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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Crustal deformation, the earthquake cycle, and models of viscoelastic flow in the asthenosphere

The crustal deformation patterns associated with the earthquake cycle can depend strongly on the rheological properties of subcrustal material. Substantial deviations from the simple patterns for a uniformly elastic earth are expected when viscoelastic flow of subcrustal material is considered. The detailed description of the deformation pattern and in particular the surface displacements, displacement rates, strains, and strain rates depend on the structure and geometry of the material near the seismogenic zone. The origin of some of these differences are resolved by analyzing several different linear viscoelastic models with a common finite element computational technique. The models involve strike-slip faulting and include a thin channel asthenosphere model, a model with a varying thickness lithosphere, and a model with a viscoelastic inclusion below the brittle slip plane. The calculations reveal that the surface deformation pattern is most sensitive to the rheology of the material that lies below the slip plane in a volume whose extent is a few times the fault depth. If this material is viscoelastic, the surface deformation pattern resembles that of an elastic layer lying over a viscoelastic half-space. When the thickness or breath of the viscoelastic material is less than a few times the fault depth, then the surface deformation pattern is altered and geodetic measurements are potentially useful for studying the details of subsurface geometry and structure. Distinguishing among the various models is best accomplished by making geodetic measurements not only near the fault but out to distances equal to several times the fault depth. This is where the model differences are greatest; these differences will be most readily detected shortly after an earthquake when viscoelastic effects are most pronounced.

Cohen, S. C.↗
NASA NTRS1992

The kinematics of buoyancy induced mixing

A model problem to study mixing driven by buoyancy flow fields due to a time dependent body force which consists of a steady and an oscillatory component is considered. Flow fields generated by a time dependent body force, which consists of a steady and an oscillatory component from vibrations or g-jitter, mix two fluids inside a cavity by stretching and folding the interface into various morphological patterns. These patterns show two basic structures depending on the dominant components of the body force. These structures are whirls and tendrils, corresponding to the dominance of either the steady or oscillatory component, respectively. A combination of these structures also occurs in the interaction region. In this region the effect of the steady component opposes the oscillatory component and the result is to smooth the interface, and prevent breakup of the interface. For the parametric range considered, two basic flow regimes occur: convective and chaotic. In the convective regime, the morphological patterns are topological. In the chaotic regime, mixing occurs by a repetitive sequence of bubble formation, necking, and breakup which spreads the concentration field throughout the cavity. The length stretch of the interface increases exponentially with time. The dynamical system characteristics show that in the convective regime, at a point in space, the flow field is oscillatory with elliptical phase space trajectories, and its power spectrum indicates that the flow field responds to the corresponding input frequency. Whereas, in the chaotic regime, the flow field is aperiodic, the phase space trajectories show irregular patterns, and the power spectrum shows a broadband distribution.

Duval, Walter M. B.↗
NASA NTRS1994

Optimal Correlation Filters for Images with Signal-Dependent Noise

We address the design of optimal correlation filters for pattern detection and recognition in the presence of signal-dependent image noise sources. The particular examples considered are film-grain noise and speckle. Two basic approaches are investigated: (1) deriving the optimal matched filters for the signal-dependent noise models and comparing their performances with those derived for traditional signal-independent noise models and (2) first nonlinearly transforming the signal-dependent noise to signal-independent noise followed by the use of a classical filter matched to the transformed signal. We present both theoretical and computer simulation results that demonstrate the generally superior performance of the second approach in terms of the correlation peak signal-to-noise ratio.

Downie, John D.↗
NASA NTRS1993

Prediction-based dynamic load-sharing heuristics

The authors present dynamic load-sharing heuristics that use predicted resource requirements of processes to manage workloads in a distributed system. A previously developed statistical pattern-recognition method is employed for resource prediction. While nonprediction-based heuristics depend on a rapidly changing system status, the new heuristics depend on slowly changing program resource usage patterns. Furthermore, prediction-based heuristics can be more effective since they use future requirements rather than just the current system state. Four prediction-based heuristics, two centralized and two distributed, are presented. Using trace driven simulations, they are compared against random scheduling and two effective nonprediction based heuristics. Results show that the prediction-based centralized heuristics achieve up to 30 percent better response times than the nonprediction centralized heuristic, and that the prediction-based distributed heuristics achieve up to 50 percent improvements relative to their nonprediction counterpart.

Goswami, Kumar K.↗
NASA NTRS1972

An EPR investigation of the dynamic Jahn-Teller effect in SrCl2:y(2 plus) and SrCl2:Sc(2 plus)

EPR spectra have been observed for SrCl2:Y(2+) and SrCl2:Sc(2+) at liquid helium temperatures. At 1.2 K the spectra were dominated by anisotropic hyperfine patterns whose lineshapes and angular dependences were explained using second order solutions of the effective Hamiltonian for an isolated 2Eg state split by large random internal strains. Pronounced asymmetries in some of the strin produced lineshapes for Srcl2:Sc(2+) are shown to result from second order terms in the solution of the effective Hamiltonian. Coexisting with the anisotropic hyperfine patterns are weak nearly isotropic hyperfine patterns with typical lineshapes. Variations in the apparent intensity of lines in these weak hyperfine patterns as functions of the applied magnetic field direction and temperature imply that these lines result from averaging by vibronic relaxation of a portion of the anisotropic pattern. The effective Hamiltonian parameters for SrCl2:La(2+), SrCl2:y(2+), and SrCl2:SC(2+) are analyzed in terms of crystal field theory modified to include a dynamic Jahn-Teller effect.

Herrington, J. R.↗
NASA NTRS1995

Inferring the thermal-infrared hemispheric emission from a sparsely-vegetated surface by directional measurements

The thermal-infrared (longwave) emission from a vegetated terrain is generally anisotropic, i.e., the emission temperature varies with the view direction. If a directional measurement of temperature is considered to be equal to the effective temperature of the hemispheric emission, then the estimate of the latter can be significantly in error. The view-direction (zenith angle theta(sub eq) at which the emission equivalence does hold is determined in our modeling study. In a two-temperature field-of-view (soil and plants), theta(sub eq) falls in a narrow range depending on plant density and canopy architecture. Theta(sub eq) does not depend on soil and (uniform) plant temperatures nor on their ratio, even though the pattern of emission vs. the view direction depends crucially on this ratio. For a sparse canopy represented as thin, vertical cylindrical stalks (or vertical blades uniformly distributed in azimuth) with horizontal facets, theta(sub eq) ranges from 48 to 53 deg depending on the optical density of the vertical elements alone. When plant elements are modeled as small spheres, theta(sub eq) lies between 53 to 57 deg (for the same values of the canopy optical density). Only for horizontal leaves (a truly planophile canopy) is the temperature measured from any direction equal to the temperature of the hemispheric emission. When the emission temperature changes with optical depth within the canopy at a specified rate, theta(sub eq) depends to some extent on that rate. For practically any sparsely vegetated surface, a directional measurement at the zenith angle of 50 deg offers an appropriate evaluation of the hemispheric emission, since the error in the estimate will, at most, only slightly exceed 1% (around 4 W/sq m). Estimates of the hemispheric emission through a nadir measurement, on the other hand, can be in error in some cases by about 10%, i.e., on the order of 40 W/sq m.

Otterman, J.↗
NASA NTRS1977

Renal electrolyte circadian rhythms - Independence from feeding and activity patterns

Experiments were conducted on six unanesthetized chair-acclimatized adult male squirrel monkeys (Saimiri sciureus) weighing 600-900 g to determine whether internal synchronization is the result of simple passive dependence of renal excretory rhythms on endogenous rhythms of those variable that influence electrolyte excretion such as dietary intake and muscular activity. Independence of the urinary rhythms from diurnal variations in feeding, drinking, and activity was secured by depriving the animals of food, water, and training them to perform a two-hourly schedule of feeding, drinking, and activity throughout day and night. Results indicate that the internal synchronization which is normally observed between the behavioral and urinary rhythms cannot be explained by any direct dependence of renal function on behavioral patterns. The most probable mechanism for circadian internal synchronization is that the various behavioral and renal rhythms are controlled by potentially independent separate oscillators which are normally kept in synchrony with one another.

Moore-Ede, M. C.↗
NASA NTRS1987

The effect of overburden thickness on tension fracture patterns above an uplifting dome

Four experiments demonstrate that tension-fracture patterns above an uplifting dome depend on the thickness of the overburden layer being deformed. Four layers of increasing thicknesses (4.92 cm, 6.92 cm, 9.05 cm, and 11.12 cm) of a very fine sand (85%) and silt-clay (15%) mixture were updomed by slowly inflating a 1.22 m-diameter circular rubber pillow. The upper 2 cm of each layer was wetted and air dried to make it brittle and susceptible to fracture. The fractures that formed during these experiments exhibited a continuum of patterns from dominantly arcuate to dominantly radial as the overburden thickness increased. However, fracture density remained constant in each case for a given amount of surface deformation.

Pranger, H., II↗
NASA NTRS1984

Fidelity of Satellite Soundings

Two pattern recognition procedures to provide improvements to first guess fields for satellite temperature retrievals were developed. The first technique where a radiometer measurement is used to select one or more historical radiosonde temperature profiles as analog estimates of ambient thermal structure. Radiosondes are the vertical scales of the analogs. The vertical resolving power of the satellite radiometer is relevant only to a decision process. The analog selection process is much more effective if implemented in an orthogonalized space of measurement information. The second procedure partitions a priori dependent data into shape coherent pattern libraries using structure information inherent in the data itself. It is shown that the pattern recognition techniques are able to reduce first guess profile errors by nearly 50%. The impact of pattern recognition on temperature retrieval error is assessed by regression and physical iterative retrieval algorithms. The influence of improved first guess fields is markedly different on these two types of algorithm. Pattern recognition was a strong positive impact on the physical iterative method.

Thompson, O.↗
DOE OSTI2025

Impact of Different Thermal Gradients on the Dynamics of Cylindrical Lithium-ion Cells Subject to Accelerated Aging and on Module Performance

This study investigates the impacts of applying different thermal gradient patterns to cylindrical lithium-ion cells in a module on cell dynamics (temperatures, current flows, state of charge), module performance (evolution of resistance, capacity, and energy versus cycle number), and module lifetime. The thermal gradients were generated using cooling plates (CPs) with three different flow-field designs, namely, straight, perpendicular, and U-turn. The study uses computational fluid dynamics (CFD), the pseudo-two-dimensional (P2D) battery model, capacity loss and increased impedance due to the growth of a solid-electrolyte-interphase, and the electric current distribution from module terminals to cells that depends on the series-parallel electrical connections among the cells. The impact of the thermal gradient (resulting from the CP designs) on the variability in resistance, current, state of charge, and voltage among the cells was analyzed and linked to differences in the module's performance. Applying a thermal gradient to parallel-connected strings of series-connected cells led to variation in the current through each parallel string and an imbalance in the voltage of series-connected cells. Module performance is poorer when the thermal gradient causes a voltage imbalance than when it causes a current imbalance. Module performance becomes the worst when both current variation and voltage imbalance happen together. For instance, the module's lifetime (estimated as reaching 80% of its initial capacity) varied by 5% to 17.5%, depending on the magnitude and pattern of the imposed thermal gradient. As the relative orientation between thermal gradients and cells' electrical connectivity influences the module's performance, appropriate consideration should be given to the choice of the CP, especially if large thermal gradients are allowed.

Battery thermal management↗
NASA NTRS1986

Tropical-extratropical geopotential height teleconnections during the Northern Hemisphere winter

Simultaneous and lagged correlation patterns between height anomalies in the Northern Hemisphere (NH) or in the Southern Hemisphere (SH) and height anomalies at tropical stations in two major tropical precipitation zones (the Indochina maritime continent and Africa) were examined. The height anomalies among the tropical stations in these zones and its nearby Pacific Ocean, South America, and Africa are strongly correlated; they are also correlated with stations in Australia and the North PLacific Ocean. The correlations between height anomalies at any of these stations and NH height anomalies show a well-defined global pattern with major features concentrated in the United States and adjacent oceans. Depending upon the location of the stations, the pattern is either a Pacific North American (PNA), a Tropical NH (TNH), or a mixed pattern having both elements. During the El Nino/Southern Oscillation (ENSO) years both TNH and PNA patterns appear in simultaneous and lagged maps, but in non-ENSO years the TNH is weak in simultaneous charts.

Mo, Kingtse C.↗
DOE OSTI2024

Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting

Advancing the capabilities of earthquake nowcasting, the real-time forecasting of seismic activities, remains crucial for reducing casualties. This multifaceted challenge has recently gained attention within the deep learning domain, facilitated by the availability of extensive earthquake datasets. Despite significant advancements, the existing literature on earthquake nowcasting lacks comprehensive evaluations of pre-trained foundation models and modern deep learning architectures; each focuses on a different aspect of data, such as spatial relationships, temporal patterns, and multi-scale dependencies. This paper addresses the mentioned gap by analyzing different architectures and introducing two innovative approaches called Multi Foundation Quake and GNNCoder. We formulate earthquake nowcasting as a time series forecasting problem for the next 14 days within 0.1-degree spatial bins in Southern California. Earthquake time series are generated using the logarithm energy released by quakes, spanning 1986 to 2024. Our comprehensive evaluations demonstrate that our introduced models outperform other custom architectures by effectively capturing temporal-spatial relationships inherent in seismic data. The performance of existing foundation models varies significantly based on the pre-training datasets, emphasizing the need for careful dataset selection. However, we introduce a novel method, Multi Foundation Quake, that achieves the best overall performance by combining a bespoke pattern with Foundation model results handled as auxiliary streams.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1983

An analytic solution for the response of the neutral atmosphere to the high-latitude convection pattern

The analytic solutions to a two-dimensional, shallow-water model of the high-latitude thermosphere are developed. The equations are a linearized representation of the response of the neutral atmosphere to the momentum source due to the plasma convection pattern. The obtained results show how the neutral winds depend on the Pedersen and Hall ion drag coefficients and the horizontal wave number. The response depends on the dimensionless scale as it appears in the product of the Rossby radius of deformation and the wave number. The flow pattern is shifted in local time from the plasma flow pattern by an amount that depends on the average electron density. At the largest scales there is a 180 deg phase shift between the rotational flow components in the E and F regions. The model applies to quiet magnetic conditions and below 200 km of altitude.

Mikkelsen, I. S.↗
NASA NTRS2004

Metal/Silicate Partitioning of P, Ga, and W at High Pressures and Temperatures: Dependence on Silicate Melt Composition

The distinctive pattern of element concentrations in the upper mantle provides essential evidence in our attempts to understand the accretion and differentiation of the Earth (e.g., Drake and Righter, 2002; Jones and Drake, 1986; Righter et al., 1997; Wanke 1981). Core formation is best investigated through use of metal/silicate partition coefficients for siderophile elements. The variables influencing partition coefficients are temperature, pressure, the major element compositions of the silicate and metal phases, and oxygen fugacity. Examples of studies investigating the effects of these variables on partitioning behavior are: composition of the metal phase by Capobianco et al. (1999) and Righter et al. (1997); silicate melt composition by Watson (1976), Walter and Thibault (1995), Hillgren et al. (1996), Jana and Walker (1997), and Jaeger and Drake (2000); and oxygen fugacity by Capobianco et al. (1999), and Walter and Thibault (1995). Here we address the relative influences of silicate melt composition, pressure and temperature.

Bailey, Edward↗
DOE OSTI2026

Lignin structural changes and high p -coumaroylation in incipient lignification in moso bamboo

Lignification is a crucial process for strengthening plant tissues, facilitating water transport, and providing defense against pathogens. In the Poaceae family, p-hydroxycinnamic acids are commonly incorporated into lignin, with acylation by p-coumarate (pCA) occurring during lignification. In this study, we performed DFRC and 2D HSQC-NMR analyses to investigate changes in lignin substructures and the degree of lignin pCA-acylation throughout bamboo stem development. Furthermore, immunohistochemical analysis was conducted to elucidate the spatial distribution of lignin substructures within different cell types. Our results revealed that, in young tissues, β–O–4-linked lignin units are predominantly derived from monolignol-pCA conjugates, specifically coniferyl- and sinapyl-pCA. Both lignin structure and the pattern of pCA acylation varied depending on the stage of cell wall formation and the cell type, particularly between vascular fiber cells and parenchyma cells. Based on our results, moso bamboo culms exhibit a distinctive feature during incipient lignification, in which monolignols are predominantly acylated with pCA. As a result, this feature has not been reported in other grasses, suggesting that extensive p-coumaroylation of monolignols plays an important role in the rapid elongation of bamboo culms.

Munekata, Noriaki [Kyoto University (Japan); Unive↗
DOE OSTI2024

Electronic angle focusing for neutron time-of-flight powder diffractometers

A neutron time-of-flight (TOF) powder diffractometer with a continuous wide-angle array of detectors can be electronically focused to make a single pseudo-constant wavelength diffraction pattern, thus facilitating angle-dependent intensity corrections. The resulting powder diffraction peak profiles are affected by the neutron source emission profile and resemble the function currently used for TOF diffraction.

electronic focusing↗
DOE OSTI2024

Performance Comparison of Machine Learning Models for Ultrasonic Nondestructive Evaluation of Alkali-Silica Reaction in Concrete

Alkali-silica reaction (ASR) causes concrete degradation, leading to cracking, rebar corrosion, and reduced structural integrity, which raises safety concerns. Ultrasonic nondestructive evaluation (NDE) effectively assesses concrete properties and monitors ASR progression. However, its deployment and analysis require specialized expertise and subjective interpretation. As computational power increases, artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used to automate NDE data analysis across various industries for AI-assisted automation. Regulatory agencies are adapting to this technological shift, prompting a need to evaluate current ML technologies’ capabilities and limitations in assessing concrete material properties and damage. This report presents a comparative analysis of four ML regression models for predicting concrete material damage induced by ASR expansion using long-term ultrasonic data monitoring. The models investigated include linear regression (LR), support vector regression (SVR), shallow neural networks (NN), and deep neural networks (DNN). LR, SVR, and shallow NN models use features extracted from ultrasonic signals, whereas the DNN model processes time-domain ultrasonic signals and frequency spectra directly. The study systematically compared the models’ performance from various perspectives, including model input, prediction performance, and generalization ability. The findings indicate significant variability in model performance, with some ML algorithms achieving very high or very low prediction accuracy depending on the preprocessing and feature engineering (extraction and selection) applied. Key insights include the observation that shallow ML models (LR, SVR, and shallow NNs) require meticulous preprocessing and feature extraction to achieve high accuracy. In contrast, the DNN model, although it bypasses the need for feature engineering, necessitates extensive preprocessing to mitigate noise and computational demands. The SVR model emerged as the top performer among the shallow models, and the DNN model exhibited superior performance on specific datasets but struggled with generalization across specimens from different batches. Additionally, the SVR model is sensitive to temperature variations, whereas the DNN model is robust in this regard. Using recurrent neural networks is recommended for future ASR expansion prediction studies. Recurrent neural networks’ inherent ability to capture temporal dependencies and long-term patterns makes them well suited for analyzing sequential ultrasonic monitoring data. Overall, the results and conclusions of this study could provide insights into the capabilities and effectiveness of ML when applied to ultrasonic NDE data and help identify best practices for using ML for ultrasonic NDE of concrete material properties.

36 MATERIALS SCIENCE↗