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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Time scales of pattern evolution from cross-spectrum analysis of advanced very high resolution radiometer and coastal zone color scanner imagery

We have selected square subareas (110 km on a side) from coastal zone color scanner (CZCS) and advanced very high resolution radiometer (AVHRR) images for 1981 in the California Current region off northern California for which we could identify sequences of cloud-free data over periods of days to weeks. We applied a two-dimensional fast Fourier transformation to images after median filtering, (x, y) plane removal, and cosine tapering. We formed autospectra and coherence spectra as functions of a scalar wavenumber. Coherence estimates between pairs of images were plotted against time separation between images for several wide wavenumber bands to provide a temporal lagged coherence function. The temporal rate of loss of correlation (decorrelation time scale) in surface patterns provides a measure of the rate of pattern change or evolution as a function of spatial dimension. We found that patterns evolved (or lost correlation) approximately twice as rapidly in upwelling jets as in the 'quieter' regions between jets. The rapid evolution of pigment patterns (lifetime of about 1 week or less for scales of 50-100 km) ought to hinder biomass transfer to zooplankton predators compared with phytoplankton patches that persist for longer times. We found no significant differences between the statistics of CZCS and AVHRR images (spectral shape or rate of decorrelation). In addition, in two of the three areas studied, the peak correlation between AVHRR and CZCS images from the same area occurred at zero lag, indicating that the patterns evolved simutaneously. In the third area, maximum coherence between thermal and pigment patterns occurred when pigment images lagged thermal images by 1-2 days, mirroring the expected lag of high pigment behind low temperatures (and high nutrients) in recently upwelled water. We conclude that in dynamic areas such as coastal upwelling systems, the phytoplankton cells (identified by pigment color patterns) behave largely as passive scalars at the mesoscale and that growth, death, and sinking of phytoplankton collectively play at most a mariginal role in determining the spectral statistics of the pigment patterns.

Denman, Kenneth L.↗
NASA NTRS1999

Ejecta Pattern of the Impact of Comet Shoemaker-Levy 9

The collision of Comet Shoemaker-Levy 9 (SL 9) with Jupiter created crescent-shaped ejecta patterns around impact sites. Although the observed impact plumes rose through a similar height of approx.3000 km, the radii of the created ejects patterns differ from impact to impact and generally are larger for larger impacts. The azimuthal angle of the symmetry axis of the ejects pattern is larger than that predicted by the models of oblique impacts, due to the action of the Coriolis force that rotates ejecta patterns counterclockwise from the south. We study the formation of ejects patterns using a simple model of ballistic plume above a rotating plane. The ejected particles follow ballistic trajectories and slide horizontally for about an hour after reentry into the jovian atmosphere. The lateral expansion of the plume is stopped by the friction force, which is assumed to be proportional to the square of the horizontal velocity. Two different mass-velocity distributions used in the simulations produce qualitatively similar results. The simulated ejecta patterns fit very well the "crescents" observed at the impact sites. The sizes and azimuthal angles of symmetry axis of ejects patterns depend on a parameter L, which has dimension of length and is related to the mass of the fragment. Thus more massive impacts produce larger ejects patterns that are rotated through a wider angle.

Pankine, Alexey A.↗
NASA NTRS2006

Pattern Formation in Diffusion Flames Embedded in von Karman Swirling Flows

Pattern formation is observed in nature in many so-called excitable systems that can support wave propagation. It is well-known in the field of combustion that premixed flames can exhibit patterns through differential diffusion mechanism between heat and mass. However, in the case of diffusion flames where fuel and oxidizer are separated initially there have been only a few observations of pattern formation. It is generally perceived that since diffusion flames do not possess an inherent propagation speed they are static and do not form patterns. But in diffusion flames close to their extinction local quenching can occur and produce flame edges which can propagate along stoichiometric surfaces. Recently, we reported experimental observations of rotating spiral flame edges during near-limit combustion of a downward-facing polymethylmethacrylate disk spinning in quiescent air. These spiral flames, though short-lived, exhibited many similarities to patterns commonly found in quiescent excitable media including compound tip meandering motion. Flame disks that grow or shrink with time depending on the rotational speed and in-depth heat loss history of the fuel disk have also been reported. One of the limitations of studying flame patterns with solid fuels is that steady-state conditions cannot be achieved in air at normal atmospheric pressure for experimentally reasonable fuel thickness. As a means to reproduce the flame patterns observed earlier with solid fuels, but under steady-state conditions, we have designed and built a rotating, porous-disk burner through which gaseous fuels can be injected and burned as diffusion flames. The rotating porous disk generates a flow of air toward the disk by a viscous pumping action, generating what is called the von K rm n boundary layer which is of constant thickness over the entire burner disk. In this note we present a map of the various dynamic flame patterns observed during the combustion of methane in air as a function of fuel flow rate and the burner rotational speed.

Nayagam, Vedha↗
NASA NTRS2008

System and technique for retrieving depth information about a surface by projecting a composite image of modulated light patterns

A technique, associated system and program code, for retrieving depth information about at least one surface of an object. Core features include: projecting a composite image comprising a plurality of modulated structured light patterns, at the object; capturing an image reflected from the surface; and recovering pattern information from the reflected image, for each of the modulated structured light patterns. Pattern information is preferably recovered for each modulated structured light pattern used to create the composite, by performing a demodulation of the reflected image. Reconstruction of the surface can be accomplished by using depth information from the recovered patterns to produce a depth map/mapping thereof. Each signal waveform used for the modulation of a respective structured light pattern, is distinct from each of the other signal waveforms used for the modulation of other structured light patterns of a composite image; these signal waveforms may be selected from suitable types in any combination of distinct signal waveforms, provided the waveforms used are uncorrelated with respect to each other. The depth map/mapping to be utilized in a host of applications, for example: displaying a 3-D view of the object; virtual reality user-interaction interface with a computerized device; face--or other animal feature or inanimate object--recognition and comparison techniques for security or identification purposes; and 3-D video teleconferencing/telecollaboration.

Hassebrook, Laurence G.↗
NASA NTRS2010

Automated Discovery and Modeling of Sequential Patterns Preceding Events of Interest

The integration of emerging data manipulation technologies has enabled a paradigm shift in practitioners' abilities to understand and anticipate events of interest in complex systems. Example events of interest include outbreaks of socio-political violence in nation-states. Rather than relying on human-centric modeling efforts that are limited by the availability of SMEs, automated data processing technologies has enabled the development of innovative automated complex system modeling and predictive analysis technologies. We introduce one such emerging modeling technology - the sequential pattern methodology. We have applied the sequential pattern methodology to automatically identify patterns of observed behavior that precede outbreaks of socio-political violence such as riots, rebellions and coups in nation-states. The sequential pattern methodology is a groundbreaking approach to automated complex system model discovery because it generates easily interpretable patterns based on direct observations of sampled factor data for a deeper understanding of societal behaviors that is tolerant of observation noise and missing data. The discovered patterns are simple to interpret and mimic human's identifications of observed trends in temporal data. Discovered patterns also provide an automated forecasting ability: we discuss an example of using discovered patterns coupled with a rich data environment to forecast various types of socio-political violence in nation-states.

Rohloff, Kurt↗
NASA NTRS2010

The Spatial Vision Tree: A Generic Pattern Recognition Engine- Scientific Foundations, Design Principles, and Preliminary Tree Design

New foundational ideas are used to define a novel approach to generic visual pattern recognition. These ideas proceed from the starting point of the intrinsic equivalence of noise reduction and pattern recognition when noise reduction is taken to its theoretical limit of explicit matched filtering. This led us to think of the logical extension of sparse coding using basis function transforms for both de-noising and pattern recognition to the full pattern specificity of a lexicon of matched filter pattern templates. A key hypothesis is that such a lexicon can be constructed and is, in fact, a generic visual alphabet of spatial vision. Hence it provides a tractable solution for the design of a generic pattern recognition engine. Here we present the key scientific ideas, the basic design principles which emerge from these ideas, and a preliminary design of the Spatial Vision Tree (SVT). The latter is based upon a cryptographic approach whereby we measure a large aggregate estimate of the frequency of occurrence (FOO) for each pattern. These distributions are employed together with Hamming distance criteria to design a two-tier tree. Then using information theory, these same FOO distributions are used to define a precise method for pattern representation. Finally the experimental performance of the preliminary SVT on computer generated test images and complex natural images is assessed.

Rahman, Zia-ur↗
NASA NTRS2010

System and technique for retrieving depth information about a surface by projecting a composite image of modulated light patterns

A technique, associated system and program code, for retrieving depth information about at least one surface of an object, such as an anatomical feature. Core features include: projecting a composite image comprising a plurality of modulated structured light patterns, at the anatomical feature; capturing an image reflected from the surface; and recovering pattern information from the reflected image, for each of the modulated structured light patterns. Pattern information is preferably recovered for each modulated structured light pattern used to create the composite, by performing a demodulation of the reflected image. Reconstruction of the surface can be accomplished by using depth information from the recovered patterns to produce a depth map/mapping thereof. Each signal waveform used for the modulation of a respective structured light pattern, is distinct from each of the other signal waveforms used for the modulation of other structured light patterns of a composite image; these signal waveforms may be selected from suitable types in any combination of distinct signal waveforms, provided the waveforms used are uncorrelated with respect to each other. The depth map/mapping to be utilized in a host of applications, for example: displaying a 3-D view of the object; virtual reality user-interaction interface with a computerized device; face--or other animal feature or inanimate object--recognition and comparison techniques for security or identification purposes; and 3-D video teleconferencing/telecollaboration.

Hassebrook, Laurence G.↗
NASA NTRS2015

The Pattern Across the Continental United States of Evapotranspiration Variability Associated with Water Availability

The spatial pattern across the continental United States of the interannual variance of warm season water-dependent evapotranspiration, a pattern of relevance to land-atmosphere feedback, cannot be measured directly. Alternative and indirect approaches to estimating the pattern, however, do exist, and given the uncertainty of each, we use several such approaches here. We first quantify the water dependent evapotranspiration variance pattern inherent in two derived evapotranspiration datasets available from the literature. We then search for the pattern in proxy geophysical variables (air temperature, stream flow, and NDVI) known to have strong ties to evapotranspiration. The variances inherent in all of the different (and mostly independent) data sources show some differences but are generally strongly consistent they all show a large variance signal down the center of the U.S., with lower variances toward the east and (for the most part) toward the west. The robustness of the pattern across the datasets suggests that it indeed represents the pattern operating in nature. Using Budykos hydroclimatic framework, we show that the pattern can largely be explained by the relative strength of water and energy controls on evapotranspiration across the continent.

Air Temperature↗
NASA NTRS2016

Investigation of Optimal Digital Image Correlation Patterns for Deformation Measurement

Digital image correlation (DIC) relies on the surface texture of a specimen to measure deformation. When the specimen itself has little or no texture, a pattern is applied to the surface which deforms with the specimen and acts as an artificial surface texture. Because the applied pattern has an effect on the accuracy of DIC, an ideal pattern is sought for which the error introduced into DIC measurements is minimal. In this work, a study is performed in which several DIC pattern quality metrics from the literature are correlated to DIC measurement error. The resulting correlations give insight on the optimality of DIC patterns in general. Optimizations are then performed to produce patterns which are well suited for DIC. These patterns are tested to show their relative benefits. Chief among these benefits are a reduction in error of approximately 30 with respect to a randomly generated pattern.

Bomarito, G. F.↗
DOE OSTI2023

Diffusion-assisted growth of periodic patterns on metal surfaces

Periodic patterns formed on various solid surfaces could seriously affect the properties of materials. Understanding the underlying mechanism is a key step to apply and control pattern formation in many fields. In this work, periodic patterns induced by low energy plasma exposures on tungsten surfaces are observed and related new formation mechanisms are developed. Results from experiments and computer simulations demonstrate that atom diffusion accelerated by low energy and high flux exposures is the dominant mechanism for pattern formation. Additionally, a similar pattern formation has been confirmed by plasma exposure experiments on molybdenum. Based on this mechanism, patterns with controllable size and shape have been successfully prepared on tungsten surface by adjusting plasma exposure parameters, which may provide a new understanding of pattern formation and related surface damage in tungsten induced by plasma exposures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Nucleation and growth of blue phase liquid crystals on chemically-patterned surfaces: a surface anchoring assisted blue phase correlation length

In condensed matter, the correlation length is an essential parameter which describes the distance over which a material maintains its structural properties. In liquid crystals, for the orientational order parameter this characteristic length is of the order of nanometers, while in solid crystals it can extend to macroscopic length scales. Here, we report the measurement of the correlation length, or persistence length, of the crystallographic orientation of blue phases (BPs)—chiral liquid crystals with long-range 3D-crystalline structures and submicron-sized lattice-parameters. These materials exhibit phase transformations that have been identified as the liquid analog of crystal-crystal martensitic transformations. In this work, we use liquid epitaxial growth to achieve spontaneous BP-crystal nucleation and subsequent growth. Specifically, we design patterned substrates made of a binary array of regions with different liquid crystal anchoring, which facilitate a uniform nucleation and growth of BP-crystals with (100)-lattice orientation and a simple cubic symmetry. Furthermore, our results indicate that this simple cubic BP, the so-called blue phase II (BPII), forms first on the patterned surface, thereby propagating the growth of domains in directions parallel or perpendicular to the patterned regions. These results are used to understand the emergence of a surface anchoring assisted BPII-correlation length, taken as the distance over which the BP preserves its lattice orientation, as a function of time and pattern characteristics. We found that BPII single crystals can be achieved on patterned regions whose lateral dimensions are equal to or larger than 10 μm, consistent with our measurements of the BPII-correlation length. This newly acquired understanding of the role of surfaces on the formation of BPs is then used to design processes that permit formation of macroscopically large mono-domain, single-crystal BPs by relying on significantly reduced patterned areas (only part of the area is patterned), a feature that could benefit the applications of this intriguing class of materials.

correlation length↗
NASA NTRS2013

A Formal Basis for Safety Case Patterns

By capturing common structures of successful arguments, safety case patterns provide an approach for reusing strategies for reasoning about safety. In the current state of the practice, patterns exist as descriptive specifications with informal semantics, which not only offer little opportunity for more sophisticated usage such as automated instantiation, composition and manipulation, but also impede standardization efforts and tool interoperability. To address these concerns, this paper gives (i) a formal definition for safety case patterns, clarifying both restrictions on the usage of multiplicity and well-founded recursion in structural abstraction, (ii) formal semantics to patterns, and (iii) a generic data model and algorithm for pattern instantiation. We illustrate our contributions by application to a new pattern, the requirements breakdown pattern, which builds upon our previous work

Formal Methods↗
NASA NTRS2021

Relationship between circum-Arctic atmospheric wave patterns and large-scale wildfires in boreal summer

Long-term assessment of severe wildfires and associated air pollution and related climate patterns in and around the Arctic is essential for assessing healthy human life status. To examine the relationships, we analyzed the National Aeronautics and Space Administration (NASA) modern-era retrospective analysis for research and applications, version 2 (MERRA-2). Our investigation based on this state-of-the-art atmospheric reanalysis data reveals that 13 out of the 20 months with the highest PM2.5 (corresponding to the highly elevated organic carbon in the particulate organic matter [POM] form) monthly mean mass concentration over the Arctic for 2003–2017 were all in summer (July and August), during which POM of ⩾0.5 μg/cu. m and PM2.5 were positively correlated. This correlation suggests that high PM2.5 in the Arctic is linked to large wildfire contributions and characterized by significant anticyclonic anomalies (i.e. clockwise atmospheric circulation) with anomalous surface warmth and drier conditions over Siberia and subpolar North America, in addition to Europe. A similar climate pattern was also identified through an independent regression analysis for the July and August mean data between the same atmospheric variables and the sign-reversed Scandinavian pattern index. We named this pattern of recent atmospheric circulation anomalies the circum-Arctic wave (CAW) pattern as a manifestation of eastward group-velocity propagation of stationary Rossby waves (i.e. large-scale atmospheric waves). The CAW induces concomitant development of warm anticyclonic anomalies over Europe, Siberia, Alaska, and Canada, as observed in late June 2019. Surprisingly, the extended regression analysis of the 1980–2017 period revealed that the CAW pattern was not prominent before 2003. Understanding the CAW pattern under future climate change and global warming would lead to better prediction of co-occurrences of European heatwaves and large-scale wildfires with air pollution over Siberia, Alaska, and Canada in and around the Arctic in summer.

wildfire↗
DOE OSTI2021

Intrinsically Honeycomb-Patterned Hydrogenated Graphene

Since the advent of graphene ushered the era of 2D materials, many forms of hydrogenated graphene have been reported, exhibiting diverse properties ranging from a tunable bandgap to ferromagnetic ordering. Patterned hydrogenated graphene with micron-scale patterns has been fabricated by lithographic means. We report successful millimeter-scale synthesis of an intrinsically honeycomb-patterned form of hydrogenated graphene on Ru(0001) by epitaxial growth followed by hydrogenation is reported. Combining scanning tunneling microscopy observations with density-functional-theory (DFT) calculations, it is revealed that an atomic-hydrogen layer intercalates between graphene and Ru(0001). The result is a hydrogen honeycomb structure that serves as a template for the final hydrogenation, which converts the graphene into graphane only over the template, yielding honeycomb-patterned hydrogenated graphene (HPHG). In effect, HPHG is a form of patterned graphane. DFT calculations find that the unhydrogenated graphene regions embedded in the patterned graphane exhibit spin-polarized edge states. This type of growth mechanism provides a new pathway for the fabrication of intrinsically patterned graphene-based materials.

2D material↗
DOE OSTI2024

Experimental Examination of Additively Manufactured Patterns on Structural Nuclear Materials for Digital Image Correlation Strain Measurements

Abstract Background There are a limited number of commercially available sensors for monitoring the deformation of materials in-situ during harsh environment applications, such as those found in the nuclear and aerospace industries. Such sensing devices, including weldable strain gauges, extensometers, and linear variable differential transformers, can be destructive to material surfaces being investigated and typically require relatively large surface areas to attach (> 10 mm in length). Digital image correlation (DIC) is a viable, non-contact alternative to in-situ strain deformation. However, it often requires implementing artificial patterns using splattering techniques, which are difficult to reproduce. Objective Additive manufacturing capabilities offer consistent patterns using programmable fabrication methods. Methods In this work, a variety of small-scale periodic patterns with different geometries were printed directly on structural nuclear materials (i.e., stainless steel and aluminum tensile specimens) using an aerosol jet printer (AJP). Unlike other additive manufacturing techniques, AJP offers the advantage of materials selection. DIC was used to track and correlate strain to alternative measurement methods during cyclic loading, and tensile tests (up to 1100 µɛ) at room temperature. Results The results confirmed AJP has better control of pattern parameters for small fields of view and facilitate the ability of DIC algorithms to adequately process patterns with periodicity. More specifically, the printed 100 μm spaced dot and 150 μm spaced line patterns provided accurate measurements with a maximum error of less than 2% and 4% on aluminum samples when compared to an extensometer and commercially available strain gauges. Conclusion Our results highlight a new pattern fabrication technique that is form factor friendly for digital image correlation in nuclear applications.

Novich, K. A. (ORCID:0000000204466022)↗
DOE OSTI2024

Phase Identification in Synchrotron X-ray Diffraction Patterns of Ti–6Al–4V Using Computer Vision and Deep Learning

X-ray diffraction patterns contain information about the atomistic structure and microstructure (defect population) of materials, extracting detailed information from diffraction patterns is complex, demanding and relies on prior knowledge. Here, we hypothesize that deep-learning techniques can help to perform an effective and accurate analysis with high throughput rates. To demonstrate this concept, we applied a novel deep learning framework to determine the evolution of the β-phase volume fraction in a Ti–6Al–4V alloy during heat-treatment from video sequences of 2D diffraction patterns recorded in transmission and with highly monochromatic radiation in a synchrotron beamline. In particular, we studied the impact of network design on prediction reliability and computational performance. Networks of different architectures were trained using 3008 experimental 2D patterns. A well-tuned model was found to reproduce the phase fractions of another experimental data set, consisting of 1100 diffraction patterns, with a mean-square error as small as 2.6 x 10 -4 . The average prediction error of β-phase volume fraction was within 1.6 x 10 -2 (in each diffraction pattern) of the values obtained by conventional methods. Our work demonstrates that convolutional neural networks can evaluate high energy X-ray diffraction patterns with a remarkable level of reliability. Furthermore, it demonstrates the significance of network design on the reliability of predictions and computational performance. The most complex models do not necessarily result in highest accuracy and may even fail to learn from the data.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Additive Lithography–Organic Monolayer Patterning Coupled with an Area-Selective Deposition

The combination of area-selective deposition (ASD) with a patternable organic monolayer provides a versatile additive lithography platform, enabling the generation of a variety of nanoscale feature geometries. Stearate hydroxamic acid self-assembled monolayers (SAMs) were patterned with extreme ultraviolet (λ = 13.5 nm) or electron beam irradiation and developed with ASD to achieve line space patterns as small as 50 nm. Density functional theory was employed to aid in the synthesis of hydroxamic acid derivatives with optimized packing density to enhance the imaging contrast and improve dose sensitivity. Near-edge X-ray absorption fine structure spectroscopy and infrared spectroscopy reveal that the imaging mechanism is based on improved deposition inhibition provided by the cross-linking of the SAM to produce a more effective barrier during a subsequent deposition step. With patterned substrates composed of coplanar copper lines and silicon spacers, hydroxamic acids selectively formed monolayers on the metal portions and could undergo a pattern-wise exposure followed by ASD in the first combination of a patternable monolayer with ASD. This material system presents an additional capability compared to traditional ASD approaches that generally reflect a starting patterned surface. Finally, this bottoms-up additive approach to lithography may be a viable alternative to subtractive nanoscale feature generation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Pit rim decomposition into multiple quantum dots on surfaces of epitaxial thin films grown on pit-patterned substrates

Here, we report results of dynamical simulations according to an experimentally validated surface morphological evolution model on the formation of regular arrays of quantum dot molecules (QDMs) consisting of 1D arrays of smaller interacting quantum dots (QDs). These QD arrays form along the sides of each pit rim on the surface of a coherently strained thin film epitaxially deposited on a semiconductor substrate, the surface of which consists of a periodic pattern of inverted prismatic pits with rectangular pit openings. We find that this complex QDM pattern results from the decomposition of the pit rim from a “quantum fortress” with four elongated QDs into four 1D arrays of multiple smaller QDs arranged along each side of the pit rim. Systematic parametric analysis indicates that varying the pit opening dimensions and the pit wall inclination directly impacts the number of QDs in the resulting QDM pattern, while varying the pit depth only affects the dimensions of the QDs in the nanostructure pattern. Therefore, the number, arrangement, and sizes of QDs in the resulting pattern of QDMs on the film surface can be engineered precisely by proper tuning of the pit design parameters. Our simulation results are supported by predictions of morphological stability analysis, which explains the pit rim decomposition into multiple QDs as the outcome of a tip-splitting instability and provides a fundamental characterization of the post-instability nanostructure pattern. Our theoretical findings can play a vital role in designing optimal semiconductor surface patterns toward enabling future nanofabrication technologies.

36 MATERIALS SCIENCE↗