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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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This bibliography cites 118 articles from the international literature concerning corrosion characteristics of nickel alloys. Articles dealing with corrosion resistance, corrosion tests, intergranular corrosion, oxidation resistance, and stress corrosion cracking of nickel alloys are included.
An abstracted bibliography of 59 articles addressing the analysis of X-ray observations from the UHURU X-Ray Observatory (SAS-1) is presented. Investigations of galactic and extragalactic X-ray sources, X-ray binaries, and transient and burst phenomena are highlighted.
Glassy carbon, a monoatomic allotrope of carbon, is a candidate material for components in fission nuclear power systems due to its radiation tolerance. This article presents comprehensive electron microscopy data revealing the effects of neutron and electron irradiation on glassy carbon. For comparison, additional data are provided for pyrolytic graphite and carbon fibers, materials that exhibit similar structural behavior under irradiation. In situ electron irradiation experiments further illustrate the real-time microstructural evolution of glassy carbon during exposure. The dataset is organized into five parts: (1) transmission electron microscopy (TEM) micrographs of as-received and neutron-irradiated glassy carbon; (2) TEM micrographs of neutron-irradiated graphite; (3) TEM micrographs of unirradiated and irradiated carbon–carbon composites; (4) TEM micrographs of pyrolytic carbon specimens in both conditions; (5) scanning transmission electron microscopy (STEM) micrographs of as-received and neutron-irradiated glassy carbon and (6) in situ electron irradiation data of a glassy carbon particle. These datasets provide valuable insights into radiation-induced structural changes in carbon-based materials relevant to nuclear applications.
Radiation exposure of the hematopoietic system that results in a radiation dose to bone marrow of more than 100 mGy leads to an increase in the risk of leukemia in humans. Excess relative risk of leukemia was observed in cohorts whose members lived in the territories of the Southern Urals that were radioactively contaminated in the 1950s. As part of the dosimetric support of epidemiological studies of these cohorts, an original methodology for stochastic bone dosimetric modeling was developed, termed the Stochastic Parametric Skeletal Dosimetry (SPSD) model. The purpose of this work was to present the anatomical and morphological bases of the SPSD model, which includes an assessment of the parameters of the microstructure of the trabecular bone in the hematopoietic areas of the human skeleton, as well as a description of the macrostructural division (segmentation) of hematopoietic areas into simple bone segments. As a result, an anatomical-morphological basis of the SPSD model was created based on published data. Data collection work included the analysis of original articles, atlases, manuals, monographs and the formation of primary data files. Data on the duration of hematopoiesis in various parts of the skeleton; and data on age-related changes in the microstructure and linear dimensions of human bones and their segments were analyzed. The paper describes the full set of parameters to be used for the dosimetric model for newborns, children aged 1, 5 and 10 years, as well as for adolescents aged 15 years and adults; for the latter, sex differences in bone size were considered. In total, the SPSD model includes 289 unique basic (bone) phantom segments, each of which is described by 7 or more parameters describing the microstructure, thickness of the cortical layer and linear dimensions. Population variability was estimated for each parameter. The approach to SPSD modeling, i.e., the use of simple geometric shapes, was successfully verified using independent datasets on bone masses and volumes.
Savanna fires and atmospheric carbon dioxide (CO2) detection and estimating burned area using Advanced Very High Resolution Radiometer_(AVHRR) reflectance data are investigated in this two part research project. The first part involves carbon dioxide flux estimates and a three-dimensional transport model to quantify the effect of north African savanna fires on atmospheric CO2 concentration, including CO2 spatial and temporal variability patterns and their significance to global emissions. The second article describes two methods used to determine burned area from AVHRR data. The article discusses the relationship between the percentage of burned area and AVHRR channel 2 reflectance (the linear method) and Normalized Difference Vegetation Index (NDVI) (the nonlinear method). A comparative performance analysis of each method is described.
There are often situations where estimates are analyzed as measurements. Here, this article considers in one application whether the raw data underlying these estimates should be analyzed directly.
A local raw ‘diffraction data images’ archive was made available and some data sets were retrieved and reprocessed, which led to analysis of the anomalous difference densities of two partially occupied Cl atoms in cisplatin as well as a re-evaluation of the resolution cutoff in these diffraction data. General questions on storing raw data are discussed. It is also demonstrated that often one needs unambiguous prior knowledge to read the (binary) detector format and the setup of goniometer geometries. Recently, the IUCr (International Union of Crystallography) initiated the formation of a Diffraction Data Deposition Working Group with the aim of developing standards for the representation of raw diffraction data associated with the publication of structural papers. Archiving of raw data serves several goals: to improve the record of science, to verify the reproducibility and to allow detailed checks of scientific data, safeguarding against fraud and to allow reanalysis with future improved techniques. A means of studying this issue is to submit exemplar publications with associated raw data and metadata. In a recent study of the binding of cisplatin and carboplatin to histidine in lysozyme crystals under several conditions, the possible effects of the equipment and X-ray diffraction data-processing software on the occupancies and B factors of the bound Pt compounds were compared. Initially, 35.3 GB of data were transferred from Manchester to Utrecht to be processed with EVAL. A detailed description and discussion of the availability of metadata was published in a paper that was linked to a local raw data archive at Utrecht University and also mirrored at the TARDIS raw diffraction data archive in Australia. By making these raw diffraction data sets available with the article, it is possible for the diffraction community to make their own evaluation. This led to one of the authors of XDS (K. Diederichs) to re-integrate the data from crystals that supposedly solely contained bound carboplatin, resulting in the analysis of partially occupied chlorine anomalous electron densities near the Pt-binding sites and the use of several criteria to more carefully assess the diffraction resolution limit. General arguments for archiving raw data, the possibilities of doing so and the requirement of resources are discussed. The problems associated with a partially unknown experimental setup, which preferably should be available as metadata, is discussed. Current thoughts on data compression are summarized, which could be a solution especially for pixel-device data sets with fine slicing that may otherwise present an unmanageable amount of data.
Data assimilation is the process by which observations are combined with short-range NWP model output to produce an analysis of the state of the atmosphere at a specified time. Since its inception in the late 1990s, the multiagency Weather Research and Forecasting (WRF) model effort has had a strong data assimilation component, dedicating two working groups to the subject. This article documents the history of the WRF data assimilation effort, and discusses the challenges associated with balancing academic, research, and operational data assimilation requirements in the context of the WRF effort to date. The WRF Model's Community Variational/Ensemble Data Assimilation System (WRFDA) has evolved over the past 10 years, and has resulted in over 30 refereed publications to date, as well as implementation in a wide range of real-time and operational NWP systems.
This is a tutorial article about language and architecture approaches for highly concurrent computer systems based on the functional style of programming. The discussion concentrates on the basic aspects of functional languages, and sequencing models such as data-flow, demand-driven and reduction which are essential at the machine organization level. Several examples of highly concurrent machines are described.
Economists are increasingly using weather data and climate model output in analyses of the economic impacts of climate change. This article introduces a set of weather data sets and climate models that are frequently used, discusses the most common mistakes economists make in using these products, and identifies ways to avoid these pitfalls. We first provide an introduction to weather data, including a summary of the types of datasets available, and then discuss five common pitfalls that empirical researchers should be aware of when using historical weather data as explanatory variables in econometric applications. We then provide a brief overview of climate models and discuss two common and significant errors often made by economists when climate model output is used to simulate the future impacts of climate change on an economic outcome of interest.
Report presents analysis of some effects of asymmetry in Manchester (biphase) binary data signal transmitted by phase modulation of sinusoidal carrier signal. Report extends analysis described in article, "Effects of Asymmetry of NRZ Data Signals on Performance" (NPO-18261), to include case where data biphase-modulated directly on residual carrier.
Within the ICSTI Insights Series we offer three articles on the 'living publication' that is already available to practitioners in the important field of crystal structure determination and analysis. While the specific examples are drawn from this particular field, we invite readers to draw parallels in their own fields of interest. The first article describes the present state of the crystallographic living publication, already recognized by an ALPSP (Association of Learned and Professional Society Publishers) Award for Publishing Innovation in 2006. The second article describes the potential impact on the record of science as greater post-publication analysis becomes more common within currently accepted data deposition practices, using processed diffraction data as the starting point. The third article outlines a vision for the further improvement of crystallographic structure reports within potentially achievable enhanced data deposition practices, based upon raw (unprocessed) diffraction data. The IUCr in its Commissions and Journals has for many years emphasized the importance of publications being accompanied by data and the interpretation of the data in terms of atomic models. This has been followed as policy by numerous other journals in the field and its cognate disciplines. This practice has been well served by databases and archiving institutions such as the Protein Data Bank (PDB), the Cambridge Crystallographic Data Centre (CCDC), and the Inorganic Crystal Structure Database (ICSD). Normally the models that are archived are interpretations of the data, consisting of atomic coordinates with their displacement parameters, along with processed diffraction data from X-ray, neutron or electron diffraction studies. In our current online age, a reader can not only consult the printed word, but can display and explore the results with molecular graphics software of exceptional quality. Furthermore, the routine availability of processed diffraction data allows readers to perform direct calculations of the electron density (using X-rays and electrons as probes) or the nuclear density (using neutrons as probe) on which the molecular models are directly based. This current community practice is described in our first article. There are various ways that these data and tools can be used to further analyze the molecules that have been crystallized. Notably, once a set of results is announced via the publication, the research community can start to interact directly with the data and models. This gives the community the opportunity not only to read about the structure, but to examine it in detail, and even generate subsequent improved models. These improved models could, in principle, be archived along with the original interpretation of the data and can represent a continuously improving set of interpretations of a set of diffraction data. The models could improve both by correction of errors in the original interpretation and by the use of new representations of molecules in crystal structures that more accurately represent the contents of a crystal. These possible developments are described in our second article. A current, significant, thrust for the IUCr is whether it would be advantageous for the crystallographic community to require, rather than only encourage, the archiving of the raw (unprocessed) diffraction data images measured from a crystal, a fibre or a solution. This issue is being evaluated in detail by an IUCr Working Group (see http://forums.iucr.org). Such archived raw data would be linked to and from any associated publications. The archiving of raw diffraction data could allow as yet undeveloped processing methods to have access to the originally measured data. The debate within the community about this much larger proposed archiving effort revolves around the issue of 'cost versus benefit'. Costs can be minimized by preserving the raw data in local repositories, either at centralized synchrotron and neutron research institutes, or at research universities. Archiving raw data is also perceived as being more effective than just archiving processed data in countering scientific fraud, which exists in our field, albeit at a tiny level of occurrences. In parallel developments, sensitivities to avoiding research malpractice are encouraging Universities to establish their own data repositories for research and academic staff. These various 'raw data archives', would complement the existing processed data archives. These archives could however have gaps in their coverage arising from a lack of resources. Nevertheless we believe that a sufficiently large raw data archive, with reasonable global coverage, could be encouraged and have major benefits. These possible developments, costs and benefits, are described in our third and final article on 'The living publication'.
This article investigates the applicability of a recently proposed, nonlinear sparse Bayesian learning (NSBL) algorithm to identify and estimate the complex aerodynamics of limit cycle oscillations. NSBL provides a semi-analytical framework for determining the data-optimal sparse model nested within a (potentially) over-parameterized model. This is particularly relevant to nonlinear dynamical systems where modelling approaches involve the use of physics-based and data-driven components. In such cases, the data-driven components, where analytical descriptions of the physical processes are not readily available, are often prone to overfitting, meaning that the empirical aspects of these models will often involve the calibration of an unnecessarily large number of parameters. While an overparameterized model may fit the observed data well, such models may be inadequate for making predictions in regimes that are different from those wherein the data were recorded. In view of this, it is desirable to not only calibrate the model parameters, but also identify the optimal compromise between data fit and model complexity. In this article, we exhibit the optimal model discovery for an aeroelastic system wherein the structural dynamics are well-known and described by a differential equation model, coupled with a semi-empirical aerodynamic model for laminar separation flutter, resulting in low-amplitude limit cycle oscillations (LCO). To illustrate the performance of the algorithm, in this article, we use synthetic data and demonstrate the ability of the algorithm to correctly rediscover the optimal model and model parameters, given a known data-generating model. The synthetic data are generated from a forward simulation of a known differential equation model with parameters selected so as to mimic the dynamics observed in wind-tunnel experiments. Subsequently, we demonstrate the performance of the algorithm for model selection using noisy LCO data from wind tunnel experiments. As there is no ground truth available for the experimental data case, we provide a comparison between NSBL and Bayesian model selection to validate the results, and demonstrate the use of NSBL as an efficient alternative to traditional methods.
This article presents the description of PSIDD(2), a post-scan interactive data display system for ultrasonic contact scan and single measurement analysis. PSIDD(2) was developed in conjunction with ASTM standards for ultrasonic velocity and attenuation coefficient contact measurements. This system has been upgraded from its original version PSIDD(1) and improvements are described in this article. PSIDD(2) implements a comparison mode where the display of time domain waveforms and ultrasonic properties versus frequency can be shown for up to five scan points on one plot. This allows the rapid contrasting of sample areas exhibiting different ultrasonic properties as initially indicated by the ultrasonic contact scan image. This improvement plus additional features to be described in the article greatly facilitate material microstructural appraisal.
Residual gas analyzers (RGA) are commonly used to measure the composition of residual gases in thermal-vacuum test chambers. Measurements from RGA's are often used to identify and quantify outgassing contaminants from a test article during thermal-vacuum testing. RGA data is typically displayed as snapshots in time, showing instantaneous concentrations of ions from ionized residual gas molecules at different atomic masses. This ion concentration information can be interpreted to be representative of the composition of the residual gas in the chamber at the instant of analysis. Typically, test personnel are most interested in tracking the time history of changes in the composition of chamber residual gas to determine the relative cleanliness and the clean-up rate of the test article under vacuum. However, displays of instantaneous RGA data cannot provide test personnel with the preferred time history information. In order to gain an understanding of gas composition trends, a series of plots of individual data snapshots must be analyzed. This analysis is cumbersome and still does not provide a very satisfactory view of residual gas composition trends. A method was devised by the authors to present RCA data in a three-axis format, plotting Atomic Mass Unit (AMU), the Ionization Signal Response (ISR) as amps/torr as a function of AMU, and Time, to provide a clear graphic visualization of trends of changes in ISR with respect to time and AMU (representative of residual gas composition). This graphic visualization method provides a valuable analytical tool to interpret test article outgassing rates during thermal vacuum tests. Raw RGA data was extracted from a series of delimited ASCII files and then converted to a data array in a spreadsheet. Consequently, using the 3-D plotting functionality provided by the spreadsheet program, 3-D plots were produced. After devising the data format conversion process, the authors began developing a program to provide real-time 3-D plotting of RGA data. The intent of this program is to automate the RGA data acquisition process and to generate up-to-the-minute time history 3-D displays of stored RGA data (development of this program was not complete at the time of this writing). This paper provides a brief description of the data format conversion process and presents results from a recent test to illustrate the usefulness of this 3-D RGA data plotting technique.
This is an introductory article for the SPIE VDA 2013 conference proceedings. Additional conference information can be found on http://vda-conference.org.
The use of advanced machine learning algorithms in experimental materials science is limited by the lack of sufficiently large and diverse datasets amenable to data mining. If publicly open, such data resources would also enable materials research by scientists without access to expensive experimental equipment. Here, we report on our progress towards a publicly open High Throughput Experimental Materials (HTEM) Database (htem.nrel.gov). This database currently contains 140,000 sample entries, characterized by structural (100,000), synthetic (80,000), chemical (70,000), and optoelectronic (50,000) properties of inorganic thin film materials, grouped in >4,000 sample entries across >100 materials systems; more than a half of these data are publicly available. This article shows how the HTEM database may enable scientists to explore materials by browsing web-based user interface and an application programming interface. This paper also describes a HTE approach to generating materials data, and discusses the laboratory information management system (LIMS), that underpin HTEM database. Finally, this manuscript illustrates how advanced machine learning algorithms can be adopted to materials science problems using this open data resource.
Statistics and Data Science provide a variety of perspectives and technical approaches for exploring and understanding Big Data. Partnerships between scientists from different fields such as statistics, machine learning, computer science, and applied mathematics can lead to innovative approaches for addressing problems involving increasingly large amounts of data in a rigorous and effective manner that takes advantage of advances in computing. Here, this article will explore various challenges in Data Science and will highlight statistical approaches that can facilitate analysis of large-scale data including sampling and data reduction methods, techniques for effective analysis and visualization of large-scale simulations, and algorithms and procedures for efficient processing.