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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Autonomous System for Monitoring the Integrity of Composite Fan Housings

A low-cost and reliable system assesses the integrity of composite fan-containment structures. The system utilizes a network of miniature sensors integrated with the structure to scan the entire structural area for any impact events and resulting structural damage, and to monitor degradation due to usage. This system can be used to monitor all types of composite structures on aircraft and spacecraft, as well as automatically monitor in real time the location and extent of damage in the containment structures. This diagnostic information is passed to prognostic modeling that is being developed to utilize the information and provide input on the residual strength of the structure, and maintain a history of structural degradation during usage. The structural health-monitoring system would consist of three major components: (1) sensors and a sensor network, which is permanently bonded onto the structure being monitored; (2) integrated hardware; and (3) software to monitor in-situ the health condition of in-service structures.

Qing, Xinlin P.↗
DOE OSTI2021

Moving beyond the constraints of chemistry via crystal structure discovery with isotropic multiwell pair potentials

The rigid constraints of chemistry-dictated by quantum mechanics and the discrete nature of the atom-limit the set of observable atomic crystal structures. What structures are possible in the absence of these constraints? Here, we systematically crystallize one-component systems of particles interacting with isotropic multiwell pair potentials. We investigate two tunable families of pairwise interaction potentials. Our simulations self-assemble a multitude of crystal structures ranging from basic lattices to complex networks. Sixteen of the structures have natural analogs spanning all coordination numbers found in inorganic chemistry. Fifteen more are hitherto unknown and occupy the space between covalent and metallic coordination environments. The discovered crystal structures constitute targets for self-assembly and expand our understanding of what a crystal structure can look like.

crystal structures↗
DOE OSTI2026

Doping Metallic Grain Boundaries to Control Atomic Structure and Damage Tolerance

Grain boundaries often act as sites for crack and void nucleation during plastic deformation of metallic materials. While it is known that grain boundary character and structure can greatly influence this damage nucleation process, the current level of control over such details is limited. The objective of this project was to obtain a fundamental understanding of how metallic grain boundary structure can be controlled through intelligent doping, with the idea of inducing planned amorphous grain boundary phases or complexions. The effect of amorphous complexion structure on dislocation accommodation mechanisms was studied, to improve the field’s understanding of damage nucleation at a promising type of interface. Different microstructural descriptors were studied, as grain boundaries can have large variations and complexity of local structure, and influence the mechanical damage resistance of these features was tested. Nanocrystalline systems that contain amorphous grain boundary complexions were prioritized, as these have more extreme variations in interfacial structure and damage tolerance, with an emphasis on isolating the importance of complexion population (type, thickness, etc.), network topology, local structure, and local chemistry. This research used a combination of computational, experimental, and characterization techniques to isolate and understand the importance of nanoscale grain boundary structure and interfacial chemistry. Amorphous grain boundary complexions were found to clearly increase a material’s resistance to mechanical damage, with the local distribution of structural short-range order within the complexions found to be an important descriptor for damage and the proposed focus of future work in this area.

36 MATERIALS SCIENCE↗
NASA NTRS2017

The Effects of Spaceflight on Neurocognitive Performance: Extent, Longevity, and Neural Bases

Spaceflight effects on gait, balance, & manual motor control have been well studied; some evidence for cognitive deficits. Rodent cortical motor & sensory systems show neural structural alterations with spaceflight. We found extensive changes in behavior, brain structure & brain function following 70 days of HDBR. Specific Aim: Aim 1-Identify changes in brain structure, function, and network integrity as a function of spaceflight and characterize their time course. Aim 2-Specify relationships between structural and functional brain changes and performance and characterize their time course.

Seidler, Rachael D.↗
DOE OSTI2023

The hidden structure of hydrodynamic transport in random fracture networks

We study the large-scale dynamics and prediction of hydrodynamic transport in random fracture networks. The flow and transport behaviour is characterized by first passage times and displacement statistics, which show heavy tails and anomalous dispersion with a strong dependence on the injection condition. The origin of these behaviours is investigated in terms of Lagrangian velocities sampled equidistantly along particle trajectories, unlike classical sampling strategies at a constant rate. The velocity series are analysed by their copula density, the joint distribution of the velocity unit scores, which reveals a simple, albeit hidden, correlation structure that can be described by a Gaussian copula. Based on this insight, we derive a Langevin equation for the evolution of equidistant particle speeds. In this framework, particle motion is quantified by a stochastic time-domain random walk, the joint density of particle position, and speed satisfies a Klein–Kramers equation. The upscaled theory quantifies particle motion in terms of the characteristic fracture length scale and the distribution of Eulerian flow velocities. That is, it is predictive in the sense that it does not require the a priori knowledge of transport attributes. The upscaled model captures non-Fickian transport features, and their dependence on the injection conditions in terms of the velocity point statistics and average fracture length. It shows that the first passage times and displacement moments are dominated by extremes occurring at the first step. The presented approach integrates the interaction of flow and structure into a predictive model for large-scale transport in random fracture networks.

42 ENGINEERING↗
DOE OSTI2026

NN-OpInf

SAND2026-18878O The NN-OpInf tool is a PyTorch-based approach to operator inference that uses composable, structure-preserving neural networks to represent nonlinear operators. Operator inference is a machine learning method for inferring low-dimensional systems from data and polynomial models for system dynamics. However, many systems do not conform to polynomial structures, which NN-OpInf addresses by parameterizing operators with neural networks. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
DOE OSTI2021

Insight into the nanostructure of “water in salt” solutions: A SAXS/WAXS study on imide-based lithium salts aqueous solutions

"Water-in-salt " electrolyte (WISE) series have been studied for lithium-ion batteries and supercapacitor applications, but so far, most of the focus has been on the LiTFSI salt-based systems. Herein, we used small-angle X-ray scattering/wide-angle X-ray scattering (SAXS/WAXS) and molecular dynamics (MD) simulation to investigate the solvation structure of a series of lithium salts with four different symmetric anions: (bis(fluoro sulfonyl)imide (FSI), bis(trifluoromethane sulfonyl)imide (TFSI), bis(pentafluoroethane sulfonyl) imide (BETI) and bis(nonafluorobutane sulfonyl)imide (BNTI)), which have similar parent structures but different lengths of the fluorocarbon chains. Two competing structures were found in the four lithium salts aqueous solutions: anion solvated structure and anion network. The anion network plays a crucial role in obtaining a stable and enlarged electrochemical window. The d spacing of the anion solvated structure follows a linear correlation with the number of carbons in the fluorocarbon chains and an exponential correlation with the concentrations. Further, we also observed that the transition from one structure to another is predominantly controlled by the salt volume fraction for all imide-based aqueous solutions. This work provides a systematic study of the atomistic scale structures of a series of imide-based lithium salt aqueous solutions that could extend the knowledge of WIS electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2024

Generative $β$-hairpin design using a residue-based physicochemical property landscape

De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the -hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach based on physicochemical and conformational properties of 20 canonical amino acids, for example, hydrophobicity and residue volume, using extant structure-specific sequence data from the PDB. The beta generative adversarial network model robustly distinguishes secondary structures of hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial -hairpin peptide sequences with minimum sequence identities around 31% and 50% when compared against the current NCBI PDB and nonredundant databases, respectively. These results highlight the potential of generative models specifically anchored by physicochemical and conformational property features of amino acids to expand the sequence-to-structure landscape of proteins beyond evolutionary limits.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1991

An advanced OBP-based payload operating in an asynchronous network for future data relay satellites utilising CCSDS-standard data structures

A possible Data Relay Satellite System (DRSS) topology and network architecture is introduced. An asynchronous network concept, whereby each link (Inter-orbit, Inter-satellite, Feeder) is allowed to operate on its own clock, without causing loss of information, in conjunction with packet data structures, such as those specified by the CCSDS for advanced orbiting systems is discussed. A matching OBP payload architecture is described, highlighting the advantages provided by the OBP-based concept and then giving some indications on the OBP mass/power requirements.

Grant, M.↗
NASA NTRS1993

A tri-state optical switch for local area network communications

This novel structure is a heterojunction phototransistor which can be used as an emitter-detector, and when placed in a quiescent mode, the device becomes a passive transmitter. By varying the voltage bias, this novel device will switch between all three modes of operation. Such a device has broad application in network environments with operation speeds of less than 50 MHz and distances of less than 1 km, e.g. automobiles, airplanes, and intra-instrumentation. During this period, the emission mode for this device was studied and mathematically modeled.

Simms, Garfield↗
NASA NTRS1991

Role of groundwater in formation of Martian channels

A global 3-D model of groundwater flow has been used to study possible behavior of groundwater on Mars and its role in creating fluvial features. Conclusions drawn from an earlier 2-D groundwater model are supplemented and expanded. Topical headings are discussed as follows: timescales of groundwater flow; wet areas on Mars and location of outflow channels; implications for valley networks; the enigma of Hellas; absence of fluvial or periglacial features on Syrtis Major; development of chaotic terrain and associated outflow channels; and structurally controlled valley networks.

Howard, Alan D.↗
NASA NTRS2017

The Effects of Spaceflight and Head Down Tilt Bed Rest on Neurocognitive Performance: Extent, Longevity, and Neural Bases

Spaceflight effects on gait, balance, & manual motor control have been well studied; some evidence for cognitive deficits. Rodent cortical motor & sensory systems show neural structural alterations with spaceflight. specific Aims: Aim 1-Identify changes in brain structure, function, and network integrity as a function of head down tilt bed rest and spaceflight, and characterize their time course. Aim 2-Specify relationships between structural and functional brain changes and performance and characterize their time course.

Seidler, Rachael D.↗
DOE OSTI2025

Structure and Dynamics of CO 2 at the Air–Water Interface from Classical and Neural Network Potentials

The accurate description of the structure and dynamics of CO 2 at the instantaneous air–water interface, along with the effects of surface fluctuations on the CO 2 -transport processes, is essential for the development of negative emission technologies aimed at minimizing climate change. In this study, we performed molecular dynamics simulations of CO 2 at the air–water interface using neural network potentials (NNPs) trained on ab initio data generated through density-functional-theory-based molecular dynamics simulations. We compared these results with classical force fields to assess their performance in modeling interfacial CO 2 behavior. Our findings revealed that the asymmetric interactions, coupled with thermal surface fluctuations at the air–water interface, significantly influence CO 2 transport into the aqueous phase. The simulations demonstrate that classical force fields underestimate both the free energy of CO 2 transport and the strength of its interactions at the interface compared with the neural network potentials. In conclusion, the free energy and the interfacial dynamics of CO 2 are primarily influenced by the distribution of water within the instantaneous interfacial water layer, responsible for creating an asymmetric intermolecular interaction environment within the interfacial region.

Ab initio molecular dynamics↗
DOE OSTI2022

Spatiotemporal Graph Convolutional Networks for Earthquake Source Characterization

Abstract Accurate earthquake location and magnitude estimation play critical roles in seismology. Recent deep learning frameworks have produced encouraging results on various seismological tasks (e.g., earthquake detection, phase picking, seismic classification, and earthquake early warning). Many existing machine learning earthquake location methods utilize waveform information from a single station. However, multiple stations contain more complete information for earthquake source characterization. Inspired by recent successes in applying graph neural networks (GNNs) in graph‐structured data, we develop a Spatiotemporal Graph Neural Network (STGNN) for estimating earthquake locations and magnitudes. Our graph neural network leverages geographical and waveform information from multiple stations to construct graphs automatically and dynamically by adaptive message passing based on graphs' edges. Using a recent graph neural network and a fully convolutional neural network as baselines, we apply STGNN to earthquakes recorded by the Southern California Seismic Network from 2000 to 2019 and earthquakes collected in Oklahoma from 2014 to 2015. STGNN yields more accurate earthquake locations than those obtained by the baseline models and performs comparably in terms of depth and magnitude prediction, though the ability to predict depth and magnitude remains weak for all tested models. Our work demonstrates the potential of using GNNs and multiple stations for better automatic estimation of earthquake epicenters.

58 GEOSCIENCES↗
DOE OSTI2020

CSB-RNN: A Faster-Than-Realtime RNN Acceleration Framework with Compressed Structured Blocks

Recurrent Neural Networks (RNN) is widely applied to temporal sequence analysis, where real-time performance is usually in demand. However, RNN suffers a heavy computational workload as the model comes with a large weight matrix. To alleviate the pain, model compression (pruning) schemes have been proposed for RNN that pruning the redundant (near-zero) weight-values. On the one hand, the non-structured pruning methods achieve a considerable pruning rate while bringing the computational irregularity, which is un-friendly to parallel-hardware. On the other hand, the existing structured pruning methods consider the hardware parallelism; However, they suffer a poor pruning rate due to the restrict constraints on pruning structure. This paper presents CSB-RNN, an optimized full-stack RNN framework with the novel compressed structured block (CSB) technique. The CSB-pruned RNN model comes with both fine-granularity that benefits the pruning rate and regular structure that facilitates the hardware-parallelism. Further, we propose a novel hardware architecture for inferencing the CSB-pruned model. Different from conventional parallel hardware, this architecture solves the block-workload imbalance issue and achieves an over 95% hardware utilization. With the experiments on 10 RNN models in 5 application domains, the CSB-RNN realizes 7×-20× lossless compression and up to 50× acceptable lossy-compression, which is 2×-7× to the prior art. With the addition of the novel hardware, the compressed-RNN inference reaches a super real-time latency of 10-400µs with FPGA implementation.

Shi, Runbin↗
DOE OSTI2024

Moiré Patterns in Pt Overlayers on Gold: A Graph Neural Network Interatomic Potential Study

Overlayer structures in bimetallic catalysts are relevant to a variety of catalytic reactions, particularly in electrocatalysis for fuel cell applications. Previous computational studies largely consider these overlayer structures to be those of a pseudomorphic overlayer, where there is a 1:1 atomic ratio between the overlayer and the support metal. Our previous work based on density functional theory (DFT) has shown that there exist nonstoichiometric overlayer structures that are more stable than the stoichiometric ones. Here, in this work, we developed a graph neural network interatomic potential (GNN-IP) to analyze structures formed in Pt overlayers on Au(111). The GNN-IP was used to explore properties of nonstoichiometric overlayers at length scales that are prohibitively expensive to pursue with planewave DFT calculations. In particular, we examined large Pt islands on top of a 48 × 48 Au(111) unit cell to explore the influence of the rotational angle (α) between the Pt overlayer and support Au(111) on both the stability of and the preferred atomic density in the overlayer. Island structures with smaller rotational angles between the Pt overlayer and Au(111) tend to be more stable. Further, smaller rotational angles tend to result in lower atomic density in the Pt overlayer.

chemical structure↗
DOE OSTI2025

Electroreduction-Driven Formation and Connectivity of Polyoxometalate Coordination Networks

We present the synthesis of metal oxide coordination networks based on Preyssler-type polyoxoanions ([NaP 5 W 30 O 110 ] 14– and [NaP 5 MoW 29 O 110 ] 14– ) bridged with metal–aquo complexes ([M(H 2 O) n ] m+ , M m+ = Co 2+ , Ni 2+ , Zn 2+ , Y 3+ ), induced by electrochemical reduction. Networks bridged with first-row transition metals are isostructural with a previously reported Co-bridged structure, while the Y 3+ -bridged structure is new. All networks feature an uncommon binding motif of the metal cation to the oxygen atoms at cap positions, which we hypothesize is due to increased electron density at the cap upon reduction. Oxidation of a Zn 2+ -bridged network resulted in a new structure in which Zn 2+ –O cap bonds are lost, indicating the importance of reduction in the connectivity of these polyoxometalate-based coordination networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗