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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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NASA NTRSDate not supplied

Models, In situ, and Remote sensing of Aerosols (MIRA)

The Models, In situ, and Remote sensing of Aerosols (MIRA) working group connects members of the different aerosol communities through collaborative projects. This abstract, additional details, and a list of current projects can be found at https://science.larc.nasa.gov/mira-wg/. Join our email distribution list of 200+ members at https://espo.nasa.gov/lists/listinfo/mira.In this talk, we will describe some current MIRA projects that are relevant to AeroCom (e.g., the Table of Aerosol Optics, or TAO) and the benefits of presenting your multi-disciplinary work at MIRA sessions and meetings. What is MIRA? MIRA is forum that fosters international collaborations amongst the aerosol Modeling, In situ, and Remote sensing specialties. MIRA advances scientific knowledge about aerosol properties and improves our understanding of air quality, weather, and climate through the pursuit of specific goals. MIRA emphasizes areas of study where the aerosol specialties overlap, and MIRA projects seek additional data and insight from others (observational and modeled). Why? The purpose of MIRA is to contextualize both observations and model results through the encouragement of holistic projects and collaborations. Linking aerosol disciplines removes gaps in understanding that hinder our interpretation of observations and model results. Such linkages improve our understanding of air quality, weather, and climate. How does MIRA differ from other working groups? MIRA focuses on interdisciplinarity to improve measurements and their utility, so MIRA complements the activities of other groups. Other interdisciplinary aerosol groups have different primary foci. For instance, AeroCom mainly focuses on improving global aerosol models, AeroSat focuses on strengthening collaboration amongst satellite aerosol retrieval groups, and ICAP focuses on aerosol forecasting. We hope that many MIRA projects engage these groups.

Gregory Schuster↗
NASA NTRS1997

The Multi-Center Airborne Coherent Atmospheric Wind Sensor, MACAWS

In 1992 the atmospheric lidar remote sensing groups of the NASA Marshall Space Flight Center, NOAA Environmental Technology Laboratory, and Jet Propulsion Laboratory began a joint collaboration to develop an airborne high-energy Doppler laser radar (lidar) system for atmospheric research and satellite validation and simulation studies. The result is the Multi-center Airborne Coherent Atmospheric Wind Sensor, MACAWS, which has the capability to remotely sense the distribution of wind and absolute aerosol backscatter in the troposphere and lower stratosphere. A factor critical to the programmatic feasibility and technical success of this collaboration has been the utilization of existing components and expertise which were developed for previous atmospheric research by the respective institutions. The motivation for the MACAWS program Is three-fold: to obtain fundamental measurements of sub-synoptic scale processes and features which may be used as a basis to improve sub-grid scale parameterizations in large-scale models; to obtain similar datasets in order to improve the understanding and predictive capabilities on the mesoscale; and to validate (simulate) the performance of existing (planned) satellite-borne sensors. Examples of the latter include participation in the validation of the NASA Scatterometer and the assessment of prospective satellite Doppler lidar for global tropospheric wind measurement. Initial flight tests were made in September 1995; subsequent flights were made in June 1996 following improvements. This paper describes the MACAWS instrument, principles of operation, examples of measurements over the eastern Pacific Ocean and western United States, and future applications.

Rothermel, Jeffry↗
DOE OSTI2024

Abstract for CRADA between NETL and Westinghouse Electric Company

The National Energy Technology Laboratory (NETL) and Westinghouse will collaborate in testing a new NETL invention known as the single-crystal optical fiber Raman Distributed Temperature Sensor (DTS). Westinghouse will use the NETL Raman DTS to test performance of their new e-Vinci heat-pipe reactor systems.

47 OTHER INSTRUMENTATION↗
NASA NTRS2005

Shuttle Orbiter Contingency Abort Aerodynamics: Real-Gas Effects and High Angles of Attack

An important element of the Space Shuttle Orbiter safety improvement plan is the improved understanding of its aerodynamic performance so as to minimize the "black zones" in the contingency abort trajectories [1]. These zones are regions in the launch trajectory where it is predicted that, due to vehicle limitations, the Orbiter will be unable to return to the launch site in a two or three engine-out scenario. Reduction of these zones requires accurate knowledge of the aerodynamic forces and moments to better assess the structural capability of the vehicle. An interesting aspect of the contingency abort trajectories is that the Orbiter would need to achieve angles of attack as high as 60deg. Such steep attitudes are much higher than those for a nominal flight trajectory. The Orbiter is currently flight certified only up to an angle of attack of 44deg at high Mach numbers and has never flown at angles of attack larger than this limit. Contingency abort trajectories are generated using the data in the Space Shuttle Operational Aerodynamic Data Book (OADB) [2]. The OADB, a detailed document of the aerodynamic environment of the current Orbiter, is primarily based on wind-tunnel measurements (over a wide Mach number and angle-of-attack range) extrapolated to flight conditions using available theories and correlations, and updated with flight data where available. For nominal flight conditions, i.e., angles of attack of less than 45deg, the fidelity of the OADB is excellent due to the availability of flight data. However, at the off-nominal conditions, such as would be encountered on contingency abort trajectories, the fidelity of the OADB is less certain. The primary aims of a recent collaborative effort (completed in the year 2001) between NASA and Boeing were to determine: 1) accurate distributions of pressure and shear loads on the Orbiter at select points in the contingency abort trajectory space; and 2) integrated aerodynamic forces and moments for the entire vehicle and the control surfaces (body flap, speed brake, and elevons). The latter served the useful purpose of verification of the aerodynamic characteristics that went into the generation of the abort trajectories.

Prabhu, Dinesh K.↗
NASA NTRS2011

In Situ Validation of the Soil Moisture Active Passive (SMAP) Satellite Mission

SMAP is a new NASA mission proposed for 2014 that would provide a number of soil moisture and freeze/thaw products. The soil moisture products span spatial resolutions from 3 to 40 km. In situ soil moisture observations will be one of the key elements of the validation program for SMAP. Data from the currently available set of soil moisture observing sites and networks need improvement if they are to be useful. Problems include a lack of standardization of instrumentation and installation and the disparity in spatial scale between the point-scale in situ data (a few centimeters) and the coarser satellite products. SMAP has initiated activities to resolve these issues for some of the existing resources. The other challenge to soil moisture validation is the need to expand the number of sites and their geographic distribution. SMAP is attempting to increase the number of sites and their value in validation through collaboration. The issues and solutions involving in situ validation being investigated will be described along with recent results from SMAP validation projects.

validation↗
NASA NTRS2011

Using Ensemble Decisions and Active Selection to Improve Low-Cost Labeling for Multi-View Data

This paper seeks to improve low-cost labeling in terms of training set reliability (the fraction of correctly labeled training items) and test set performance for multi-view learning methods. Co-training is a popular multiview learning method that combines high-confidence example selection with low-cost (self) labeling. However, co-training with certain base learning algorithms significantly reduces training set reliability, causing an associated drop in prediction accuracy. We propose the use of ensemble labeling to improve reliability in such cases. We also discuss and show promising results on combining low-cost ensemble labeling with active (low-confidence) example selection. We unify these example selection and labeling strategies under collaborative learning, a family of techniques for multi-view learning that we are developing for distributed, sensor-network environments.

machine learning↗
NASA NTRS2014

Aggregation Tool to Create Curated Data albums to Support Disaster Recovery and Response

Despite advances in science and technology of prediction and simulation of natural hazards, losses incurred due to natural disasters keep growing every year. Natural disasters cause more economic losses as compared to anthropogenic disasters. Economic losses due to natural hazards are estimated to be around $6-$10 billion dollars annually for the U.S. and this number keeps increasing every year. This increase has been attributed to population growth and migration to more hazard prone locations such as coasts. As this trend continues, in concert with shifts in weather patterns caused by climate change, it is anticipated that losses associated with natural disasters will keep growing substantially. One of challenges disaster response and recovery analysts face is to quickly find, access and utilize a vast variety of relevant geospatial data collected by different federal agencies such as DoD, NASA, NOAA, EPA, USGS etc. Some examples of these data sets include high spatio-temporal resolution multi/hyperspectral satellite imagery, model prediction outputs from weather models, latest radar scans, measurements from an array of sensor networks such as Integrated Ocean Observing System etc. More often analysts may be familiar with limited, but specific datasets and are often unaware of or unfamiliar with a large quantity of other useful resources. Finding airborne or satellite data useful to a natural disaster event often requires a time consuming search through web pages and data archives. Additional information related to damages, deaths, and injuries requires extensive online searches for news reports and official report summaries. An analyst must also sift through vast amounts of potentially useful digital information captured by the general public such as geo-tagged photos, videos and real time damage updates within twitter feeds. Collecting and aggregating these information fragments can provide useful information in assessing damage in real time and help direct recovery efforts. The search process for the analyst could be made much more efficient and productive if a tool could go beyond a typical search engine and provide not just links to web sites but actual links to specific data relevant to the natural disaster, parse unstructured reports for useful information nuggets, as well as gather other related reports, summaries, news stories, and images. This presentation will describe a semantic aggregation tool developed to address similar problem for Earth Science researchers. This tool provides automated curation, and creates "Data Albums" to support case studies. The generated "Data Albums" are compiled collections of information related to a specific science topic or event, containing links to relevant data files (granules) from different instruments; tools and services for visualization and analysis; information about the event contained in news reports, and images or videos to supplement research analysis. An ontology-based relevancy-ranking algorithm drives the curation of relevant data sets for a given event. This tool is now being used to generate a catalog of Hurricane Case Studies at Global Hydrology Resource Center (GHRC), one of NASA's Distribute Active Archive Centers. Another instance of the Data Albums tool is currently being created in collaboration with NASA/MSFC's SPoRT Center, which conducts research on unique NASA products and capabilities that can be transitioned to the operational community to solve forecast problems. This new instance focuses on severe weather to support SPoRT researchers in their model evaluation studies

Ramachandran, Rahul↗
DOE OSTI2025

Optimization of distributed compute resources utilization in the CMS Global Pool

The CMS Submission Infrastructure is the primary system for managing computing resources for CMS workflows, including data processing, simulation, and analysis. It integrates geographically distributed resources from Grid, HPC, and cloud providers into federated pools managed by HTCondor and Glidein- WMS, for a total of around 500k CPU cores. This system dynamically manages workloads based on priorities defined by the collaboration. Additionally, CMS scheduling strategies must be flexible to handle multiple concurrent workloads while considering changing processing demands and resource availability from various providers.Efficient utilization of vast amounts of distributed compute resources is a key element for the success of the scientific programs of the LHC experiments. Optimizing the system is essential to maximize resource efficiency and fully utilize the distributed computing power. The CMS Submission Infrastructure team thus systematically investigates sources of inefficiency in workload scheduling to reduce their impact. In addition, a strategy of pilot overloading has been introduced to compensate for other inefficiency sources, thereby optimizing resource utilization and enhancing computational throughput.

Mascheroni, Marco [UC, San Diego (main)]↗
NASA NTRS1995

Development and Application of the Collaborative Optimization Architecture in a Multidisciplinary Design Environment

Collaborative optimization is a design architecture applicable in any multidisciplinary analysis environment but specifically intended for large-scale distributed analysis applications. In this approach, a complex problem is hierarchically de- composed along disciplinary boundaries into a number of subproblems which are brought into multidisciplinary agreement by a system-level coordination process. When applied to problems in a multidisciplinary design environment, this scheme has several advantages over traditional solution strategies. These advantageous features include reducing the amount of information transferred between disciplines, the removal of large iteration-loops, allowing the use of different subspace optimizers among the various analysis groups, an analysis framework which is easily parallelized and can operate on heterogenous equipment, and a structural framework that is well-suited for conventional disciplinary organizations. In this article, the collaborative architecture is developed and its mathematical foundation is presented. An example application is also presented which highlights the potential of this method for use in large-scale design applications.

Braun, R. D.↗
NASA NTRS2012

Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events as well as faster responses, such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if learners at individual nodes can communicate with their neighbors. In previous work, methods were developed by which classification algorithms deployed at sensor nodes can communicate information about event labels to each other, building on prior work with co-training, self-training, and active learning. The idea of collaborative learning was extended to function for clustering algorithms as well, similar to ideas from penta-training and consensus clustering. However, collaboration between these learner types had not been explored. A new protocol was developed by which classifiers and clusterers can share key information about their observations and conclusions as they learn. This is an active collaboration in which learners of either type can query their neighbors for information that they then use to re-train or re-learn the concept they are studying. The protocol also supports broadcasts from the classifiers and clusterers to the rest of the network to announce new discoveries. Classifiers observe an event and assign it a label (type). Clusterers instead group observations into clusters without assigning them a label, and they collaborate in terms of pairwise constraints between two events [same-cluster (mustlink) or different-cluster (cannot-link)]. Fundamentally, these two learner types speak different languages. To bridge this gap, the new communication protocol provides four types of exchanges: hybrid queries for information, hybrid "broadcasts" of learned information, each specified for classifiers-to-clusterers, and clusterers-to-classifiers. The new capability has the potential to greatly expand the in situ analysis abilities of sensor networks. Classifiers seeking to categorize incoming data into different types of events can operate in tandem with clusterers that are sensitive to the occurrence of new kinds of events not known to the classifiers. In contrast to current approaches that treat these operations as independent components, a hybrid collaborative learning system can enable them to learn from each other.

Wagstaff, Kiri L.↗
DOE OSTI2025

ESPPU INPUT: C$^3$ within the "Linear Collider Vision"

The Linear Collider Vision calls for a Linear Collider Facility with a physics reach from a Higgs Factory to the TeV-scale with $e^+e^{-}$ collisions. One of the technologies under consideration for the accelerator is a cold-copper distributed-coupling linac capable of achieving high gradient. This technology is being pursued by the C$^3$ collaboration to understand its applicability to future colliders and broader scientific applications. In this input we share the baseline parameters for a C$^3$ Higgs-factory and the energy reach of up to 3 TeV in the 33 km tunnel foreseen under the Linear Collider Vision. Recent results, near-term plans and future R&D needs are highlighted.

43 PARTICLE ACCELERATORS↗
NASA NTRS1989

Bonner Durchmusterung (Argelander 1859-1862): Documentation for the machine-readable version

The machine-readable version of the catalog, as it is currently being distributed from the Astronomical Data Center, is described. The entire Bonner Durchmusterung (BD) was computerized through the collaborative efforts of the Centre de Donnees Astronomiques de Strasbourg, l'Observatoire de Nice, and the Astronomical Data Center at the NASA/Goddard Space Flight Center. All corrigenda published in the original BD volumes were incorporated into the machine file, along with changes published following the 1903 edition. In addition, stars indicated to be missing in published lists and verified by various techniques are flagged so that they can be omitted from computer plotted charts if desired. Stars deleted in the various errata lists were similarly flagged, while those with revised data are flagged and listed in a separate table.

Warren, Wayne H., Jr.↗
NASA NTRS1992

Gamma ray bursts from collisions of primordial small mass black holes with comets

Recent results of BATSE (a collaborative project on independent sky surveys of gamm ray bursts and optical sky patrols) reinforce the isotropic distribution of gamm-ray bursts. Alternatively to cosmological models, collisions between small mas primordial black holes and comets in the Oort cloud are proposed. Assuming typical Oort cloud densities and velocities for comets and primordial black holes, many of the observed properties of gamma-ray bursts can be explained.

Bickert, K. F.↗
NASA NTRS1995

Constructing Scientific Applications from Heterogeneous Resources

A new model for high-performance scientific applications in which such applications are implemented as heterogeneous distributed programs or, equivalently, meta-computations, is investigated. The specific focus of this grant was a collaborative effort with researchers at NASA and the University of Toledo to test and improve Schooner, a software interconnection system, and to explore the benefits of increased user interaction with existing scientific applications.

Schichting, Richard D.↗
NASA NTRS2002

Clouds and Water Vapor in the Climate System and Radiative Transfer in Clear Air and Cirrus Clouds in the Tropics

Research supported under this grant was aimed at attacking unanswered scientific questions that lie at the intersection of radiation, dynamics, chemistry, and climate. Considerable emphasis was placed on scientific collaboration and the innovative development of instruments required to address these issues. Specific questions include water vapor distribution in the tropical troposphere, atmospheric radiation, thin cirrus clouds, stratosphere-troposphere exchange, and correlative science with satellite observations.

Anderson, James G.↗
NASA NTRS2006

Magneto-thermo-elastokinetics of Geometrically Nonlinear Laminated Composite Plates. Part 1: Foundation of the Theory

A fully coupled magneto-thermo-elastokinetic model of laminated composite, finitely electroconductive plates incorporating geometrical nonlinearities and subjected to a combination of magnetic and thermal fields, as well as carrying an electrical current is developed, In this context. the first-order transversely shearable plate theory in conjunction with von-Karman geometrically nonlinear strain concept is adopted. Related to the distribution of electric and magnetic field disturbances within the plate, the assumptions proposed by Ambartsumyan and his collaborators are adopted. Based on the electromagnetic equations (i.e. the ones by Faraday, Ampere, Ohm, Maxwell and Lorentz), the modified Fourier's law of heat conduction and on the elastokinetic field equations, the 3-D coupled problem is reduced to an equivalent 2- D one. The theory developed herein provides a foundation for the investigation, both analytical and numerical, of the interacting effects among the magnetic, thermal and elastic fields in multi-layered thin plates made of anisotropic materials.

Hasanyan, Davresh↗
NASA NTRS2008

Re-Engineering the Tropical Rainfall Measuring Mission (TRMM) Satellite Utilizing Goddard Space Flight Center (GSFC) Mission Services Center (GMSEC) Middleware Based Technology to Enable Lights Out Operations and Autonomous Re-Dump of Lost Telemetry Data

The Tropical Rainfall Measuring Mission (TRMM) spacecraft was launched in November of 1996 in order to obtain unique three dimensional radar cross sectional observations of cloud structures with particular interest in hurricanes. The TRMM mission life was recently extended with current estimates that operations will continue through the 2012-2013 timeframe. Faced with this extended mission profile, the project has embarked on a technology refresh and re-engineering effort. TRMM has recently implemented a re-engineering effort to expand a middleware based messaging architecture to enable fully redundant lights-out of flight operations activities. The middleware approach is based on the Goddard Mission Services Evolution Center (GMSEC) architecture, tools and associated open-source Applications Programming Interface (API). Middleware based messaging systems are useful in spacecraft operations and automation systems because private node based knowledge (such as that within a telemetry and command system) can be broadcast on the middleware messaging bus and hence enable collaborative decisions to be made by multiple subsystems. In this fashion, private data is made public and distributed within the local area network and multiple nodes can remain synchronized with other nodes. This concept is useful in a fully redundant architecture whereby one node is monitoring the processing of the 'prime' node so that in the event of a failure the backup node can assume operations of the prime, without loss of state knowledge. This paper will review and present the experiences, architecture, approach and lessons learned of the TRMM re-engineering effort centered on the GMSEC middleware architecture and tool suite. Relevant information will be presented that relates to the dual redundant parallel nature of the Telemetry and Command (T and C) and Front-End systems and how these systems can interact over a middleware bus to achieve autonomous operations including autonomous commanding to recover missing science data during the same spacecraft contact.

Marius, Julio L.↗
NASA NTRS2023

Retrieval of Normalized Gamma Size Distribution Parameters Using Precipitation Imaging Package (Pip) Snowfall Observations During Ice-Pop 2018

Parameters of the normalized gamma particle size distribution (PSD) have been retrieved from the Precipitation Image Package (PIP) snowfall observations collected during the International Collaborative Experiment - PyeongChang Olympics and Paralympic (ICE-POP 2018). Two of the gamma PSD parameters, the mass weighted particle diameter (Dmass) and the normalized intercept parameter NW, have median values of 1.15-1.31 mm and 2.84-3.04 log(mm-1 m-3), respectively. This range arises from the choice of the relationship between the maximum versus equivalent diameter, Dmx-Deq, and the relationship between the Reynolds and Best numbers, Re-X. Normalization of snow water equivalent rate (SWER) and ice water content (W) by NW reduces the range in NW resulting in well fitted power law relationship, between SWER/NW and Dmass and between W/NW and Dmass. The bulk descriptors of snowfall are calculated from PIP observations and from the gamma PSD with values of the shape parameter (μ) ranging from -2 to 10. NASA’s Global Precipitation Measurement (GPM) mission, which adopted the normalized gamma PSD, assumes μ = 2 and μ = 3 in its two separate algorithms. The mean fractional bias (MFB) of the snowfall parameters changes with μ, where the functional dependence on μ depends on the specific snowfall parameter of interest. The MFB of the total concentration was underestimated by 0.23-0.34 when μ = 2 and by 0.29-0.40 when μ = 3, while the MFB of SWER had a much narrower range (-0.03 to 0.04) for the same μ values.

Snowfall↗