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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Young Lunar Volcanism: Irregular Mae Patches as Drained Lava Ponds and Inflated Flows

Irregular mare patches (IMPs) were previously proposed as sites of young lunar volcanism and/or endogenic activity [1-3], raising the possibility that volcanism on the Moon may not have ended approximately 1 Ga as determined from surface ages [4]. The wide-spread occurrence of more than 70 IMPs on the lunar nearside suggests a correlation with Th-rich PKT regions and a basaltic composition [1,2]. IMPs exhibit at least two types of deposit morphologies: (a) dome-like, steep-sided and smooth mounds and (b) low-relief ropy (LRR) to hummocky (LRH) materials (Fig. 1). Morphologic indicators such as steep (>40 deg) margin slopes across distances <10 m and crisp boundaries imply ages less than a few 100 Ma [e.g.,1-3]. The IMPs also have relatively few superposed craters >10 m in diameter, and young absolute model ages, which when combined with their superposition on young crater deposits like the continuous ejecta of Aristarchus crater, imply ages approximately 10 Ma to 100 Ma [1,2]. However, the mounds do not exhibit blocky crater morphologies, nor fracturing typical of young volcanic deposits, and the mound texture resembles that of mature regolith [e.g., 2,5], implying a much older age (approximately 3.5 Ga). These contrasting indicators have led to the proposal of widely varying formation mechanisms that range from ancient volcanism modified by recent tectonism [6] or outgassing [3,7], ancient volcanism of atypical materials [8], to Copernican-era volcanism [1,2].

Stopar, J. D.↗
NASA NTRS2016

Search for Spectral Irregularities due to Photon-Axionlike-Particle Oscillations with the Fermi Large Area Telescope

We report on the search for spectral irregularities induced by oscillations between photons and axion-like particles (ALPs) in the gamma-ray spectrum of NGC 1275, the central galaxy of the Perseus cluster. Using 6 years of Fermi Large Area Telescope data, we find no evidence for ALPs and exclude couplings above 5 times 10 (sup -12) per gigaelectronvolt for ALP masses less than or approximately equal to 0.5 apparent magnitude (m (sub a)) less than or approximately equal to 5 nanoelectronvolts at 95 percent confidence. The limits are competitive withthe sensitivity of planned laboratory experiments, and, together with other bounds, strongly constrain thepossibility that ALPs can reduce the gamma-ray opacity of the Universe.

Ajello, M.↗
NASA NTRS2021

Measuring Small- and Medium-Scale TEC Variations and Irregularities from Ground-Based GNSS Observations

This paper describes approaches to measuring small- and medium-scale spatial rate of vertical TEC from GNSS observations. The horizontal scale size measured by small-scale TEC spatial rate (STSR, in TECU/km), including its latitude and longitude components, ranges from about 1.8 km to 15 km at 450 km altitude. The medium-scale TEC gradient components can be derived from global ionospheric map (GIM) based TEC gradient (GBTG) over about 110 km horizontally at low and middle latitudes, with smaller scale lengths in the longitude component in the polar region. Our analyses of GPS data show that the latitude component of STSR and GBTG is much larger than the longitude component. The spatial rates in most regions of the globe are relatively small, and their absolute mean values are mostly under 0.03 TECU/km. However, STSR is substantially larger, often greater than 0.05 TECU/km, in the equatorial ionospheric anomaly (EIA) region than in other regions, or when ionospheric disturbances occur. The disturbances include ionospheric irregularities, significant changes of regional ionospheric state during space weather events, and traveling ionospheric disturbances. Combined global TEC gradient (CGTG) with STSR and GBTG is also shown in this paper as snapshots using data from networks of more than three thousand GNSS receivers. The comparisons between STSR and GBTG indicate that their regional variation patterns are consistent though STSR is often larger than GBTG in the equatorial anomaly region or during ionospheric disturbances particularly during space weather events. The difference is attributed to the fact that the global TEC mapping technique may smooth the gradient in regions where spatial variations are large at small- and medium scales.

Akopian, Vardan↗
DOE OSTI2023

Deep neural operators can predict the real-time response of floating offshore structures under irregular waves

The use of neural operators in a digital twin model of an offshore floating structure holds the potential for a significant shift in the prediction of structural responses and health monitoring, offering valuable real-time control insights. In this work, we investigate the effectiveness of three neural operators, namely the deep operator network (DeepONet), the Fourier neural operator (FNO), and the Wavelet neural operator (WNO), to accurately capture the responses of a floating structure under six different sea state codes (3 − 8) based on the wave characteristics described by the World Meteorological Organization (WMO). To further enhance the accuracy of the vanilla architecture of the neural operators, novel extensions, such as wavelet-DeepONet and self-adaptive WNO, are proposed in this paper. The results demonstrate that these high-precision neural operators can deliver structural responses more efficiently, up to two orders of magnitude faster than a dynamic analysis using conventional numerical solvers. Additionally, compared to gated recurrent units (GRUs), a commonly used recurrent neural network for time-series estimation, neural operators are both more accurate and efficient, especially in situations with limited data availability. Taken together, our study shows that FNO outperforms all other operators for approximating the mapping of one input functional space to the output space as well as for responses that have small bandwidth of the frequency spectrum. Conversely, DeepONet, with historical states, proves most accurate in learning the mapping of multiple input functions to the output space and capturing responses within a broad frequency spectrum.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Graph-based design of irregular metamaterials

In the field of metamaterial research, random structures offer a novel and less conventional approach compared to traditional periodic designs. Designing random metamaterials is challenging when it comes to ensuring intercon- nectivity, which is essential for manufacturability. This study introduces an innovative framework for generating random metamaterials using graph al- gorithms, ensuring connectivity and adaptability across various base shapes, including cylinders, triangles, pyramids, and cubes. By employing graph algorithms, our framework enhances the intuitiveness and efficiency of de- sign representation and manipulation, streamlining the design process. The framework generates families of designs that exhibit a wide range of prop- erty magnitudes that can be adjusted intuitively by modifying the input parameters. The rapid design process allows many designs to be generated, offering the user a multitude of solutions around the target property range. The designs can be effectively implemented in various fields and subjected to diverse analytical studies, including static, dynamic, and eigenfrequency assessments. We illustrate computational results for two key properties (stiff- ness and acoustic impedance), showcasing the method’s effectiveness through examples ranging from rod-based to cube-based designs. Here, the framework not only advances metamaterial research but also creates new opportunities for innovation in fields requiring customized material properties.

36 MATERIALS SCIENCE↗
NASA NTRS2000

Robust detection of ionospheric irregularities

The approach outlined in this paper conservatively bounds the ionospheric errors even for the worst observed ionospheric conditions to date, using data sets taken from the operational receivers in the WAAS reference station network.

Wide Area Augmentation System (WAAS) ionosphere ir↗
DOE OSTI2025

Quantifying particle movement in a spout-fluidized bed with irregular feedstock morphology

Here, the spout-bed fluidization behavior of nonspherical, 140 μm SiC feedstock was quantified via particle image velocimetry for varying gas distributor geometries. A bench-scale, room-temperature fluidization setup was assembled to model a 50 mm fluidized bed chemical vapor deposition (FB-CVD) system, and fluidized bed motion was captured using a high-speed camera. Modular tips with varying inlet geometries were 3D printed and tested on the bench-scale rig using identical feedstock and gas flow rates in the range of 3.0–9.0 L/min. Fluidization behavior was quantified by extracting parameters of the bed velocity, frequency, dead time, and other measurements, which were ranked for each inlet geometry configuration tested. The results from this work demonstrate that changing the path of inlet gas flow can significantly change the hydrodynamics within a spout-fluidized bed under identical feedstock, loading, and flow rate conditions, potentially enabling experimental control of particle fluidization behavior for a given condition. Moreover, composite rankings of fluidization behavior for unique distributor geometries hold potential to guide the design of FB-CVD experiments for various engineering and scientific applications.

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

A machine learning-based interaction force model for non-spherical and irregular particles in low Reynolds number incompressible flows

In this study, interaction force of non-spherical particles in low Reynolds number gas-solid flow is investigated by neural network approaches. An artificial neural network (ANN) model is developed to correlate the non-spherical particle shape and the flow conditions with the interaction force. To define the particle shape, spherical harmonic expansion is applied. Furthermore, variational autoencoder model is then used to extract latent geometric features. The latent vector is utilized as an input with the Reynolds number for the ANN. The interaction force data, which is used as output data of the ANN, is obtained by particle resolved direct numerical simulation for 5200 non-spherical particles. The proposed model enables unsupervised extraction for non-spherical particle shapes and accurate predictions on the interaction force without heavy computation. This study provides the model that can explain complicated shapes of particles and be applied to a large scale, computational fluid dynamics simulation.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Discrete element modeling of irregular-shaped soft pine particle flow in an FT4 powder rheometer

Pine residues are a commonly used biomass feedstock that consists of different anatomical fractions, each with distinct particle characteristics. Here, in this work, an experiment-informed discrete element model is developed to investigate the flowability of pine residues in an FT4 rheometer. Multi-sphere particles with distinct particle attributes are created to model each anatomical fraction type. A systematic analysis of specimens with varying particle characteristics (e.g., anatomical fraction type, particle shape, and size) is conducted to elucidate the relationship between particle attributes and flowability. The results show that stems recorded the highest axial force and torque and, correspondingly, the highest flow energy, which is attributed to their high stiffness and interlocking effect. Increasing their percentage in the mixture increases the flow energy while increasing needles tends to decrease flow energy. Knowledge gained in this study on the flow of anatomical fractions is important for the efficient and robust processing of pine residues.

09 BIOMASS FUELS↗