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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Low-Energy Sputtering Studies of Boron Nitride with Xenon Ions

Sputtering of boron nitride with xenon ions was investigated using secondary ion (SIMS) and secondary neutral (SNMS) mass spectrometry. The ions generated from the ion gun were incident on the target at an angle of 50' with respect to the surface'normal. The energy of ions ranged from 100 eV to 3 keV. A flood electron gun was used to neutralize the positive charge build-up on the target surface. The intensities of sputtered neutral and charged particles, including single atoms, molecules, and clusters, were measured as a function of ion energy. Positive SIMS spectra were dominated by the two boron isotopes whereas BN- and B- were the two major constituents of the negative SIMS spectra. Nitrogen could be detected only in the SNMS spectra. The intensity-energy curves of the sputtered particles were similar in shape. The knees in P-SIMS and SNMS intensity-energy curves appear at around I keV which is significantly higher that 100 to 200 eV energy range at which knees appear in the sputtering of medium and heavy elements by ions of argon and xenon. This difference in the position of the sputter yield knee between boron nitride and heavier targets is due to the reduced ion energy differences. The isotopic composition of secondary ions of boron were measured by bombarding boron nitride with xenon ions at energies ranging from 100 eV to 1.5 keV using a quadrupole mass spectrometer. An ion gun was used to generate the ion beam. A flood electron gun was used to neutralize the positive charge buildup on the target surface. The secondary ion flux was found to be enriched in heavy isotopes at lower incident ion energies. The heavy isotope enrichment was observed to decrease with increasing primary ion energy. Beyond 350 eV, light isotopes were sputtered preferentially with the enrichment increasing to an asymptotic value of 1.27 at 1.5 keV. The trend is similar to that of the isotopic enrichment observed earlier when copper was sputtered with xenon ions in the same energy range.

Ray, P. K.↗
DOE OSTI2018

CarbonSAFE Phase I: Integrated CCS Pre-Feasibility – Northwest Gulf of Mexico (Final Research Performance Progress Report)

Offshore storage achieves two major objectives for the US commercial large scale CCS deployment: 1. Adding large capacity to serve local regional, and potentially broader objectives 2. Lowering risk by providing storage with one public owner, away from population, with no conflict with water resources and reduced concern about induced seismicity. A high-concentration CO 2 source was identified as the top candidate for the project and going forward with the CarbonSAFE Phase II proposal. The top-rated source is the NET Power facility in Houston (La Porte), Texas. A manuscript based on analysis of results from the two-stage survey conducted in eight selected Texas counties was submitted to the International Journal of Greenhouse Gas Control on August 21, 2018. A primary confining interval (seal) is associated with MFS9 (biochronozone Amphistegina B) which can reach a thickness of up to 250 m. However, the Amphistegina B confining interval thins considerably in the onshore direction. Consequently, the most suitable portion of the Miocene section for future CO 2 sequestration in the study area is considered to be the offshore area where Amphistegina B is thickest. Based on three models for capacity assessment, the study proposes a base case for the High Island 10-L Field in which 9 wells operated for 12 years each completed into 4 zones will emplace a total of 150MMT of CO 2 with wells placed in the water leg where all the plume will slowly migrate into the structural trap is feasible in terms of geology and engineering. The 10-L Field was assessed in more detail than other examined oil and gas fields in the study area in order to look at some specifics about how initial future CCS projects might be accomplished in the favorable GoM of the US region and expand the sites to a larger set to experiment with matching all the possible sources to sinks. The 10-L site is large enough to accept CO 2 from multiple sinks; the expanded sinks are estimated to be large enough to accept all the CO 2 from the region plus some from outside the region. A number of uncertainties were identified. The largest and most consequential uncertainty is the cost of offshore pipelines in the study setting, which impacts the conditions where CO 2 transport would be by ship versus the cases where pipeline would be preferred. Ships are preferred for small volumes and short durations; pipelines for larger volumes and long duration. Additional work is needed to advance the maturity of multiple sinks available, to continue outreach to industries and the public, and to develop realistic source opportunities. The study demonstrates that industrial source clusters connected to a transport hub delivering CO 2 to a nearby storage complex is the most cost-effective and improved way to de-carbonize industrial activities, particularly, in an expected low-carbon and increasing carbon price environmental. The feasibility of the new business models should be based on the best use of the existing infrastructure and strategically build on new supporting infrastructure to drive down the costs of large-scale CCS deployment. Assessing the pre-feasibility of the commercial implementation of a CCS cluster and hub in the GoM energy ecosystem, our study links these elements successfully through an optimized combination (minimum cost) of CO 2 sources on land with offshore storage. Offshore storage achieves two major objectives for the US commercial large scale CCS deployment: 1. Adding large capacity to serve local regional, and potentially broader objectives 2. Lowering risk by providing storage with one public owner, away from population, with no conflict with water resources and reduced concern about induced seismicity.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

MEDPRAT Treatment Clusters: Improving Representation of Mission Medical Risk

INTRODUCTION The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) implements a computational model that aims to quantify spaceflight medical risk by utilizing probabilistic techniques to simulate critical event incidence and outcomes over thousands of simulated mission trials. The goal of MEDPRAT is to characterize mission medical risk and provide insight into medical resource utilization. In order to analyze the medical resource space, treatment must be mapped from each simulated condition, and resources consumed as a result of this treatment must be tracked throughout the course of the mission. A new MEDPRAT feature, ‘treatment clusters’, provide a more sophisticated method of defining the structure and interaction between resources, more closely mimicking the way treatment is carried out clinically. METHODS Treatment clusters expand on the two existing treatment groupings (combination and alternate) adding a new grouping: bundled treatment. Treatment clusters may be combined to any depth, giving users the ability to specify complex treatment trees whose behavior is governed by several user-specified parameters. This approach emphasizes reusability, as treatment clusters, once defined, can be used to create more complex treatment trees or applied to many conditions. By configuring parameters for contribution, efficacy, necessity, primacy, and equivalence, resource relationships and dependencies can be more accurately represented, thereby allowing users to build capabilities with desired treatment properties, for example an intravenous capability for conditions such as anaphylaxis, acute radiation syndrome, etc. MEDPRAT v1.0 remains backward compatible with existing treatment structures, giving users the ability to define new treatment clusters as evidence becomes available, without having to recode their existing treatment databases. In addition to facilitating the representation of more complex treatment options, by pairing treatment clusters with the internal optimization routine, the MEDPRAT set selector, medical resources can be identified as organized in bundles, where appropriate, so that optimized resource sets include groups of highly-dependent resources only when all resources of the group are together. For example, it would be wasteful to include ultrasound gel but not an ultrasound machine, since the gel provides no benefit as a treatment without the ultrasound machine. With treatment clusters, the user may require that both resources are available to provide any benefit as treatment, so that if one resource is optimized out of the set, the other resource will be optimized out as well. RESULTS AND CONCLUSIONS We will report on MEDPRAT treatment clusters used in a bundling study under the IMPACT project of the ExMC element. We will discuss an example of a complex treatment tree. Through the implementation of this feature MEDPRAT enables treatment to be defined and applied in a way that is more representative of the real world, providing more accurate insight into mission medical risk and the medical resource space.

Lawrence Leinweber↗
DOE OSTI2021

Boron nitride nanotube precursor formation during high-temperature synthesis: kinetic and thermodynamic modelling

In this work, we performed integrated modelling of the chemical pathways of formation for boron nitride nanotube (BNNT) precursors during high-temperature synthesis in a B/N 2 mixture. Integrated modelling includes quantum chemistry, Quantum–classical molecular dynamics, thermodynamic modelling, and kinetic approaches. We demonstrate that BN compounds are formed via the interaction of molecular nitrogen with small boron clusters, rather than through interactions with less reactive liquid boron. (This process can also be described as N 2 molecule fixation.) Liquid boron evaporates to produce these boron clusters (B m with m ≤ 5), which are subsequently converted into B m N n chains. The production of such chains is crucial to the growth of BNNTs because these chains form the building blocks of bigger and longer BN chains and rings, which are in turn the building blocks of fullborenes and BNNTs. Additionally, kinetic modelling revealed that B 4 N 4 and B 5 N 4 species in particular play a major role in the N 2 molecule fixation process. Furthermore, the formation of these species via reactions with B 4 and B 5 clusters is not adequately described under the assumption of thermodynamic equilibrium, as is demonstrated in our kinetic modelling. Thus, the accumulation of both B 4 N 4 and B 5 N 4 depends on the background gas pressure and the gas cooling rate. Long BN chains and rings, which are precursors of the fullborene and BNNT growth, form via self-assembly of components B 4 N 4 and B 5 N 4 . Our modelling results—particularly the increased densities of B 4 N 4 and B 5 N 4 species at higher gas pressures—explain the experimentally observed effect of gas pressure on the yield of high-quality BNNTs. The catalytic role of hydrogen was also studied; it is shown that HBNH molecules can be the main precursor of BNNT synthesis in the presence of hydrogen.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Microstructure effects on fatigue crack growth in additively manufactured Ti–6Al–4V

In this article we investigate the relationship between the different microstructural features in additively-manufactured (AM) Direct Metal Laser Deposited Ti–6Al–4V alloys and the observed fatigue and fracture behaviors, using high resolution Digital Image Correlation experiments and microstructural imaging. Fatigue cracks in the AM specimens were observed to periodically propagate parallel to the α' laths and deflect at prior β grain boundaries. Corresponding plastic strain distributions show needle-like regions of high strain that correlated to the α' laths, suggesting significant influence of the microstructure on the plastic zone. In many cases, the fatigue cracks also propagated towards voids from unsintered powder or gas entrapment in the vicinity. In several experiments, premature failure of the AM specimens occurred under monotonic fracture or cyclic fatigue loading, which we attribute to the presence of void clusters located near the crack-tip. These fatigue and fracture characteristics were similar for AM specimens of different build-orientations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Biomimetic catalysts of iron-based metal–organic frameworks with high peroxidase-mimicking activity for colorimetric biosensing

The field of metal–organic framework (MOF)-based biomimetic catalysts has achieved great progress but is still in its infancy. The systematic investigation of the tailored construction of MOF-based biomimetic catalysts is required for further development. Herein, two iron-based MOFs, namely, [(Fe 3 O) 2 (H 2 O) 4 (HCOO)(L) 2 ] n (HUST-5: H 6 L = hexakis(4-formylphenoxy) cyclotriphosphazene; HUST = Huazhong University of Science and Technology) and [(Fe 3 O)(H 2 O) 3 (L)] n (HUST-7) have been fabricated through the assembly of different iron clusters and hexa-carboxylate ligand under the control of the added acid species. The two MOFs exhibit distinct secondary building units (SBUs) and topological structures, which could be used as biomimetic catalysts for the systematic comparisons of structural characteristics and the catalytic activity. Both MOFs possess catalytic activity similar to that of natural peroxidases towards the catalysis of the oxidation of a variety of substrates. Significantly, HUST-5 and HUST-7 can effectively catalyze the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) by H 2 O 2 accompanied by significant colorimetric biosensing. With same compositions, different catalytic performances were obtained due to differences in the porous structures and characteristics of SBUs in two Fe-MOFs, which was also validated by theoretical calculation results. Furthermore, the phenomenon of colorimetric biosensing could be significantly suppressed by the addition of ascorbic acid (AA) during the oxidation process of TMB. It was observed from these findings that a facile colorimetric biosensing platform for detecting H 2 O 2 and ascorbic acid has been successfully explored. Therefore, this work provides another unique perspective for the tailor-made preparation of stable MOF-based peroxidase mimics with excellent catalytic performance and colorimetric biosensing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Simple and Scalable Streaming: The GRETA Data Pipeline

The Gamma Ray Energy Tracking Array (GRETA) is a state of the art gamma-ray spectrometer being built at Lawrence Berkeley National Laboratory to be first sited at the Facility for Rare Isotope Beams (FRIB) at Michigan State University. A key design requirement for the spectrometer is to perform gamma-ray tracking in near real time. To meet this requirement we have used an inline, streaming approach to signal processing in the GRETA data acquisition system, using a GPU-equipped computing cluster. The data stream will reach 480 thousand events per second at an aggregate data rate of 4 gigabytes per second at full design capacity. We have been able to simplify the architecture of the streaming system greatly by interfacing the FPGA-based detector electronics with the computing cluster using standard network technology. A set of highperformance software components to implement queuing, flow control, event processing and event building have been developed, all in a streaming environment which matches detector performance. Prototypes of all high-performance components have been completed and meet design specifications.

Cromaz, Mario↗
DOE OSTI2023

DESI mock challenge: constructing DESI galaxy catalogues based on FastPM simulations

Together with larger spectroscopic surveys such as the Dark Energy Spectroscopic Instrument (DESI), the precision of large-scale structure studies and thus the constraints on the cosmological parameters are rapidly improving. Therefore, one must build realistic simulations and robust covariance matrices. We build galaxy catalogues by applying a halo occupation distribution (HOD) model upon the FastPM simulations, such that the resulting galaxy clustering reproduces high-resolution N-body simulations. While the resolution and halo finder are different from the reference simulations, we reproduce the reference galaxy two-point clustering measurements – monopole and quadrupole – to a precision required by the DESI Year 1 emission line galaxy sample down to non-linear scales, i.e. $k\lt 0.5\, h\, \mathrm{Mpc}^{-1}$ or $s\gt 10\, \mathrm{Mpc}\, h^{-1}$. Furthermore, we compute covariance matrices based on the resulting FastPM galaxy clustering – monopole and quadrupole. We study for the first time the effect of fitting on Fourier conjugate (e.g. power spectrum) on the covariance matrix of the Fourier counterpart (e.g. correlation function). We estimate the uncertainties of the two parameters of a simple clustering model and observe a maximum variation of 20 per cent for the different covariance matrices. Nevertheless, for most studied scales the scatter is between 2 and 10 per cent. Consequently, using the current pipeline we can precisely reproduce the clustering of N-body simulations and the resulting covariance matrices provide robust uncertainty estimations against HOD fitting scenarios. We expect our methodology will be useful for the coming DESI data analyses and their extension for other studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Deep clustering of protein folding simulations

Background: We investigate the problem of clustering biomolecular simulations using deep learning techniques. Since biomolecular simulation datasets are inherently high dimensional, it is often necessary to build low dimensional representations that can be used to extract quantitative insights into the atomistic mechanisms that underlie complex biological processes. Results: We use a convolutional variational autoencoder (CVAE) to learn low dimensional, biophysically relevant latent features from long time-scale protein folding simulations in an unsupervised manner. We demonstrate our approach on three model protein folding systems, namely Fs-peptide (14 μs aggregate sampling), villin head piece (single trajectory of 125 μs) and β- β- α (BBA) protein (223 + 102 μs sampling across two independent trajectories). In these systems, we show that the CVAE latent features learned correspond to distinct conformational substates along the protein folding pathways. The CVAE model predicts, on average, nearly 89% of all contacts within the folding trajectories correctly, while being able to extract folded, unfolded and potentially misfolded states in an unsupervised manner. Further, the CVAE model can be used to learn latent features of protein folding that can be applied to other independent trajectories, making it particularly attractive for identifying intrinsic features that correspond to conformational substates that share similar structural features. Conclusions: Together, we demonstrate that the CVAE model can quantitatively describe complex biophysical processes such as protein folding.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals

Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials, albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting the dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surfaces (PESs) when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a predefined functional form. Machine learning (ML) models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics, and the complex nanoscale PESs, without sacrificing the bulk properties. Here, in this study, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NNs) for capturing the cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage, to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning via retraining with a higher-fidelity first-principles training data set to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled data set that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly nonequilibrium as well as learning strategies that iteratively improve the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles data set of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML-trained potentials to accelerate materials discovery and design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Discovering Dark Matter Clumps and Primordial Particles with Galaxies

Cosmological observations and galaxy dynamics seem to imply that five out of six parts in mass of all matter in the universe is composed of dark matter, which is not accounted for by the Standard Model of particles. The cold dark matter (CDM) paradigm has been extremely successful at describing observations on large-cosmological scale. However, many different dark matter candidates have the same observable effects as CDM on large-length scales. One possible avenue to distinguish between these different models is to look on much smaller-length scales, where usually dark matter models become distinguishable. This research developed the theoretical framework and statistical tools needed to map the detailed distribution of dark matter on subgalactic scales using strong gravitational lensing. A second goal of this research was to build new statistical techniques that efficiently exploit the full power of cosmic-structure data from next-generation surveys. Galaxy clustering on large scales provides significant cosmological information through the power spectrum. Additional information can be gained from higher-order statistics. This research provided new ways to understand the initial conditions of the universe from current and upcoming cosmological surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Neuromorphic learning with Mott insulator NiO

Habituation and sensitization (nonassociative learning) are among the most fundamental forms of learning and memory behavior present in organisms that enable adaptation and learning in dynamic environments. Emulating such features of intelligence found in nature in the solid state can serve as inspiration for algorithmic simulations in artificial neural networks and potential use in neuromorphic computing. In this work, we demonstrate nonassociative learning with a prototypical Mott insulator, nickel oxide (NiO), under a variety of external stimuli at and above room temperature. Similar to biological species such as Aplysia, habituation and sensitization of NiO possess time-dependent plasticity relying on both strength and time interval between stimuli. A combination of experimental approaches and first-principles calculations reveals that such learning behavior of NiO results from dynamic modulation of its defect and electronic structure. An artificial neural network model inspired by such nonassociative learning is simulated to show advantages for an unsupervised clustering task in accuracy and reducing catastrophic interference, which could help mitigate the stability–plasticity dilemma. Mott insulators can therefore serve as building blocks to examine learning behavior noted in biology and inspire new learning algorithms for artificial intelligence.

42 ENGINEERING↗
DOE OSTI2019

Enhancing laboratory capacity during Ebola virus disease (EVD) heightened surveillance in Liberia: lessons learned and recommendations

Following a declaration by the World Health Organization that Liberia had successfully interrupted Ebola virus transmission on May 9th, 2015; the country entered a period of enhanced surveillance. The number of cases had significantly reduced prior to the declaration, leading to closure of eight out of eleven Ebola testing laboratories. Enhanced surveillance led to an abrupt increase in demand for laboratory services. We report interventions, achievements, lessons learned and recommendations drawn from enhancing laboratory capacity. Using archived data, we reported before and after interventions that aimed at increasing laboratory capacity. Laboratory capacity was defined by number of laboratories with Ebola Virus Disease (EVD) testing capacity, number of competent staff, number of specimens tested, specimen backlog, daily and surge testing capacity, and turnaround time. Using Stata 14 (Stata Corporation, College Station, TX, USA), medians and trends were reported for all continuous variables. Our results show that between May and December 2015, interventions including recruitment and training of eight staff, establishment of one EVD laboratory facility, implementation of ten Ebola GeneXpert diagnostic platforms, and establishment of working shifts yielded an 8-fold increase in number of specimens tested, a reduction in specimens backlog to zero, and restoration of turn-around time to 24 hours. This enabled a more efficient surveillance system that facilitated timely detection and containment of two EVD clusters observed thereafter. In conclusion, effective enhancement of laboratory services during high demand periods requires a combination of context-specific interventions. Building and ensuring sustainability of local capacity is an integral part of effective surveillance and disease outbreak response efforts.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Ruminococcus gnavus , a member of the human gut microbiome associated with Crohn’s disease, produces an inflammatory polysaccharide

A substantial and increasing number of human diseases are associated with changes in the gut microbiota, and discovering the molecules and mechanisms underlying these associations represents a major research goal. Multiple studies associate Ruminococcus gnavus , a prevalent gut microbe, with Crohn’s disease, a major type of inflammatory bowel disease. We have found that R. gnavus synthesizes and secretes a complex glucorhamnan polysaccharide with a rhamnose backbone and glucose sidechains. Chemical and spectroscopic studies indicated that the glucorhamnan was largely a repeating unit of five sugars with a linear backbone formed from three rhamnose units and a short sidechain composed of two glucose units. The rhamnose backbone is made from 1,2- and 1,3-linked rhamnose units, and the sidechain has a terminal glucose linked to a 1,6-glucose. This glucorhamnan potently induces inflammatory cytokine (TNFα) secretion by dendritic cells, and TNFα secretion is dependent on toll-like receptor 4 (TLR4). We also identify a putative biosynthetic gene cluster for this molecule, which has the four biosynthetic genes needed to convert glucose to rhamnose and the five glycosyl transferases needed to build the repeating pentasaccharide unit of the inflammatory glucorhamnan.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

dCache on Steroids - Delegated Storage Solutions

For over a decade, dCache.org has delivered a robust software used at more than 80 Universities and research institutes around the world, allowing these sites to provide reliable storage services for the WLCG experiments as well as many other scientific communities. The flexible architecture of dCache allows running it in a wide variety of configurations and platforms - from a SoC based all-in-one Raspberry-Pi up to hundreds of nodes in a multipetabyte installation. Due to lack of managed storage at the time, dCache implemented data placement, replication and data integrity directly. Today, many alternatives are available: S3, GlusterFS, CEPH and others. While such solutions position themselves as scalable storage systems, they cannot be used by many scientific communities out of the box. The absence of community-accepted authentication and authorization mechanisms, the use of product specific protocols and the lack of namespace are some of the reasons that prevent wide-scale adoption of these alternatives. Most of these limitations are already solved by dCache. By delegating low-level storage management functionality to the above-mentioned new systems and providing the missing layer through dCache, we provide a solution which combines the benefits of both worlds - industry standard storage building blocks with the access protocols and authentication required by scientific communities. In this paper, we focus on CEPH, a popular software for clustered storage that supports file, block and object interfaces. CEPH is often used in modern computing centers, for example as a backend to OpenStack services. We will show prototypes of dCache running with a CEPH backend and discuss the benefits and limitations of such an approach. As a result, we will also outline the roadmap for supporting ‘delegated storage’ within the dCache releases.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1990

MEASURE: An integrated data-analysis and model identification facility

The first phase of the development of MEASURE, an integrated data analysis and model identification facility is described. The facility takes system activity data as input and produces as output representative behavioral models of the system in near real time. In addition a wide range of statistical characteristics of the measured system are also available. The usage of the system is illustrated on data collected via software instrumentation of a network of SUN workstations at the University of Illinois. Initially, statistical clustering is used to identify high density regions of resource-usage in a given environment. The identified regions form the states for building a state-transition model to evaluate system and program performance in real time. The model is then solved to obtain useful parameters such as the response-time distribution and the mean waiting time in each state. A graphical interface which displays the identified models and their characteristics (with real time updates) was also developed. The results provide an understanding of the resource-usage in the system under various workload conditions. This work is targeted for a testbed of UNIX workstations with the initial phase ported to SUN workstations on the NASA, Ames Research Center Advanced Automation Testbed.

Singh, Jaidip↗
NASA NTRS2002

Louisiana NASA EPSCoR Project

In 1994, the National Aeronautics and Space Administration issued a Cooperative Agreement (CA) to the State of Louisiana, through the Louisiana Board of Regents (BOB), for the performance of scientific research under the Experimental Program to Stimulate Competitive Research (EPSCoR) Project. Originally constructed as a three-year program with an optional two-year follow on, this federal-state partnership culminated on 31 October 2001, including two CA extensions. The total value of the project reached $3.3M in NASA funding, matched by $2.75M in BOB funds, and supplemented by several million dollars in institutional contributions. Three Research Clusters comprised the state-wide research effort coupled with scientific/technical management and a teacher involvement component. The three research clusters addressed the Enterprises of Space Science, Earth Science and Aerospace Technology with research in High Energy Astrophysics, the Global Carbon Cycle, and Propulsion. Ten universities, over two dozen faculty, over 150 students and numerous support personnel were involved. All of the scientific and technical objectives were met or exceeded. In aggregate, the clusters generated about $18M in outside support, better than a 2:1 return on investment (better than 5:1 considering only the NASA investment). Moreover, two of the clusters have advanced to the level of applying for major NSF research center designation. This project was a trial of the model of building research infrastructure through mentoring. While not completely successful, the results at the smaller institutions were, none the less, positive. Faculty were engaged in major research and involved their students. Administrations improved their capabilities to handle grants and contracts. Faculty release time was granted, research space was provided and, in some cases, equipment was made available for the research. Some of the faculty at these schools have remained involved in research and/or formed collaborations for new endeavors, demonstrating that the mentoring model can be successful.

Wefel, John P.↗
DOE OSTI2020

Performance and usability enhancements for continuous subgraph matching queries on graph-structured data

A query graph, which includes vertices and edges, represents a query on graph-structured data. The query graph is decomposed into query subgraphs. A network analysis tool performs continuous subgraph matching queries to facilitate analysis of computer network traffic, social media events, or other streams of data represented as a dynamic data graph (graph-structured data). This can help identify emerging trends in the data. Some features of the network analysis tool enhance performance by effectively utilizing distributed computing resources (including processing cores and memory at different nodes of a cluster) to speed up the process of updating the dynamic data graph and detecting matches of query subgraphs. Features of a query graph building tool enhance usability by providing intuitive ways to specify query graphs and their subgraphs. Features of a results visualization tool enhance usability by providing an intuitive way to present the results of continuous subgraph matching queries.

Choudhury, Sutanay↗