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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 NTRS2008

Bonding by Hydroxide-Catalyzed Hydration and Dehydration

A simple, inexpensive method for bonding solid objects exploits hydroxide-catalyzed hydration and dehydration to form silicate-like networks in thin surface and interfacial layers between the objects. The method can be practiced at room temperature or over a wide range of temperatures. The method was developed especially to enable the formation of precise, reliable bonds between precise optical components. The bonds thus formed exhibit the precision and transparency of bonds formed by the conventional optical-contact method and the strength and reliability of high-temperature frit bonds. The method also lends itself to numerous non-optical applications in which there are requirements for precise bonds and/or requirements for bonds, whether precise or imprecise, that can reliably withstand severe environmental conditions. Categories of such non-optical applications include forming composite materials, coating substrates, forming laminate structures, and preparing objects of defined geometry and composition. The method is applicable to materials that either (1) can form silicate-like networks in the sense that they have silicate-like molecular structures that are extensible into silicate-like networks or (2) can be chemically linked to silicate-like networks by means of hydroxide-catalyzed hydration and dehydration. When hydrated, a material of either type features surface hydroxyl (-OH) groups. In this method, a silicate-like network that bonds two substrates can be formed either by a bonding material alone or by the bonding material together with material from either or both of the substrates. Typically, an aqueous hydroxide bonding solution is dispensed and allowed to flow between the mating surfaces by capillary action. If the surface figures of the substrates do not match precisely, bonding could be improved by including a filling material in the bonding solution. Preferably, the filling material should include at least one ingredient that can be hydrated to have exposed hydroxyl groups and that can be chemically linked, by hydroxide catalysis, to a silicate-like network. The silicate-like network could be generated in situ from the filling material and/or substrate material, or could be originally present in the bonding material.

Gwo, Dz-Hung↗
NASA NTRS1993

Radial basis function network learns ceramic processing and predicts related strength and density

Radial basis function (RBF) neural networks were trained using the data from 273 Si3N4 modulus of rupture (MOR) bars which were tested at room temperature and 135 MOR bars which were tested at 1370 C. Milling time, sintering time, and sintering gas pressure were the processing parameters used as the input features. Flexural strength and density were the outputs by which the RBF networks were assessed. The 'nodes-at-data-points' method was used to set the hidden layer centers and output layer training used the gradient descent method. The RBF network predicted strength with an average error of less than 12 percent and density with an average error of less than 2 percent. Further, the RBF network demonstrated a potential for optimizing and accelerating the development and processing of ceramic materials.

Cios, Krzysztof J.↗
DOE OSTI2021

Estimating Internal Stress of an Alteration Layer Formed on Corroded Boroaluminosilicate Glass through Spectroscopic Ellipsometry Analysis

Aqueous corrosion of glass may result in the formation of an alteration layer in the glass surface of which chemical composition and network structure are different from those of the bulk glass. Since corrosion occurs far below the glass-transition temperature, the alteration layer cannot fully relax to the new structure with the lowest possible energy. Molecular dynamics simulations suggested that such a network will contain highly strained chemical bonds, which can be manifested as a stress in the alteration layer. Common techniques to measure stress in thin films or surface layers were found inadequate for thick monolithic glass samples corroded in water. Here, we explored the use of spectroscopic ellipsometry to test the presence of internal stress in the alteration layer formed by aqueous corrosion of glass. Furthermore, a procedure for analyses of spectroscopic ellipsometry data to determine birefringence in the alteration layer was developed. Findings with the established fitting procedure suggested that a stress builds up in the corroded surface layer of a boroaluminosilicate glass if there is a change in relative humidity, pH, or electrolyte concentration of the environment to which the glass surface is exposed. A similar process may occur in other types of glass, and it may affect the surface properties of corroded glass objects.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRSDate not supplied

Design and Analysis of Convolutional Neural Network for RF Signal Modulation Classification for In-Orbit Deployment

To effectively transmit data to and from satellites requires a complex and robust RF communication system. Commonly, several different types of signal modulations may be required to maximize satellite efficiency depending on a variety of unexpected channel impairments. We propose a neural network algorithm capable of learning these RF signal modulations using a supervised learning technique designed for low power, high-efficiency in-orbit deployment. The work presented demonstrates a convolutional neural network (CNN) capable of learning and recognizing a set of modulation schemes commonly used to transmit RF information. We are capable of recognizing the modulation scheme from the I and Q data channels directly, with no preprocessing or data conversion required other than breaking the incoming signal into a set of uniform normalized samples. We perform a network design and size analysis, showing that reasonably high accuracy can be obtained using networks with a relatively low number of trainable parameters. Given that a user of a system such as this may wish to receive a signal using a modulation scheme that the network has not previously learned, we demonstrate that transfer learning can learn new modulation schemes by retraining only the fully connected layers in the CNN. Thus, this type of network would excel in outer space deployment using high-efficiency transfer learning hardware. Modulation recognition can be performed through rapid feedforward computation, and the CNN training process is significantly simplified when learning new modulations is required.

CNN↗
DOE OSTI2020

On Transfer Learning Of Neural Networks Using Bi-Fidelity Data For Uncertainty Propagation

Due to their high degree of expressiveness, neural networks have recently been used as surrogate models for mapping inputs of an engineering system to outputs of interest. Once trained, neural networks are computationally inexpensive to evaluate and remove the need for repeated evaluations of computationally expensive models in uncertainty quantification applications. However, given the highly parameterized construction of neural networks, especially deep neural networks, accurate training often requires large amounts of simulation data that may not be available in the case of computationally expensive systems. In this paper, to alleviate this issue for uncertainty propagation, we explore the application of transfer learning techniques using training data generated from both high- and low-fidelity models. We explore two strategies for coupling these two datasets during the training procedure, namely, the standard transfer learning and the bi-fidelity-weighted learning. In the former approach, a neural network model mapping the inputs to the outputs of interest is trained based on the low-fidelity data. The high-fidelity data are then used to adapt the parameters of the upper layer(s) of the low-fidelity network, or train a simpler neural network to map the output of the low-fidelity network to that of the high-fidelity model. In the latter approach, the entire low-fidelity network parameters are updated using data generated via a Gaussian process model trained with a small high-fidelity dataset. The parameter updates are performed via a variant of stochastic gradient descent with learning rates given by the Gaussian process model. Using three numerical examples, we illustrate the utility of these bi-fidelity transfer learning methods where we focus on accuracy improvement achieved by transfer learning over standard training approaches.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1995

Quantitative analysis of volatile organic compounds using ion mobility spectra and cascade correlation neural networks

Ion Mobility Spectrometry (IMS) is a powerful technique for trace organic analysis in the gas phase. Quantitative measurements are difficult, because IMS has a limited linear range. Factors that may affect the instrument response are pressure, temperature, and humidity. Nonlinear calibration methods, such as neural networks, may be ideally suited for IMS. Neural networks have the capability of modeling complex systems. Many neural networks suffer from long training times and overfitting. Cascade correlation neural networks train at very fast rates. They also build their own topology, that is a number of layers and number of units in each layer. By controlling the decay parameter in training neural networks, reproducible and general models may be obtained.

Harrington, Peter DEB.↗
DOE OSTI2023

3D Carbon Coating Enabled High‐capacity and Stable Micro‐sized Silicon Suboxide‐graphite Blended Anodes for Practical Lithium‐ion Batteries

Abstract Silicon oxide (SiO x ) is a promising anode candidate of lithium‐ion batteries (LIBs) owing to its extremely high specific capacity. However, the low initial Coulombic efficiency (ICE) and rapid capacity degradation of SiO x , triggered by the enormous volume variation upon repeated (de)lithiation, gravely hinder its practical use. Herein, two mass‐produced micro‐sized SiO x @C composites with obviously different morphologies for commercial LIBs are reported. Particularly, the SiO x ‐graphite blended anode (SiO x @3D‐G‐Gr) based on SiO x wrapped by three‐dimensional (3D) carbon layers (SiO x @3D‐G) exhibits a capacity of 519 mAh g −1 , an ICE of 90.0 % and a capacity retention of 83.4 % at 0.2 C over 100 cycles. which is far exceeding its counterpart SiO x @C‐H‐Gr (65.7 %). The obtained impressive properties of SiO x @3D‐G originate from the critical contribution of 3D carbon layers, which serves as the effective stress buffer and protective layer as well as the strong networks for electron/Li + transport. Accordingly, the full‐cell based on SiO x @3D‐G‐Gr anode and commercial LiCoO 2 cathode delivers a capacity of 803 mAh and an excellent capacity retention of 95.6 % (616 mAh, 96.6 % for graphite, respectively) at 1 C over 100 cycles with a stabilized CE of nearly 100 %. The micro‐sized SiO x @3D‐G showing a promising prospect in the commercial‐grade anodes in LIBs.

Electrochemistry↗
DOE OSTI2022

Registration and fusion of large-scale melt pool temperature and morphology monitoring data demonstrated for surface topography prediction in LPBF

In-situ monitoring technologies for laser powder bed fusion (LPBF) additive manufacturing often face one key challenge, extracting the ultrafast melt pool (MP) signatures for understanding the localized part properties. Further, the spatial information of each monitored MP signature is essential for correlating the MP – part property. This spatial information is often unavailable especially from commercial LPBF printers. Many MP monitoring methods have been reported and utilized. However, very few of these have the MP’s spatial information. To overcome this challenge, in this work we report a method for spatially registering the key MP signatures (MP intensity, temperature, and area) to the monitored print parts. The MP signatures are obtained from our coaxial high-speed single-camera based two-wavelength imaging pyrometry (STWIP) system and the MP spatial information is obtained from an off-axis camera system. A machine learning aided image analysis method is employed to retrieve the spatial distribution of MPs within the corresponding part’s coordinates system. Then, the MP signature maps (MPSMs) are reconstructed by mapping the STWIP measured MP signatures to the registered MP coordinates. Further, a long short-term memory (LSTM) neural network is developed for estimating the layer surface topography from the registered MPSMs. The obtained results indicate that the layer surface topography can be more accurately estimated by using MP temperature signature rather than MP intensity and/or area signatures as in common practice. Finally, our developed methods for MP monitoring, registration, and MP-surface topography prediction offer advanced capabilities for the online detection of process anomalies and part defects.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Microstructural Engineering of Cu-Rich Nanoprecipitate formation in NiCoFeCrCu0.12 High-Entropy Alloy via Severe Plastic Deformation for Enhanced Irradiation Tolerance

This study demonstrates a defect-engineering approach for controlling Cu-rich precipitates in FeNiCrCoCu0.2 high-entropy alloys (Cu-HEAs), delivering a novel pathway for next-generation nuclear reactor materials with superior irradiation resistance. This work establishes that severe plastic deformation (SPD) processing via Shear Assisted Processing and Extrusion (ShAPE) and Friction Stir Layer Deposition (FSLD) creates dense dislocation networks and subgrain boundaries that fundamentally alter precipitation behavior under identical thermal treatments. Atom probe tomography (APT) indicates that SPD produces a metastable, atomically homogeneous solid solution that, upon moderate heat treatment (500°C/10 hour), develops remarkedly stronger Cu clustering than the as-cast counterpart. High-temperature exposure (800°C/100 h) produces near-pure Cu precipitates (~90 at% Cu) with significantly enhanced defect-sink efficacy in SPD-processed alloys: precipitate sizes of 50-60 nm and number densities of 2.7-3.8 × 10¹7 m?³, compared to 89 nm and 0.44 × 10¹7 m?³ in as-cast materials. Collectively, the findings establish defect-mediated precipitation control as a scalable, high-impact route to tailor sink density and distribution in HEAs, enabling microstructures optimized for irradiation tolerance and mechanical robustness in nuclear reactor environments.

Meher, Subhashish↗
DOE OSTI2023

Discovery and construction of surface kagome electronic states induced by p-d electronic hybridization in Co3Sn2S2

Abstract Kagome-lattice materials possess attractive properties for quantum computing applications, but their synthesis remains challenging. Herein, based on the compelling identification of the two cleavable surfaces of Co 3 Sn 2 S 2 , we show surface kagome electronic states (SKESs) on a Sn-terminated triangular Co 3 Sn 2 S 2 surface. Such SKESs are imprinted by vertical p-d electronic hybridization between the surface Sn (subsurface S) atoms and the buried Co kagome-lattice network in the Co 3 Sn layer under the surface. Owing to the subsequent lateral hybridization of the Sn and S atoms in a corner-sharing manner, the kagome symmetry and topological electronic properties of the Co 3 Sn layer is proximate to the Sn surface. The SKESs and both hybridizations were verified via qPlus non-contact atomic force microscopy (nc-AFM) and density functional theory calculations. The construction of SKESs with tunable properties can be achieved by the atomic substitution of surface Sn (subsurface S) with other group III-V elements (Se or Te), which was demonstrated theoretically. This work exhibits the powerful capacity of nc-AFM in characterizing localized topological states and reveals the strategy for synthesis of large-area transition-metal-based kagome-lattice materials using conventional surface deposition techniques.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Dome-like pressure-temperature phase diagram of the cooperative Jahn–Teller distortion in NaNiO 2

NaNiO 2 is a Ni 3+ -containing layered material consisting of alternating triangular networks of Ni and Na cations, separated by octahedrally-coordinated O anions. At ambient pressure, it features a collinear Jahn–Teller distortion below $T^{JT}_{onset} ≈ 480$ K, which disappears in a first-order transition on heating to $T^{JT}_{onset} ≈ 500$ K, corresponding to the increase in symmetry from monoclinic to rhombohedral. It was previously studied by variable-pressure neutron diffraction (Nagle-Cocco et al 2022 ACS Inorg. Chem. 61 4312) and found to exhibit an increasing $T^{JT}_{onset}$ with pressure up to ∼5 GPa. In this work, powdered NaNiO 2 was studied via variable-pressure synchrotron x-ray diffraction up to pressures of ∼67 GPa at 294 K and 403 K. Suppression of the collinear Jahn–Teller ordering is observed via the emergence of a high-symmetry rhombohedral phase, with the onset pressure occurring at ∼18 GPa at both studied temperatures. Further, a discontinuous decrease in unit cell volume is observed on transitioning from the monoclinic to the rhombohedral phase. These results taken together suggest that in the vicinity of the transition, application of pressure causes the Jahn–Teller transition temperature, $T^{JT}_{onset}$, to decrease rapidly. We conclude that the pressure-temperature phase diagram of the cooperative Jahn–Teller distortion in NaNiO 2 is dome-like.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

A Projected Network Model of Online Disinformation Cascades

Within the past half-decade, it has become overwhelmingly clear that suppressing the spread of deliberate false and misleading information is of the utmost importance for protecting democratic institutions. Disinformation has been found to come from both foreign and domestic actors, but the effects from either can be disastrous. From the simple encouragement of unwarranted distrust to conspiracy theories promoting violence, the results of disinformation have put the functionality of American democracy under direct threat. Present scientific challenges posed by this problem include detecting disinformation, quantifying its potential impact, and preventing its amplification. We present a model on which we can experiment with possible strategies toward the third challenge: the prevention of amplification. This is a social contagion network model, which is decomposed into layers to represent physical, ''offline'', interactions as well as virtual interactions on a social media platform. Along with the topological modifications to the standard contagion model, we use state-transition rules designed specifically for disinformation, and distinguish between contagious and non-contagious infected nodes. We use this framework to explore the effect of grassroots social movements on the size of disinformation cascades by simulating these cascades in scenarios where a proportion of the agents remove themselves from the social platform. We also test the efficacy of strategies that could be implemented at the administrative level by the online platform to minimize such spread. These top-down strategies include banning agents who disseminate false information, or providing corrective information to individuals exposed to false information to decrease their probability of believing it. We find an abrupt transition to smaller cascades when a critical number of random agents are removed from the platform, as well as steady decreases in the size of cascades with increasingly more convincing corrective information. Finally, we compare simulated cascades on this framework with real cascades of disinformation recorded on Whatsapp surrounding the 2019 Indian election. We find a set of hyperparameter values that produces a distribution of cascades matching the scaling exponent of the distribution of actual cascades recorded in the dataset. We acknowledge the available future directions for improving the performance of the framework and validation methods, as well as ways to extend the model to capture additional features of social contagion.

42 ENGINEERING↗
NASA NTRS1985

Beam forming network

The network, which is connected to a layer of 134 feed elements that transmit and receive microwaves, consists of a pair of circuit boards parallel to the feed element layer. One of the two boards has 87 dividers that each divide a signal to be transmitted into seven portions, and the other board has 134 combiners that each collect seven transmit signal portions and deliver the sum to one of the feed elements. A similar arrangement is used to handle received signals. The large number of interconnections are made by printed circuit conductors radiating from each of the numerous dividers and combiners, and by providing interconnection pins that interconnect the ends of pairs of conductors lying on the two boards. The printed circuit conductors extend in undulating paths that provide maximum separation of conductors to minimize crosstalk.

Cramer, P. W., Jr.↗
NASA NTRS1990

Overview of the 27-28 October FIRE Cirrus IFO case: Meteorology and cloud fields

A detailed description of the meteorology of the 27 and 28 October FIRE Cirrus IFO case is presented. The progression of synoptic scale events over the region is reviewed including the evolution of surface and upper air features, i.e., the surface cyclone that formed to the west over Montana on the 27th and moved through Wisconsin late on the 28th and the associated short wave ridge aloft. A summary of the corresponding cloud conditions observed over the area of aircraft operations is given including characterization of the cloud forms and cloud heights based on information derived from debriefings of the aircrews, surface observations, airborne (ER-2) and surface lidars observations and satellite imagery (GOES). The correspondence between the resolved meteorological features (subsynoptic rawinsonde network) and the development of cloud layers is illustrated. In particular, the discussion emphasizes the relationship between vertical thermodynamic structure and the character and vertical structure of the cloud fields. The role of the larger scale in maintaining the observed vertical structure is quantified both with respect to its control via forced ascent and advective processes. Based on results of these analyses, conclusions are drawn with respect to the design of future field missions.

Starr, David OC.↗
NASA NTRS1990

Advanced software integration: The case for ITV facilities

The array of technologies and methodologies involved in the development and integration of avionics software has moved almost as rapidly as computer technology itself. Future avionics systems involve major advances and risks in the following areas: (1) Complexity; (2) Connectivity; (3) Security; (4) Duration; and (5) Software engineering. From an architectural standpoint, the systems will be much more distributed, involve session-based user interfaces, and have the layered architectures typified in the layers of abstraction concepts popular in networking. Typified in the NASA Space Station Freedom will be the highly distributed nature of software development itself. Systems composed of independent components developed in parallel must be bound by rigid standards and interfaces, the clean requirements and specifications. Avionics software provides a challenge in that it can not be flight tested until the first time it literally flies. It is the binding of requirements for such an integration environment into the advances and risks of future avionics systems that form the basis of the presented concept and the basic Integration, Test, and Verification concept within the development and integration life cycle of Space Station Mission and Avionics systems.

Garman, John R.↗
NASA NTRS2004

Eastern Sahara Geology from Orbital Radar: Potential Analog to Mars

Much of the surface of Mars has been intensely reworked by aeolian processes and key evidence about the history of the Martian environment seems to be hidden beneath a widespread layer of debris (paleo lakes and rivers, faults, impact craters). In the same way, the recent geological and hydrological history of the eastern Sahara is still mainly hidden under large regions of wind-blown sand which represent a possible terrestrial analog to Mars. The subsurface geology there is generally invisible to optical remote sensing techniques, but radar images obtained from the Shuttle Imaging Radar (SIR) missions were able to penetrate the superficial sand layer to reveal parts of paleohydrological networks in southern Egypt.

Farr, T. G.↗
NASA NTRS1999

Loss tolerant speech decoder for telecommunications

A method and device for extrapolating past signal-history data for insertion into missing data segments in order to conceal digital speech frame errors. The extrapolation method uses past-signal history that is stored in a buffer. The method is implemented with a device that utilizes a finite-impulse response (FIR) multi-layer feed-forward artificial neural network that is trained by back-propagation for one-step extrapolation of speech compression algorithm (SCA) parameters. Once a speech connection has been established, the speech compression algorithm device begins sending encoded speech frames. As the speech frames are received, they are decoded and converted back into speech signal voltages. During the normal decoding process, pre-processing of the required SCA parameters will occur and the results stored in the past-history buffer. If a speech frame is detected to be lost or in error, then extrapolation modules are executed and replacement SCA parameters are generated and sent as the parameters required by the SCA. In this way, the information transfer to the SCA is transparent, and the SCA processing continues as usual. The listener will not normally notice that a speech frame has been lost because of the smooth transition between the last-received, lost, and next-received speech frames.

Prieto, Jr., Jaime L.↗