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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Failure detection and accommodation in structural dynamics systems using analytic redundancy

The concept of analytic redundancy is extended to identify nonzero constant output failures in large spacecraft active control sensors and actuators, using a set of distributed sensors which do not nominally produce equivalent signals but are related through the structural dynamics of the system. The suboptimal technique uses a Sequential Probability Ratio Test on the residual sequence of a Kalman filter based on a modal structure model, to determine failure of a component based on an assumed failure hypothesis. The specific failure is identified and then the Kalman filter gains are reconfigured for the identified remaining working sensor set. Experimental data using an apparatus whose dynamics are representative of a large spacecraft show the nominal filter performance under failed and unfailed conditions.

Williams, J. P.↗
NASA NTRS1992

A status of the activities of the NASA/MSFC pump stage technology team

The Consortium for Computational Fluid Dynamics (CFD) Application in Propulsion Technology was established to aid the transfer of CFD related advancements among academia, government agencies, and industry. The specific goals of the Consortium are to develop CFD methodologies necessary to solve propulsion problems, to validate these methodologies, and to apply these methodologies in the design process. To accomplish these goals, a team of experts in various related fields was formed, a schedule of activities necessary to meet the goals was generated, and funding for the activities was obtained from NASA. During the past year (Mar. 1991 - Mar. 1992) the team's activities have focused on preliminary code validation and on the design of an advanced impeller. Six codes were used to calculate the flow in a Rocketdyne 0.3 flow coefficient inducer, and the results were compared to L2F data available for the inducer. This activity identified shortcomings in the experimental data sets and in the analytical solutions which must be surmounted in any future team activity. The design of the advanced impeller relied heavily on CFD results to obtain an optimized geometry. The optimized geometry was analyzed using four different codes, at design and off-design conditions. Activities for the next year include the optimization of a tandem blade impeller design, benchmark of CFD codes for diffuser and volute flows, the collection of L2F data for 'state-of-the-art' impeller and inducer, and the verification of the advanced pump team impeller design in a water rig.

Garcia, R.↗
NASA NTRS2004

Nature and Variability of Coronal Streamers and Their Relationship to the Slow Speed Wind

NASA Grant NAGS12781 is a study on the "Nature and Variability of Coronal Streamers and their Relationship to the Slow Speed Wind." The two main goals of this study are to identify: I ) Where in the streamer structure does the solar wind originate, and 2 ) What coronal conditions are responsible for the variability of the slow speed wind. These goals are to be accomplished by carrying out the following activities: I ) Developing models for inferring the plasma outflow velocities in the extended solar corona based on UV spectroscopic and white light coronagraph data. 2 ) Producing UVCS Carrington Maps for the period around solar minimum 1996-1998. 3) Producing electron density profiles from the corresponding LASCO-C2 observations. 4) Identifying and selecting in situ data sets to be used for characterizing the solar wind from the streamers observed with UVCS. 5) Developing new visualization tools for using the UVCS synoptic data.

Strachan, Leonard↗
NASA NTRS2010

Near Real-Time Optimal Prediction of Adverse Events in Aviation Data

The prediction of anomalies or adverse events is a challenging task, and there are a variety of methods which can be used to address the problem. In this paper, we demonstrate how to recast the anomaly prediction problem into a form whose solution is accessible as a level-crossing prediction problem. The level-crossing prediction problem has an elegant, optimal, yet untested solution under certain technical constraints, and only when the appropriate modeling assumptions are made. As such, we will thoroughly investigate the resilience of these modeling assumptions, and show how they affect final performance. Finally, the predictive capability of this method will be assessed by quantitative means, using both validation and test data containing anomalies or adverse events from real aviation data sets that have previously been identified as operationally significant by domain experts. It will be shown that the formulation proposed yields a lower false alarm rate on average than competing methods based on similarly advanced concepts, and a higher correct detection rate than a standard method based upon exceedances that is commonly used for prediction.

Martin, Rodney Alexander↗
NASA NTRS2012

Traverse Planning Experiments for Future Planetary Surface Exploration

The purpose of the investigation is to evaluate methodology and data requirements for remotely-assisted robotic traverse of extraterrestrial planetary surface to support human exploration program, assess opportunities for in-transit science operations, and validate landing site survey and selection techniques during planetary surface exploration mission analog demonstration at Haughton Crater on Devon Island, Nunavut, Canada. Additionally, 1) identify quality of remote observation data sets (i.e., surface imagery from orbit) required for effective pre-traverse route planning and determine if surface level data (i.e., onboard robotic imagery or other sensor data) is required for a successful traverse, and if additional surface level data can improve traverse efficiency or probability of success (TRPF Experiment). 2) Evaluate feasibility and techniques for conducting opportunistic science investigations during this type of traverse. (OSP Experiment). 3) Assess utility of remotely-assisted robotic vehicle for landing site validation survey. (LSV Experiment).

Hoffman, Stephen J.↗
DOE OSTI2024

Power Profile Monitoring and Tracking Evolution of System-Wide HPC Workloads

The power & energy demands of HPC machines have grown significantly. Modern exascale HPC systems require tens of megawatts of combined power for computing resources and cooling facilities at full capacity. The current energy trend is not sustainable for future HPC systems, and there is a need to work toward the energy efficiency aspect of HPC performance. Energy awareness of the HPC applications at the job level is essential for running an efficient HPC system. This work aims to develop a pipeline to provide a production-level system-wide overview of the HPC workloads' power profile while handling evolving workloads exhibiting new power trends. We developed an open-set classification model for HPC jobs based on the properties of power profiles to continuously provide a system-wide holistic view of recently completed jobs. The pipeline helps continuously monitor the job-level power usage pattern of HPC and enables us to capture the new trends in applications' power behavior. We employed a comprehensive set of techniques to generate job-level data, custom-designed feature extraction methods to extract critical features from jobs' power profiles, clustering techniques powered by generative modeling, and open-set classification for identifying job profiles into known classes or an unknown set. With extensive evaluations, we demonstrate the effectiveness of each component in our pipeline. We provide an analysis of the resulting clusters that characterize the power profile landscape of the Summit supercomputer from more than 60K jobs executed in a year. The open-set classification classifies the known data sets into known classes with high accuracy and identifies unknown data noints with over 85% accuracy.

Karimi, Ahmad Maroof↗
NASA NTRS1980

Geologic application of thermal inertia imaging using HCMM data

The feasibility of using thermal inertia, inferred from remotely sensed temperature data, to complement LANDSAT reflectivity data for reconnaissance geologic mapping and mineral exploration is under investigation. The bulk of HCMM data tapes was received and processed, and a thermal inertia image of one data set was made. Additional areas of interest were identified on the HCMM photographic products and data tapes were ordered for these areas. During analysis of selected subareas, various sedimentary rock units were distinguished in the Death Valley, California test site and areas of altered rock were identified in the Cuprite/Goldifield, Nevada test site.

Paley, H. N.↗
NASA NTRS2003

Geology of the Icy Galilean Satellites: Understanding Crustal Processes and Geologic Histories Through the JIMO Mission

Knowledge of the geology, style and time history of crustal processes on the icy Galilean satellites is necessary to understanding how these bodies formed and evolved. Data from the Galileo mission have provided a basis for detailed geologic and geo- physical analysis. Due to constrained downlink, Galileo Solid State Imaging (SSI) data consisted of global coverage at a -1 km/pixel ground sampling and representative, widely spaced regional maps at -200 m/pixel. These two data sets provide a general means to extrapolate units identified at higher resolution to lower resolution data. A sampling of key sites at much higher resolution (10s of m/pixel) allows evaluation of processes on local scales. We are currently producing the first global geological map of Europa using Galileo global and regional-scale data. This work is demonstrating the necessity and utility of planet-wide contiguous image coverage at global, regional, and local scales.

Figueredo, P. H.↗
DOE OSTI2024

AutoCheck: Automatically Identifying Variables for Checkpointing by Data Dependency Analysis

Checkpoint/Restart (C/R) has been widely deployed in numerous HPC systems, Clouds, and industrial data centers, which are typically operated by system engineers. Nevertheless, there is no existing approach that helps system engineers without domain expertise and domain scientists without system fault tolerance knowledge identify those critical variables accounted for correct application execution restoration in a failure for C/R. To address this problem, we propose an analytical model and a tool (AutoCheck) that can automatically identify critical variables to checkpoint for C/R. AutoCheck relies on first, analytically tracking and optimizing data dependency between variables and other application execution state, and second, a set of heuristics that identify critical variables for checkpointing from the refined data dependency graph (DDG). AutoCheck allows programmers to pinpoint critical variables to checkpoint quickly within a few minutes. We evaluate AutoCheck on 13 representative HPC benchmarks, demonstrating that AutoCheck can efficiently identify correct critical variables to checkpoint.

HPC↗
NASA NTRS1992

Improvement of observer/Kalman filter identification (OKID) by residual whitening

This paper presents a time-domain method to identify a state space model of a linear system and its corresponding observer/Kalman filter from a given set of general input-output data. The identified filter has the properties that its residual is minimized in the least squares sense, orthogonal to the time-shifted versions of itself, and to the given input-output data sequence. The connection between the state space model and a particular auto-regressive moving average description of a linear system is made in terms of the Kalman filter and a deadbeat gain matrix. The procedure first identifies the Markov parameters of an observer system, from which a state space model of the system and the filter gain are computed. The developed procedure is shown to improve results obtained by an existing observer/Kalman filter identification method, which is based on an auto-regressive model without the moving average terms. Numerical and experimental results are presented to illustrate the proposed method.

Phan, Minh↗
NASA NTRS2005

Analysis of Upper Air, Ground and Remote Sensing Data for the Atlas Field Campaign in San Juan, Puerto Rico

The general climate of the island of Puerto Rico is dominated by the easterly trade winds from the Atlantic Ocean, and during synoptically calm days by the topographic and local land surface characteristics [1]. The urban canopy of the metropolitan area of San Juan, capital city of the Island, may introduce a new microclimate that changes the characteristics of the low atmosphere and interacts with the other microclimates already present in the island. The primitive land cover and land use (LCLU) of the metropolitan area of San Juan was composed by broadleaf trees, moist soils, and very dense vegetation in general. The urban LCLU changes the balance for the mass, momentum and energy between the bottom boundary and the lower atmosphere, creating different climate conditions over urban and rural regions. Some of these differences are low relative humidity and high temperatures observed in urban areas when compared to rural areas. These in turn produces a convective circulation over the urban areas, a phenomenon compared to the sea and land breezes, commonly known as heat islands (UHI). Factors that contribute to the formation of the UHI are anthropogenic heat sources, aerosols from pollutants, fast water canalization due to the presence of buildings and streets, among others. The comparison between urban and rural climates is the most common approach to analyze the UHI. These contrasts are larger in clear and calm conditions and tend to disappear in cloudy and windy weather. The UHI was recognized in the early 1950 s as closed isotherms that separates the city from the general temperature field [2]. The impact of the urban LCLU in San Juan, Puerto Rico, was quantified calculating the difference between historical data sets for the air temperature over an identified urban area and a rural area dT(U-R). The analysis of the climatological data revealed that a UHI exists in the metropolitan area of San Juan, Puerto Rico. The data reveals a permanent urban heat island effect present in the SJMA during the year, which is increasing at a rate of 0.41oC/decade. These findings encouraged the planning and execution of an intense field campaign in February 2004 referred as the ATLAS San Juan mission. The focus of the remaining of this report is the analysis of the data for this field campaign.

Gonzalez-Cruz, Jorge E.↗
NASA NTRS1975

Real time telemetry and data compression

The generation of telemetry and data compression by the flight program was verified. The adequacy of flight program telemetry control and timing is proven by the analysis of simulation laboratory runs of past programs through the use of telemetry. The telemetry data are correctly issued using specific tags called process input/output (P10) tags. Verification is accomplished by ensuring that a specific P10 tag and mode register setting identifies the correct parameter and that the data are properly scaled for subsequent ground station reduction. It was checked that the LVDC telemetry correctly adheres to the general requirements specified by the EDD. Data compression specifications are verified using compressed data from nominal flight simulations and from a series of perturbations designed to test data table overflows, data dump rates, and compression of data for occurrence events.

Source record↗
DOE OSTI2024

Coincident learning for unsupervised anomaly detection of scientific instruments

Abstract Anomaly detection is an important task for complex scientific experiments and other complex systems (e.g. industrial facilities, manufacturing), where failures in a sub-system can lead to lost data, poor performance, or even damage to components. While scientific facilities generate a wealth of data, labeled anomalies may be rare (or even nonexistent), and expensive to acquire. Unsupervised approaches are therefore common and typically search for anomalies either by distance or density of examples in the input feature space (or some associated low-dimensional representation). This paper presents a novel approach called coincident learning for anomaly detection (CoAD), which is specifically designed for multi-modal tasks and identifies anomalies based on coincident behavior across two different slices of the feature space. We define an unsupervised metric, F ^ β , out of analogy to the supervised classification F β statistic. CoAD uses F ^ β to train an anomaly detection algorithm on unlabeled data , based on the expectation that anomalous behavior in one feature slice is coincident with anomalous behavior in the other. The method is illustrated using a synthetic outlier data set and a MNIST-based image data set, and is compared to prior state-of-the-art on two real-world tasks: a metal milling data set and our motivating task of identifying RF station anomalies in a particle accelerator.

43 PARTICLE ACCELERATORS↗
NASA NTRS2011

Tool for Rapid Analysis of Monte Carlo Simulations

Designing a spacecraft, or any other complex engineering system, requires extensive simulation and analysis work. Oftentimes, the large amounts of simulation data generated are very di cult and time consuming to analyze, with the added risk of overlooking potentially critical problems in the design. The authors have developed a generic data analysis tool that can quickly sort through large data sets and point an analyst to the areas in the data set that cause specific types of failures. The Tool for Rapid Analysis of Monte Carlo simulations (TRAM) has been used in recent design and analysis work for the Orion vehicle, greatly decreasing the time it takes to evaluate performance requirements. A previous version of this tool was developed to automatically identify driving design variables in Monte Carlo data sets. This paper describes a new, parallel version, of TRAM implemented on a graphical processing unit, and presents analysis results for NASA's Orion Monte Carlo data to demonstrate its capabilities.

Restrepo, Carolina↗
NASA NTRS2004

An Investigation of the Correlation of Water-Ice and Dust Retrievals Via the MGS TES Data Set

Water-ice in the Martian atmosphere was first identified in the Mariner 9 Infrared Interferometer Spectrometer (IRIS) spectra. The Viking Imaging Subsystem (VIS) instruments aboard the Viking orbiter also observed water-ice clouds and hazes in the Martian atmosphere. The MGS TES instrument is an infrared inferometer/spectrometer which covers the spectral range 6-50 micron with a selectable sampling resolution of either 5 or 10 per cm. Using the relatively independent and distinct spectral signatures for dust and water-ice, these two retrieved quantities have been retrieved simultaneously. Although the interrelations among the two quantities have been analyzed by Smith et al. and the retrievals are thought to be robust, understanding the impact of each quantity on the other during their retrievals as well as the impact from the surface for retrievals is important for correctly interpreting the science, and therefore requires close examination. An understanding of the correlation or a-correlation between dust and water-ice would aid in understanding the physical processes responsible for the transport of aerosols in the Martian atmosphere. In this presentation, we present an investigation of the correlation between water-ice and dust in the MGS TES data set.

Qu, Z.↗
NASA NTRS2017

Systemic Response to Microgravity: Utilizing GeneLab Datasets to Identify Molecular Targets for Future Hypotheses-Driven Spaceflight Studies

Biological risks associated with microgravity are a major concern for long-term space travel. Although determination of risk has been a focus for NASA research, data examining systemic (i.e., multi- or pan-tissue) responses to space flight are sparse. To perform our analysis, we utilized the NASA GeneLab database which is a publicly available repository containing a wide array of omics results from experiments conducted with: i) with different flight conditions (space shuttle (STS) missions vs. International Space Station (ISS); ii) a variety of tissues; and 3) assays that measure epigenetic, transcriptional, and protein expression changes. Meta-analysis of the transcriptomic data from 7 different murine and rat data sets, examining tissues such as liver, kidney, adrenal gland, thymus, mammary gland, skin, and skeletal muscle (soleus, extensor digitorum longus, tibialis anterior, quadriceps, and gastrocnemius) revealed for the first time, the existence of potential master regulators coordinating systemic responses to microgravity in rodents. We identified p53, TGF(beta)1 and immune related pathways as the highly prevalent pan-tissue signaling pathways that are affected by microgravity. Some variability in the degree of change in their expression across species, strain and time of flight was also observed. Interestingly, while certain skeletal muscle (gastrocnemius and soleus) exhibited an overall down-regulation of these genes, some other muscle types such as the extensor digitorum longus, tibialis anterior and quadriceps, showed an up-regulated expression, indicative of potential compensatory mechanisms to prevent microgravity-induced atrophy. Key genes isolated by unbiased systems analyses displayed a major overlap between tissue types and flight conditions and established TGF(beta)1 to be the most connected gene across all data sets. Finally, a set of microgravity responsive miRNA signature was identified and based on their predicted functional state and subsequent impact on health, a theoretical health risk score was calculated. The genes and miRNAs identified from our analyses can be targeted for future research involving efficient countermeasure design. Our study thus exemplifies the utility of GeneLab data repository to aid in the process of performing novel hypothesis based spaceflight research aimed at elucidating the global impact of environmental stressors at multiple biological scales.

GeneLab↗
DOE OSTI2025

Development of Machine Learning Algorithm for Pebble Bed Modular Reactor Misuse Detection

The objective of this work was to develop a machine learning ensemble that could assist pebble bed reactor verification by evaluating whether a given pebble circulating through a PBR was normal or anomalous using gamma spectroscopy measurements from a notional PBR burnup measurement system. Using a PBR reference design, data sets of synthetic gamma spectra representative of BUMS measurements of normal and anomalous pebbles that may be used to produce special fissile material were generated to train and test an ML anomaly detection ensemble on two reference scenarios – substitution of normal pebbles with target pebbles for production of Pu or 233 U. The ML ensemble correctly identified all anomalous pebbles in the testing data set, and while perfect ensemble performance is normally indicative of overfitting, it was concluded that significantly lower photon intensity of target pebbles produced distinctly less intense photon spectra to where perfect ensemble performance was expected.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2004

Mapping and Monitoring Delmarva Fox Squirrel Habitat Using an Airborne LiDAR Profiler

Twenty five hundred thirty nine kilometers of airborne laser profiling and videography data were acquired over the state of Delaware during the summer of 2000. The laser ranging measurements and video from approximately one-half of that data set (1304 km) were analyzed to identify and locate forested sites that might potentially support populations of Delmarva fox squirrel (DFS, Sciurus niger cinereus). The DFS is an endangered species previously endemic to tall, dense, mature forests with open understories on the Eastern Shore of the Chesapeake Bay. The airborne LiDAR employed in this study can measure forest canopy height and canopy closure, but cannot measure or infer understory canopy conditions. Hence the LiDAR must be viewed as a tool to map potential, not actual, habitat. Fifty-three potentially suitable DFS sites were identified in the 1304 km of flight transect data. Each of the 53 sites met the following criteria according to the LiDAR and video record: (1 ) at least 120m of contiguous forest; (2) an average canopy height greater than 20m; (3) an average canopy closure of >80%; and (4) no roofs, impervious surface (e.g., asphalt, concrete), and/or open water anywhere along the 120m length of the laser segment. Thirty-two of the 53 sites were visited on the ground and measurements taken for a DFS habitat suitability model. Seventy eight percent of the sites (25 of 32) were judged by the model to be suited to supporting a DFS population. Twenty-eight of the 32 sites visited in the field were in forest cover types (hardwood, mixed wood, conifer, wetlands) according to a land cover GIS map. Of these, 23 (82%) were suited to support DFS. The remaining 4 sites were located in nonforest cover types - agricultural or residential areas. Two of the four, or 50% were suited to the DFS. All of the LiDAR flight data, 2539 km, were analyzed to

Nelson, Ross↗