed generation resources. Although works have demonstrated diverse theoretical advantages of demand response programs, little real-world data are available and utilities generally remain reticent in moving forward with large-scale implementation due to risks inherent to any modification of the power system. Deciding that the next pertinent step in bringing demand response from theory to technology is developing an Internet-of-things hardware-in-the-loop simulation power system integrated device capable of empirically testing the theoretical mechanisms, this work presents an architecture testbed for providing demand response (telemetric monitoring and actuation of loads), which is real node in a power system simulation where virtual node's parameters derive real node data. Finally, we test a demand response algorithm, which provides frequency regulation services.

97 MATHEMATICS AND COMPUTING↗