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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Isomerization kinetics of azobenzene crosslinked to a liquid crystal polymer network

Azobenzene is an important chromophore having numerous applications that are a direct implication of its ability to change shape upon irradiation with a suitable wavelength. Here, in this study, photoisomerization kinetics of a diacrylate azobenzene molecule (A3MA) cross-linked to a liquid crystal polymer network (LCN) is investigated. Isomerization experiments were conducted on planar LCN thin films with varying cross-linking densities for a fixed azobenzene concentration. The experiments were conducted at approximately 26 °C, which is significantly below each film’s glass transition temperature. The forward trans-cis and reverse cis-trans isomerization slows down in crosslinked azobenzene liquid crystal polymer networks (ALCNs) as compared to a solution and an uncrosslinked polymer mixture. The observed slowdown is due to the reduction in the free volume and increased steric hindrances with an increase in crosslinking. A single-term first-order kinetics rate equation is known to describe the isomerization kinetics in solutions or uncrosslinked mixtures while the same form fails to represent the reaction kinetics in moderate to highly cross-linked ALCNs. Our results for the first time indicate that a two-term first-order reaction rate is an accurate way to describe the forward trans-cis and reverse thermal cis-trans isomerization in crosslinked azobenzene liquid crystal polymer networks. In addition, the effect of film thickness and incident intensities on the reaction kinetics is investigated. Finally, the two-term isomerization kinetics is incorporated into a finite element photo-chemo-mechanical model to reveal the consequent slowdown in the actuation response of ALCN films. The current findings will be of particular interest to theoreticians and experimentalists alike, for it will help predict and understand accurately the responses of azobenzene-based coatings, and actuators and may have potential applications in underwater soft robotics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1990

Assignment Of Finite Elements To Parallel Processors

Elements assigned approximately optimally to subdomains. Mapping algorithm based on simulated-annealing concept used to minimize approximate time required to perform finite-element computation on hypercube computer or other network of parallel data processors. Mapping algorithm needed when shape of domain complicated or otherwise not obvious what allocation of elements to subdomains minimizes cost of computation.

Salama, Moktar A.↗
NASA NTRS2003

Neural-net Processed Electronic Holography for Rotating Machines

This report presents the results of an R&D effort to apply neural-net processed electronic holography to NDE of rotors. Electronic holography was used to generate characteristic patterns or mode shapes of vibrating rotors and rotor components. Artificial neural networks were trained to identify damage-induced changes in the characteristic patterns. The development and optimization of a neural-net training method were the most significant contributions of this work, and the training method and its optimization are discussed in detail. A second positive result was the assembly and testing of a fiber-optic holocamera. A major disappointment was the inadequacy of the high-speed-holography hardware selected for this effort, but the use of scaled holograms to match the low effective resolution of an image intensifier was one interesting attempt to compensate. This report also discusses in some detail the physics and environmental requirements for rotor electronic holography. The major conclusions were that neural-net and electronic-holography inspections of stationary components in the laboratory and the field are quite practical and worthy of continuing development, but that electronic holography of moving rotors is still an expensive high-risk endeavor.

Decker, Arthur J.↗
DOE OSTI2020

Development of a Convolutional Neural Network Classifier for Data Starved Spectra - 20199

The Institute for Clean Energy Technology (ICET) at Mississippi State University is exploring the utility of machine learning in augmenting its mobile radiation surveying platforms, which are currently being developed as means to survey depleted uranium contaminated areas in support of remediation and decommissioning efforts. Mobile survey platforms provide a means to efficiently scan large areas of interest while reducing human exposure to radiation and other hazards. The survey platforms can also be used for scanning for any gamma emitting isotope in addition to depleted uranium. The spectral data that the platforms collect may be data starved with relatively low counts and poorly defined spectral features depending on the speed of the platforms and scintillation detector selection. Such data-starved spectra are difficult to use for isotope identification, requiring advanced knowledge of the possible radionuclides that could be present and environmental factors that could attenuate signals or introduce background noise. These factors in combination with the volume of survey data increases the time it takes to perform analysis of survey data when the source type is unknown. There are a number of algorithms in the field of machine learning that can be used to classify data that would be challenging and time-consuming for a human to identify. Supervised machine learning algorithms train models based on extensive amounts of human-labeled training data. Once sufficiently trained, these models can be used to quickly make high-fidelity predictions on new data. Convolutional neural networks are machine learning algorithms that excel in learning representations of 'shapes'. They do this by taking numerical input data and convolving them with spatial feature detectors referred to as filters. These filters are incrementally adjusted to reduce the prediction error on the data during the backpropagation step of training. Discussed in this paper is the development of a convolutional neural network classifier (CNNC) that can utilize spectral survey data for source discrimination and isotope identification. Bench-top laboratory experiments data using LaBr{sub 3}(Ce) scintillation detectors were used to train and evaluate the performance of the developed CNNC. The CNNC is capable of discriminating a variety of gamma emitting source types, differentiating different forms of uranium (depleted vs. natural), and estimating the amount of uranium for a known geometry. The discussed CNNC may be useful in scenarios where survey systems are deployed in situations where hazardous radioactive material maybe present, but the type is unknown. When used in remediation applications the CNNC can be used to screen-out false positives, helping reduce remediation costs. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

Nonlinear behavior of urban flood peaks in the U.S. Mid-Atlantic region

Urbanization, i.e., increasing urban development areas in a watershed, is well known as a major cause of increasing flood magnitudes. This study analyzes the observed flood peaks at 262 watersheds in the U.S. Mid-Atlantic region with varying levels of urban development and free from reservoir impacts. Our analysis reveals an interesting, V-shaped nonlinear behavior: flood peaks first decrease and then increase with increasing percentage of urban development area at the watershed scale (PDAW), with the shift occurring at a PDAW threshold of around 10%. Regression analyses suggest that the V-shaped pattern primarily results from complex interactions among climate conditions (e.g., storm-event rainfall) and landscape properties (e.g., elevation, distance to the coast). A neural network model was then developed to capture such interactions, satisfactorily reproducing the V-shaped pattern with an R-squared value of 0.58, RMSE of 6.72 mm/day, and NSE of 0.55. These findings highlight the need to account for nonlinear dynamics in flood prediction and management in the coastal environment.

flood peaks↗
NASA NTRS2005

Software-Reconfigurable Processors for Spacecraft

A report presents an overview of an architecture for a software-reconfigurable network data processor for a spacecraft engaged in scientific exploration. When executed on suitable electronic hardware, the software performs the functions of a physical layer (in effect, acts as a software radio in that it performs modulation, demodulation, pulse-shaping, error correction, coding, and decoding), a data-link layer, a network layer, a transport layer, and application-layer processing of scientific data. The software-reconfigurable network processor is undergoing development to enable rapid prototyping and rapid implementation of communication, navigation, and scientific signal-processing functions; to provide a long-lived communication infrastructure; and to provide greatly improved scientific-instrumentation and scientific-data-processing functions by enabling science-driven in-flight reconfiguration of computing resources devoted to these functions. This development is an extension of terrestrial radio and network developments (e.g., in the cellular-telephone industry) implemented in software running on such hardware as field-programmable gate arrays, digital signal processors, traditional digital circuits, and mixed-signal application-specific integrated circuits (ASICs).

Farrington, Allen↗
NASA NTRS1999

Neural-Net Processing of Characteristic Patterns From Electronic Holograms of Vibrating Blades

Finite-element-model-trained artificial neural networks can be used to process efficiently the characteristic patterns or mode shapes from electronic holograms of vibrating blades. The models used for routine design may not yet be sufficiently accurate for this application. This document discusses the creation of characteristic patterns; compares model generated and experimental characteristic patterns; and discusses the neural networks that transform the characteristic patterns into strain or damage information. The current potential to adapt electronic holography to spin rigs, wind tunnels and engines provides an incentive to have accurate finite element models lor training neural networks.

Decker, Arthur J.↗
DOE OSTI2023

Data-driven modeling and control of an X-ray bimorph adaptive mirror

Adaptive X-ray mirrors are being adopted on high-coherent-flux synchrotron and X-ray free-electron laser beamlines where dynamic phase control and aberration compensation are necessary to preserve wavefront quality from source to sample, yet challenging to achieve. Additional difficulties arise from the inability to continuously probe the wavefront in this context, which demands methods of control that require little to no feedback. In this work, a data-driven approach to the control of adaptive X-ray optics with piezo-bimorph actuators is demonstrated. This approach approximates the non-linear system dynamics with a discrete-time model using random mirror shapes and interferometric measurements as training data. For mirrors of this type, prior states and voltage inputs affect the shape-change trajectory, and therefore must be included in the model. Without the need for assumed physical models of the mirror's behavior, the generality of the neural network structure accommodates drift, creep and hysteresis, and enables a control algorithm that achieves shape control and stability below 2 nm RMS. Using a prototype mirror and ex situ metrology, it is shown that the accuracy of our trained model enables open-loop shape control across a diverse set of states and that the control algorithm achieves shape error magnitudes that fall within diffraction-limited performance.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Tuning the Double Gyroid Phase Window in Block Copolymers via Polymer Chain Conformation Near the Interface

Block copolymer network morphologies have been proven interesting for applications ranging from mechanical to transport and optical properties. The shape of the polymer chain and its ability to stretch across an interface can be used as handles to target these network morphologies. Here, we show that the double gyroid phase window is broadened when there is a flexible segment near the interface and narrowed when a more constrained segment is placed there by using a series of poly(styrene-b-peptoid) block copolymers in which the polypeptoid block chain conformation can be tuned to adopt either a helical or a coil conformation (NRpe 6 vs Npe 6 ). The double gyroid phase is accessed in both block copolymer series, while the phase boundaries are shifted toward larger polypeptoid volume fractions in the helix-forming PS–(NRpe 6 Nmey) series, due to the more compact helix segment (i.e., the helix segment has the same chain volume as its random coil counterpart but occupies less space). The space-filling difference between the helix and coil segment is further confirmed by the smaller domain spacing of PS–(NRpe 6 Nme 39 ) compared to PS–(Npe 6 Nme 36 ) (13.9 nm vs 14.3 nm) despite the former having a longer polypeptoid block. Furthermore, a broadened double gyroid phase window is accessed in the PS–(Npe 6 Nme y ) series that has a flexible coil segment near the interface. These results demonstrate the possibility of tuning the double gyroid phase in linear block copolymers by chain conformation near the interface alone, highlighting chain conformation as a versatile handle in block copolymer design.

36 MATERIALS SCIENCE↗
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 OSTI2021

Semi-supervised Bayesian Low-shot Learning

Deep neural networks (NNs) typically outperform traditional machine learning (ML) approaches for complicated, non-linear tasks. It is expected that deep learning (DL) should offer superior performance for the important non-proliferation task of predicting explosive device configuration based upon observed optical signature, a task which human experts struggle with. However, supervised machine learning is difficult to apply in this mission space because most recorded signatures are not associated with the corresponding device description, or “truth labels.” This is challenging for NNs, which traditionally require many samples for strong performance. Semi-supervised learning (SSL), low-shot learning (LSL), and uncertainty quantification (UQ) for NNs are emerging approaches that could bridge the mission gaps of few labels and rare samples of importance. NN explainability techniques are important in gaining insight into the inferential feature importance of such a complex model. In this work, SSL, LSL, and UQ are merged into a single framework, a significant technical hurdle not previously demonstrated. Exponential Average Adversarial Training (EAAT) and Pairwise Neural Networks (PNNs) are chosen as the SSL and LSL methods of choice. Permutation feature importance (PFI) for functional data is used to provide explainability via the Variable importance Explainable Elastic Shape Analysis (VEESA) pipeline. A variety of uncertainty quantification approaches are explored: Bayesian Neural Networks (BNNs), ensemble methods, concrete dropout, and evidential deep learning. Two final approaches, one utilizing ensemble methods and one utilizing evidential learning, are constructed and compared using a well-quantified synthetic 2D dataset along with the DIRSIG Megascene.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2000

Performance Evaluation of the NASA/KSC Transmission System

NASA-KSC currently uses three bridged 100-Mbps FDDI segments as its backbone for data traffic. The FDDI Transmission System (FTXS) connects the KSC industrial area, KSC launch complex 39 area, and the Cape Canaveral Air Force Station. The report presents a performance modeling study of the FTXS and the proposed ATM Transmission System (ATXS). The focus of the study is on performance of MPEG video transmission on these networks. Commercial modeling tools - the CACI Predictor and Comnet tools - were used. In addition, custom software tools were developed to characterize conversation pairs in Sniffer trace (capture) files to use as input to these tools. A baseline study of both non-launch and launch day data traffic on the FTXS is presented. MPEG-1 and MPEG-2 video traffic was characterized and the shaping of it evaluated. It is shown that the characteristics of a video stream has a direct effect on its performance in a network. It is also shown that shaping of video streams is necessary to prevent overflow losses and resulting poor video quality. The developed models can be used to predict when the existing FTXS will 'run out of room' and for optimizing the parameters of ATM links used for transmission of MPEG video. Future work with these models can provide useful input and validation to set-top box projects within the Advanced Networks Development group in NASA-KSC Development Engineering.

Christensen, Kenneth J.↗
NASA NTRS2013

Assessment of Ice Shape Roughness Using a Self-Orgainizing Map Approach

Self-organizing maps are neural-network techniques for representing noisy, multidimensional data aligned along a lower-dimensional and nonlinear manifold. For a large set of noisy data, each element of a finite set of codebook vectors is iteratively moved in the direction of the data closest to the winner codebook vector. Through successive iterations, the codebook vectors begin to align with the trends of the higher-dimensional data. Prior investigations of ice shapes have focused on using self-organizing maps to characterize mean ice forms. The Icing Research Branch has recently acquired a high resolution three dimensional scanner system capable of resolving ice shape surface roughness. A method is presented for the evaluation of surface roughness variations using high-resolution surface scans based on a self-organizing map representation of the mean ice shape. The new method is demonstrated for 1) an 18-in. NACA 23012 airfoil 2 AOA just after the initial ice coverage of the leading 5 of the suction surface of the airfoil, 2) a 21-in. NACA 0012 at 0AOA following coverage of the leading 10 of the airfoil surface, and 3) a cold-soaked 21-in.NACA 0012 airfoil without ice. The SOM method resulted in descriptions of the statistical coverage limits and a quantitative representation of early stages of ice roughness formation on the airfoils. Limitations of the SOM method are explored, and the uncertainty limits of the method are investigated using the non-iced NACA 0012 airfoil measurements.

Icing↗
DOE OSTI2024

Clustering Effects on the Structure of Ionomer Solutions: A Combined SANS and Simulations Study

Ionic assemblies, or clusters, determine the structure and dynamics of ionizable polymers and enable their many applications. Fundamental to attaining well-defined materials is controlling the balance between van der Waals interactions that govern the backbone behavior and the forces that drive the formation of ionic clusters. Here, using small angle neutron scattering and fully atomistic molecular dynamics simulations, the structure of a model ionomer, sulfonated polystyrene in toluene solutions, was investigated as the cluster cohesion was tweaked by the addition of ethanol. The static structure factor was measured by both techniques and correlated with the size of the ionic clusters as the polymer concentration was varied. The conjunction of SANS results and molecular insight from MD simulations enabled the determination of the structure in these inhomogeneous networks on multiple length scales. Additionally, we find that across the entire concentration range studied, a network driven by the formation of ionic clusters was formed, where the size of the clusters drives the inhomogeneity of these systems. Tweaking the ionic clusters through the addition of ethanol impacts the packing of the sulfonated groups, their shape, and their size distribution, which in turn, affects the structure of these networks.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Throughput Measurements and Profile Analysis of Cloud Networks

Cloud networks utilize virtual connections to connect virtual machines distributed across cloud sites. They are increasingly deployed due to flexible provisioning using software and cost-effectiveness in not requiring to build physical network infrastructure. However, their extensive virtualization makes it unclear how well the established practices of conventional networks translate to them. Here, we study throughput measurements over a Google Cloud network using a matching hardware emulated conventional network, which provide production and exploratory conditions, respectively. The measurements span connections representing local, cross-continental and around the Earth distances. We study the effects of parallel flows, congestion control algorithms and retransmissions on the network throughput profile expressed as a function of RTT. We compare the throughput profile of Google Cloud network with those of emulated network under various loss conditions, including those too disruptive or expensive in the former. Our analysis based on the concave-convex shape and utilization-concavity coefficients of throughput profiles indicates an overall agreement of performance between the two networks, thereby justifying the use of conventional network emulations to analyze cloud networks. In terms of practical use, our study establishes that BBR and BBRv2 alpha TCP achieve higher throughput compared to loss-based congestion control algorithms under most network configurations, especially, under losses at large RTT.

Phanekham, Derek [Southern Methodist Univ., Dallas↗
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 OSTI2022

Resolving the structure of phage–bacteria interactions in the context of natural diversity

Microbial communities are shaped by viral predators. Yet, resolving which viruses (phages) and bacteria are interacting is a major challenge in the context of natural levels of microbial diversity. Thus, fundamental features of how phage-bacteria interactions are structured and evolve in the wild remain poorly resolved. Here we use large-scale isolation of environmental marine Vibrio bacteria and their phages to obtain estimates of strain-level phage predator loads, and use all-by-all host range assays to discover how phage and host genomic diversity shape interactions. We show that lytic interactions in environmental interaction networks (as observed in agar overlay) are sparse—with phage predator loads being low for most bacterial strains, and phages being host-strain-specific. Paradoxically, we also find that although overlap in killing is generally rare between tailed phages, recombination is common. Together, these results suggest that recombination during cryptic co-infections is an important mode of phage evolution in microbial communities. In the development of phages for bioengineering and therapeutics it is important to consider that nucleic acids of introduced phages may spread into local phage populations through recombination, and that the likelihood of transfer is not predictable based on lytic host range.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Shear Thickening and Jamming of Dense Suspensions: The "Roll" of Friction.

Particle-based simulations of discontinuous shear thickening (DST) and shear jamming (SJ) suspensions are used to study the role of stress-activated constraints, with an emphasis on resistance to gearlike rolling. Rolling friction decreases the volume fraction required for DST and SJ, in quantitative agreement with real-life suspensions with adhesive surface chemistries and "rough" particle shapes. It sets a distinct structure of the frictional force network compared to only sliding friction, and from a dynamical perspective leads to an increase in the velocity correlation length, in part responsible for the increased viscosity. The physics of rolling friction is thus a key element in achieving a comprehensive understanding of strongly shear-thickening materials.

Singh, Abhinendra↗