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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
DOE OSTI2018

Uncovering anisotropic magnetic phases via fast dimensionality analysis

A quantitative geometric predictor for the dimensionality of magnetic interactions is presented. This predictor is based on networks of superexchange interactions and can be quickly calculated for crystalline compounds of arbitrary chemistry, occupancy, or symmetry. The resulting data are useful for classifying structural families of magnetic compounds. We have examined compounds from a demonstration set of 42,520 materials with 3d transition metal cations. The predictor reveals trends in magnetic interactions that are often not apparent from the space group of the compounds, such as triclinic or monoclinic compounds that are strongly 2D. We present speci c cases where the predictor identi es compounds that should exhibit competition between 1D and 2D interactions, and how the predictor can be used to identify sparsely-populated regions of chemical space with as-yet-unexplored topologies of speci c 3d magnetic cations. The predictor can be accessed for the full list of compounds using a searchable frontend, and further information on the connectivity, symmetry, valence, and cation-anion and cation-cation coordination can be freely exported.

36 MATERIALS SCIENCE↗
NASA NTRS1993

Adaptive nonlinear polynomial neural networks for control of boundary layer/structural interaction

The acoustic pressures developed in a boundary layer can interact with an aircraft panel to induce significant vibration in the panel. Such vibration is undesirable due to the aerodynamic drag and structure-borne cabin noises that result. The overall objective of this work is to develop effective and practical feedback control strategies for actively reducing this flow-induced structural vibration. This report describes the results of initial evaluations using polynomial, neural network-based, feedback control to reduce flow induced vibration in aircraft panels due to turbulent boundary layer/structural interaction. Computer simulations are used to develop and analyze feedback control strategies to reduce vibration in a beam as a first step. The key differences between this work and that going on elsewhere are as follows: that turbulent and transitional boundary layers represent broadband excitation and thus present a more complex stochastic control scenario than that of narrow band (e.g., laminar boundary layer) excitation; and secondly, that the proposed controller structures are adaptive nonlinear infinite impulse response (IIR) polynomial neural network, as opposed to the traditional adaptive linear finite impulse response (FIR) filters used in most studies to date. The controllers implemented in this study achieved vibration attenuation of 27 to 60 dB depending on the type of boundary layer established by laminar, turbulent, and intermittent laminar-to-turbulent transitional flows. Application of multi-input, multi-output, adaptive, nonlinear feedback control of vibration in aircraft panels based on polynomial neural networks appears to be feasible today. Plans are outlined for Phase 2 of this study, which will include extending the theoretical investigation conducted in Phase 2 and verifying the results in a series of laboratory experiments involving both bum and plate models.

Parker, B. Eugene, Jr.↗
DOE OSTI2023

FriendlyNets

FriendlyNets provides a method for assessing the promotion/inhibition effect on a microbe of a microbial community using a network of community interactions. At its core, FriendlyNets judges how much a network promotes or inhibits one of its nodes. It does this by assuming a set of dynamical systems represented by the network and using the resulting dynamics. FriendlyNets is also packaged with functions for generating a network from a set of genome-scale metabolic models (commonly called GSMs or GEMs) by simulating pairwise growth.

Brunner, James↗
NASA NTRS1998

Spinoff 1998

In 1958, a Congressional Mandate directed the National Aeronautics and Space Agency to ensure for the widest possible dissemination of its research and development results. Thus, the Scientific and Technical Information (STI) Program was born. While this program addressed mostly the timely dissemination of information to NASA, NASA contractors, other government agencies, and the public, technologies were identified that were clearly transferable and applicable to industry for additional use in the development of commercial products and services. Such considerations spun off the Technology Utilization Program. The very successful program went through several name changes and is today called the NASA Commercial Technology Program. The changes that have occurred over time are not only name changes, but program changes that have dramatically altered the philosophy, mission, and goal of the program. It has been identified that a more intense and proactive outreach effort within the program is necessary in order to make the newest and latest technologies available to industry now-at the time the technology is actually developed. The NASA Commercial Technology Network (NCTN), its interaction with industry at all levels through a large network of organizations and offices, is contributing to the success of small, medium, and large U.S. businesses to remain globally competitive. At the same time, new products and services derived from the transfer and application of NASA technology benefit everyone. This publication includes the following: Aerospace research and development - NASA headquarters and centers. Technology transfer and commercialization. Commercial benefits - spinoffs.NASA success and education. NASA commercial technology network.

Source record↗
DOE OSTI2022

Three-state majority-vote model on small-world networks

Abstract In this work, we study the opinion dynamics of the three-state majority-vote model on small-world networks of social interactions. In the majority-vote dynamics, an individual adopts the opinion of the majority of its neighbors with probability 1- q , and a different opinion with chance q , where q stands for the noise parameter. The noise q acts as a social temperature, inducing dissent among individual opinions. With probability p , we rewire the connections of the two-dimensional square lattice network, allowing long-range interactions in the society, thus yielding the small-world property present in many different real-world systems. We investigate the degree distribution, average clustering coefficient and average shortest path length to characterize the topology of the rewired networks of social interactions. By employing Monte Carlo simulations, we investigate the second-order phase transition of the three-state majority-vote dynamics, and obtain the critical noise $$q_c$$ q c , as well as the standard critical exponents $$\beta /\nu$$ β / ν , $$\gamma /\nu$$ γ / ν , and $$1/\nu$$ 1 / ν for several values of the rewiring probability p . We conclude that the rewiring of the lattice enhances the social order in the system and drives the model to different universality classes from that of the three-state majority-vote model in two-dimensional square lattices.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Catch bond kinetics are instrumental to cohesion of fire ant rafts under load

Dynamic networks composed of constituents that break and reform bonds reversibly are ubiquitous in nature owing to their modular architectures that enable functions like energy dissipation, self-healing, and even activity. While bond breaking depends only on the current configuration of attachment in these networks, reattachment depends also on the proximity of constituents. Therefore, dynamic networks composed of macroscale constituents (not benefited by the secondary interactions cohering analogous networks composed of molecular-scale constituents) must rely on primary bonds for cohesion and self-repair. Toward understanding how such macroscale networks might adaptively achieve this, we explore the uniaxial tensile response of 2D rafts composed of interlinked fire ants ( S. invicta ). Through experiments and discrete numerical modeling, we find that ant rafts adaptively stabilize their bonded ant-to-ant interactions in response to tensile strains, indicating catch bond dynamics. Consequently, low-strain rates that should theoretically induce creep mechanics of these rafts instead induce elastic-like response. Our results suggest that this force-stabilization delays dissolution of the rafts and improves toughness. Nevertheless, above 35 % strain low cohesion and stress localization cause nucleation and growth of voids whose coalescence patterns result from force-stabilization. These voids mitigate structural repair until initial raft densities are restored and ants can reconnect across defects. However mechanical recovery of ant rafts during cyclic loading suggests that—even upon reinstatement of initial densities—ants exhibit slower repair kinetics if they were recently loaded at faster strain rates. These results exemplify fire ants’ status as active agents capable of memory-driven, stimuli-response for potential inspiration of adaptive structural materials.

Science & Technology - Other Topics↗
DOE OSTI2018

Bioprinting microbial communities to examine interspecies interactions in time and space

Bioprinting experiments have emerged as an approach for studying complex cellular interactions in the context of spatial structure, ranging from intracellular networks to interspecies interactions in communities. Despite the progress made in developing and optimizing computational modeling parameters to recapitulate these interactions in silico, experimental verification of model predictions in vitro is often elusive, due to the limits of common laboratory culturing methods, especially at the micro- and nanoscale. In this work, micro-scale bioprinting was used to evaluate the spatiotemporal effects of metabolite sharing between partners in an artificial syntrophic consortium consisting of Salmonella enterica serovar Typhimurium and Escherichia coli. Micro-colonies of these bacteria were patterned at increasing spatial separation distances, in the presence of competitor or cooperator strains. Growth of the consortium members was evaluated and compared to predictions generated by a bacterial growth modeling platform known as Computation of Microbial Ecosystems in Time and Space (COMETS). In close agreement with simulation, experimental metabolite sharing between micro-colonies exhibited strong distance-dependency. When bioprinted micro-colonies were confronted with one-way or two-way diffusion barriers, such as the intervention of cooperative or competitive bacteria, experimental results showed that spatial positioning of the barriers critically influences metabolite sharing between consortium members. These results were generally predicted by simulation in COMETS. Our data demonstrate the utility of micro-scale bioprinting to experimentally test which aspects of predictive microbial growth models hold at this spatial scale, and for a range of synthetic biology applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

hippynn Python Package

hippynn is a python package for defining, training, and applying neural networks to atomistic systems. In particular, it focuses on Hierarchical Interacting Particle Neural Networks (HIP-NNs), a deep learning architecture for atomistic systems. HIP-NNs take input data describing the properties of atomistic systems (often generated using ab-initio quantum mechanics) to learn fast and accurate models. A trained HIP-NN can predict potential energy surfaces, atomic charges, and more. hippynn uses PyTorch for portable high-performance code, including both CPU and GPU support. hippynn allows for extensive customization, including user-defined models and loss functions, to facilitate future research into extensions of HIP-NN as well as other atomistic deep learning models.

Lubbers, Nicolas↗
DOE OSTI2025

Development and transferability of neural-network models for plasma-surface interactions

Plasma-surface interactions are increasingly critical to modern technologies; yet, accurate molecular dynamics simulations remain limited by the capabilities of interatomic potentials. Deep Potentials (DPs) promise to revolutionize the field by providing a systematic method for producing accurate interatomic potentials. The primary challenge of DP development is selecting a dataset, which efficiently spans the set of atomic environments one expects to encounter in the subsequent molecular dynamics simulations. The computational cost of density functional theory calculations, which are the typical basis for DP development, makes it impossible to directly verify the quality of a given DP. To address this challenge, we explore the development of a deep-learned interatomic potential, “DeepREBO,” trained to reproduce the behavior of the REBO2 empirical potential, enabling direct validation of training methodology and transferability. Using an active learning framework, we begin with a minimal dataset and iteratively expand it to train a Deep Potential-Smooth Edition model that faithfully reproduces REBO2 results for 25 eV hydrogen bombardment of diamond (001), a particularly challenging case. We show that small, carefully curated datasets can outperform large, unguided ones, with effective models requiring fewer than 15 000 snapshots. Subsequent transferability tests demonstrate that while DeepREBO generalizes well to diamond (111) surfaces, performance degrades for amorphous carbon or higher-energy impacts, highlighting the need for use-case-specific training data. We also evaluate methods to improve short-range repulsion. This study outlines best practices for training robust deep potentials and underscores the importance of dataset design for predictive plasma simulations.

Ab-initio molecular dynamics↗
DOE OSTI2016

A Game-Theoretic Approach to Modeling Attacks and Defenses of Smart Grids at Three Levels

As optimization, user capabilities, and data-taking abilities are incorporated into next-generation power grids, or smart grids, they face cyber threats. The traditional electrical grid could only be damaged by physical attacks; however thesmart grid can suffer remote/cyber attacks, which have not been studied extensively in the literature. The electrical grid forms the backbone of the modern society and its security has significant implications in military settings. This paper applies game theory to model three-levels (power plants, transmission, and distribution networks) of defenses and attacks in smart grid network security. We characterize both the attacker and the defender (who interact at three network levels: distribution, transmission, and power plants) best responses and equilibrium strategies. We find that the defender's best response is not only a function of direct attacks but also of the spread from connected networks. Sensitivity analyses of the equilibrium strategies show that when success probability of an attack against power plantsreaches a threshold, the defender increases defending efforts for power plants. In contrast, the attack eort at all levels is not affected by this probability. This paper provides some novel insights to modeling and analyzing the emerging threats to the growing smart grid networks.

Rao, Nageswara S.↗
DOE OSTI2020

Mutualism increases diversity, stability, and function of multiplex networks that integrate pollinators into food webs

Ecosystems are composed of complex networks of many species interacting in different ways. While ecologists have long studied food webs of feeding interactions, recent studies increasingly focus on mutualistic networks including plants that exchange food for reproductive services provided by animals such as pollinators. Here, we synthesize both types of consumer-resource interactions to better understand the controversial effects of mutualism on ecosystems at the species, guild, and whole-community levels. We find that consumer-resource mechanisms underlying plant-pollinator mutualisms can increase persistence, productivity, abundance, and temporal stability of both mutualists and non-mutualists in food webs. These effects strongly increase with floral reward productivity and are qualitatively robust to variation in the prevalence of mutualism and pollinators feeding upon resources in addition to rewards. This work advances the ability of mechanistic network theory to synthesize different types of interactions and illustrates how mutualism can enhance the diversity, stability, and function of complex ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Resonant interaction between phonons and PbTe/PbSe (001) misfit dislocation networks

Here this work aims at a quantitative and mechanistic understanding of the dynamic process of the phonon-dislocation interaction in PbTe/PbSe (001) heterostructures using the Concurrent Atomistic-Continuum (CAC) method as the simulation tool. The misfit dislocation network and the atomic-scale dislocation core structure obtained in the simulations are found to agree reasonably well with the experimental observations of the PbTe/PbSe (001) interface. Through visualizing the dynamic interaction between phonons and dislocations, as well as quantifying the dislocation vibration amplitude, the phonon energy transmission, and the thermal resistance of the misfit interfaces, this work has illustrated and quantified two mechanisms for phonon-dislocation interaction: (1) phonon scattering by the strain field of dislocations, and (2) phonon scattering by dislocations that vibrate via the local modes of a dislocation network; the latter, leads to resonant phonon-dislocation interaction, which is manifested as local maxima of out-of-phase vibration of the atoms on the two sides of the slip plane, leading to local minima of the energy transmission in the heterostructure that contains one interface. The local vibrational modes are found to be excited only by shear stress induced by transverse phonons. Among various resonant modes, the one with the lowest frequency has the strongest effect. This work has also demonstrated the collective motion of dislocations under ultrafast phonon pulses. In addition, the dynamic properties of the misfit dislocation network localized within one interface are found to be significantly altered by the presence of misfit dislocations at other interfaces, thus further confirming the cooperative dynamic nature of the motion of dislocations and phonons.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Reconstruction and Selection of Neutrino Interactions in MicroBooNE using Deep Convolutional Neural Networks

In this document, we describe a new reconstruction workflow developed for the MicroBooNE experiment. It features the use of Deep Convolutional Neural Networks trained to recognize key structures within the data sufficient for the 3D reconstruction of neutrino interactions within the detector. As a test of the reconstruction utility, the products of the reconstruction workflow are used to select inclusive charged-current (CC) $\nu_e$ and $\nu_\mu$ interactions in both simulated and real MicroBooNE data. In simulation, our $\nu_e$ and $\nu_\mu$ selections achieve an efficiency of 57% and 68\%, respectively, with a purity of 91% and 96%, respectively. We find that these selections are competitive with the inclusive selections used for the most recent MicroBooNE LEE searches. In particular, the CC-$\nu_e$ inclusive selection efficiency improves by over 20% while also improving sample purity. As a first step in quantifying potential bias, the data and Monte Carlo expectati ons are compared for both selections using the MicroBooNE open data. Within statistical and systematic uncertainties, both the electron and muon CC-inclusive event samples agree. A comparison of the real data events chosen by our work and another reconstruction framework shows that the two analyses each identify a sizeable fraction of events the other does not. This suggests that future analyses integrating the strengths of each could lead to combined gains. This work demonstrates, for the first time on real LArTPC data, state-of-the-art neutrino interaction reconstruction centered around deep learning algorithms.

43 PARTICLE ACCELERATORS↗
DOE OSTI2013

Four homochiral coordination polymers contain N-acetyl-L-tyrosine and different N-donor ligand: Influence of metal cations, ancillary ligands and coordination modes

Using the chiral ligand N-acetyl-L-tyrosine (Hacty) and maintaining identical reaction conditions, Zn(II), Co(II), and Cd(II) salts provided four novel homochiral coordination polymers ([Zn(acty)(bipy){sub 2}(H{sub 2}O){sub 2}]·NO{sub 3}·2H{sub 2}O){sub n}1, ([Co(acty)(bipy){sub 2}(H{sub 2}O){sub 2}]·NO{sub 3}·2H{sub 2}O){sub n}2, ([Cd(acty){sub 2}(bipy)H{sub 2}O]·H{sub 2}O){sub n}3, and ([Cd(acty)(bpe){sub 2}(Ac)]·6H{sub 2}O){sub n}4 (bipy=4,4′-bipyridine; bpe=1,2-di(4-pyridyl)ethane) in the presence of ancillary ligands. Compounds 1 and 2 are isostructural 1D chain structures. The neighboring chains are further linked into a 3D supramolecular structure via π⋯π stacking and hydrogen bond interactions. Compound 3 shows a 2D network and 4 generates 1D infinite chains along the c-axis. Compounds 3 and 4 are further connected into 3D supramolecular network by hydrogen bond interactions. More importantly, coordination in acyl oxygen atoms and ancillary ligands (bpe) as monodentate decorating ligands in 4 are rarely reported. Ancillary ligands and metal cations significantly influence the structure of the complexes. The photoluminescence properties of 1, 3, and 4 were studied at room temperature. Circular dichroism (CD) of the complexes have been investigated. - Graphical abstract: Four new homochiral coordination polymers were prepared and structurally characterized, which investigate the influence of the ancillary ligands and metal ions on the design and synthesis of coordination polymers. Display Omitted - Highlights: • It is rarely reported that the chiral coordination polymers prepared with N-acetyl-L-tyrosine ligands. • The alkalescent acetyl oxygen atom is difficult to participate in coordination but it is happened in the N-acetyl-L-tyrosine ligands. • The ancillary ligands (4,4′-bipy and bpe) are present in an unusual coordination modes, monodentate decorating ligands in 1, 2 and 4. • Structure comparative analyses results indicate that the secondary ligands and metal ions influence the fabrication of these inorganic–organic arrangements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

NLML: A Deep Neural Network Emulator for the Exact Nonlinear Interactions in a Wind Wave Model

Nonlinear wave interactions describe the resonant energy transfer between wave components, playing a fundamental role in the evolution of ocean wave spectra. Nonlinear wave interactions significantly influence wave growth and development, making them essential for accurate wave modeling. However, resolving the full six-dimensional Boltzmann integral of the exact nonlinear wave interactions (Webb-Resio-Tracy method, WRT) is computationally expensive, limiting its application in real-time operational wave forecasting and for research purposes. Current approximations, such as the Discrete Interaction Approximation (DIA), prioritize computational speed over accuracy, resulting in significant errors in wave mean parameters. Here, we introduce NLML, a machine learning (ML) emulator designed to approximate the exact nonlinear wave interactions within WAVEWATCH III (WW3), with the goal of achieving the accuracy of WRT while maintaining the stability and computational speed of DIA. By leveraging GPU capabilities such as half precision inference, we achieved substantial speedups, up to 136x mathematical equation faster than the WRT and only a modest 1.04x mathematical equation slowdown relative to DIA, while achieving 2x mathematical equation the accuracy of DIA in global wave spectral energy and mean wave parameters, with up to 7x mathematical equation higher accuracy in some regions. Unlike previous ML approaches, NLML maintained inherent stability throughout model integration in a standalone, year-long WW3 simulation, without requiring additional constraints. Our new ML parameterization bridges the gap between accuracy and efficiency, offering a promising alternative for improving wave modeling in operational settings and research purposes.

16 TIDAL AND WAVE POWER↗