Search NASA⌕ Search

SEARCH · Search NASA

Results for “network reduction”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

The structure and properties of boron carbide ceramics modified by high-current pulsed electron-beam

The present work is devoted to numerical simulation of temperature fields and the analysis of structural and strength properties of the samples surface layer of boron carbide ceramics treated by the high-current pulsed electron-beam of the submillisecond duration. The samples made of sintered boron carbide ceramics are used in these investigations. The problem of calculating the temperature field is reduced to solving the thermal conductivity equation. The electron beam density ranges between 8…30 J/cm{sup 2}, while the pulse durations are 100…200 μs in numerical modelling. The results of modelling the temperature field allowed ascertaining the threshold parameters of the electron beam, such as energy density and pulse duration. The electron beam irradiation is accompanied by the structural modification of the surface layer of boron carbide ceramics either in the single-phase (liquid or solid) or two-phase (solid-liquid) states. The sample surface of boron carbide ceramics is treated under the two-phase state (solid-liquid) conditions of the structural modification. The surface layer is modified by the high-current pulsed electron-beam produced by SOLO installation at the Institute of High Current Electronics of the Siberian Branch of the Russian Academy of Sciences, Tomsk, Russia. The elemental composition and the defect structure of the modified surface layer are analyzed by the optical instrument, scanning electron and transmission electron microscopes. Mechanical properties of the modified layer are determined measuring its hardness and crack resistance. Research results show that the melting and subsequent rapid solidification of the surface layer lead to such phenomena as fragmentation due to a crack network, grain size reduction, formation of the sub-grained structure due to mechanical twinning, and increase of hardness and crack resistance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Learning-based traffic signal control algorithms with neighborhood information sharing: An application for sustainable mobility

Here, this research applies R-Markov Average Reward Technique based reinforcement learning (RL) algorithm, namely RMART, for vehicular signal control problem leveraging information sharing among signal controllers in connected vehicle environment. We implemented the algorithm in a network of 18 signalized intersections and compare the performance of RMART with fixed, adaptive, and variants of the RL schemes. Results show significant improvement in system performance for RMART algorithm with information sharing over both traditional fixed signal timing plans and real time adaptive control schemes. Additionally, the comparison with reinforcement learning algorithms including Q learning and SARSA indicate that RMART performs better at higher congestion levels. Further, a multi-reward structure is proposed that dynamically adjusts the reward function with varying congestion states at the intersection. Finally, the results from test networks show significant reduction in emissions (CO, CO 2 , NO x , VOC, PM 10 ) when RL algorithms are implemented compared to fixed signal timings and adaptive schemes.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Design of Hopfield Networks Based on Superconducting Coupled Oscillators

The global energy shortage has driven the development of many energy-efficient computational platforms beyond Moore's law, among which brain-inspired neuromorphic computing is one of the promising solutions. Associative memory and pattern recognition are important computations solved by brain-inspired Hopfield networks. Classical Hopfield networks store memories via fixed point attractors of their dynamics. In oscillatory Hopfield networks, these attractors are replaced by periodic orbits. Here, we design an oscillatory Hopfield network based on coupled superconducting oscillators. We first employ a mathematical phase reduction approach to map networks of coupled superconducting rapid single flux quantum (RSFQ) ring oscillators to coupled Kuramoto phase-oscillator networks. We use this theory to numerically optimize the hardware's mutual inductances in order to directly match the phase-reduced superconducting oscillators to a model of phase-oscillator-based Hopfield networks. The resulting network can store multiple oscillatory phase-locked memory patterns and recover the patterns based on the initial phase conditions. As different pattern recognition tasks, or learning, require tunable connectivity strengths between the oscillatory nodes, we further employ a coupler circuit that enables tuning the coupling strength between two oscillators by applying an external flux. We demonstrate the functionality of our design through numerical simulations of a small example network with oscillators operating at 86 GHz and recognizing patterns within 10 ns. Our approach enables the learning and retrieval of dynamical memory patterns with a wide range of applications where rhythmic dynamic output is beneficial.

Cheng, Ran↗
DOE OSTI2021

Fault Detection Utilizing Convolution Neural Network on Timeseries Synchrophasor Data From Phasor Measurement Units

An end-to-end supervised learning method is proposed for fault detection in the electric grid using Big Data from multiple Phasor Measurement Units (PMUs). The approach consists of preprocessing steps aimed at reducing data noise and dimensionality, followed by utilization of six classification models considered for detecting faults. Three of the models were variants of Convolutional Neural Network (CNN) architectures that consider a single type of measurement (voltage, current or frequency) at all PMUs or all types together also at all PMUs. CNN based models were compared to traditional methods of Logistic Regression (LR), Multi-layer Perceptron (MLP) and Support Vector Machine (SVM). Evaluation was conducted on two-year data measured by PMUs at 37 locations in a large electric grid. Here, the response variable for classification were extracted from the grid-wide outage event log. Experiments show that CNN-based models outperformed traditional methods on one year out-of-sample outage detection over the entire grid.

42 ENGINEERING↗
DOE OSTI2020

A dynamic microbial sulfur cycle in a serpentinizing continental ophiolite

Summary Serpentinization is the hydration and oxidation of ultramafic rock, which occurs as oceanic lithosphere is emplaced onto continental margins (ophiolites), and along the seafloor as faulting exposes this mantle‐derived material to circulating hydrothermal fluids. This process leads to distinctive fluid chemistries as molecular hydrogen (H 2 ) and hydroxyl ions (OH − ) are produced and reduced carbon compounds are mobilized. Serpentinizing ophiolites also serve as a vector to transport sulfur compounds from the seafloor onto the continents. We investigated hyperalkaline, sulfur‐rich, brackish groundwater in a serpentinizing continental ophiolite to elucidate the role of sulfur compounds in fuelling in situ microbial activities. Here we illustrate that key sulfur‐cycling taxa, including Dethiobacter , Desulfitispora and ‘ Desulforudis ’, persist throughout this extreme environment. Biologically catalysed redox reactions involving sulfate, sulfide and intermediate sulfur compounds are thermodynamically favourable in the groundwater, which indicates they may be vital to sustaining life in these characteristically oxidant‐ and energy‐limited systems. Furthermore, metagenomic and metatranscriptomic analyses reveal a complex network involving sulfate reduction, sulfide oxidation and thiosulfate reactions. Our findings highlight the importance of the complete inorganic sulfur cycle in serpentinizing fluids and suggest sulfur biogeochemistry provides a key link between terrestrial serpentinizing ecosystems and their submarine heritage.

Sabuda, Mary C.↗
DOE OSTI2022

A Micro-Mechanical Constitutive Model to Predict Hygrothermal Aging of Cross-Linked Polymers

Abstract A multi-physics material model is presented to describe the effects of temperature, oxygen, and humidity on the constitutive response of cross-linked polymers. The effect of hygrothermal damage on the mechanical integrity of the polymer matrix can be considered as the result of damage accumulation of two independent aging mechanism namely, i) thermo-oxidative, and ii) hydrolytic aging. In order to capture the mutual effects of thermo-oxidative and hydrolytic aging, an assumption has been made that each of the aging phenomenon can be superposed to each other. In fact, each of them works independently and as a result, they can compete with each other. Utilizing the theory of network decomposition, all phenomena and their correlation were modeled and thus, the strain energy function of the polymer matrix is written with respect to four independent mechanisms, i) the shrinking original matrix that has neither been attacked by water nor oxygen, ii) conversion of the first network to two new network due to the reduction and formation of cross-links, and iii) energy loss from network degradation due to attack of the water molecules to polymer active agents. Moreover, the proposed model is micro-mechanically based and is mainly relevant on thin samples due to our underlying assumption of homogeneous diffusion of oxygen and water throughout the matrix. The model has been validated against extensive data-sets obtained from experiments we specifically designed for concept validation.

Bahrololoumi, Amir↗
DOE OSTI2024

Transfer learning for metamaterial design and simulation

Abstract We demonstrate transfer learning as a tool to improve the efficacy of training deep learning models based on residual neural networks (ResNets). Specifically, we examine its use for study of multi-scale electrically large metasurface arrays under open boundary conditions in electromagnetic metamaterials. Our aim is to assess the efficiency of transfer learning across a range of problem domains that vary in their resemblance to the original base problem for which the ResNet model was initially trained. We use a quasi-analytical discrete dipole approximation (DDA) method to simulate electrically large metasurface arrays to obtain ground truth data for training and testing of our deep neural network. Our approach can save significant time for examining novel metasurface designs by harnessing the power of transfer learning, as it effectively mitigates the pervasive data bottleneck issue commonly encountered in deep learning. We demonstrate that for the best case when the transfer task is sufficiently similar to the target task, a new task can be effectively trained using only a few data points yet still achieve a test mean absolute relative error of 3 % with a pre-trained neural network, realizing data reduction by a factor of 1000.

Peng, Rixi↗
NASA NTRS1966

High-performance RC bandpass filter is adapted to miniaturized construction

Miniaturized bandpass filter with RC networks is suitable for use in integrated circuits. The circuit consists of three stages of amplification with additional resistive and capacitive components to obtain the desired characteristics. The advantages of the active RC filter network are the reduction in size and weight and elimination of magnetic materials.

Source record↗
NASA NTRS1998

Simple Automatic File Exchange (SAFE) to Support Low-Cost Spacecraft Operation via the Internet

Various issues associated with Simple Automatic File Exchange (SAFE) are presented in viewgraph form. Specific topics include: 1) Packet telemetry, Internet IP networks and cost reduction; 2) Basic functions and technical features of SAFE; 3) Project goals, including low-cost satellite transmission to data centers to be distributed via an Internet; 4) Operations with a replicated file protocol; 5) File exchange operation; 6) Ground stations as gateways; 7) Lessons learned from demonstrations and tests with SAFE; and 8) Feedback and future initiatives.

Baker, Paul↗
NASA NTRS1995

Experiences with the AEROnet/PSCN ATM Prototype

This paper discusses the experience gained by the AEROnet/PSCN networking team in deploying a prototype Asynchronous Transfer Mode (ATM) based network as part of the wide-area network for the Numerical Aerodynamic Simulation (NAS) Program at NASA Ames Research Center. The objectives of this prototype were to test concepts in using ATM over wide-area Internet Protocol (IP) networks and measure end-to-end system performance. This testbed showed that end-to-end ATM over a DS3 reaches approximately 80% of the throughput achieved from a FDDI to DS3 network. The 20% reduction in through-put can be attributed to the overhead associated with running ATM. As a result, we conclude that if the loss in capacity due to ATM overhead is balanced by the reduction in cost of ATM services, as compared to dedicated circuits, then ATM can be a viable alternative.

Kurak, Richard S.↗
DOE OSTI2022

Dimensionally Reduced Model for Rapid and Accurate Prediction of Gas Saturation, Pressure, and Brine Production in a CO 2 Storage Application: Case Study Using the SACROC Field as Part of SMART Task 5

This technical report presents work conducted by the sub surface analysis team of the Strategic Systems Analysis & Engineering group at NETL for Task 5 of SMART Phase 1. This study involved the development of deep learning models for CO 2 geologic storage that are capable of accurate prediction of spatio-temporal outputs of CO 2 saturation, pressure, and brine production in three dimensional space over a storage operation's injection and post-injection timeframes. The model framework involves ensembling multi-layer encoder networks that provide dimesionality reduction of geologic inputs with fully connected long short-term memory (LSTM) neural networks that generate time-series prediction This approach offers a means to maximize training time efficiency, reduce computational memory burden, and minimize prediction turnaround.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Model reduction for the dynamics and control of large structural systems via neutral network processing direct numerical optimization

Three neural network processing approaches in a direct numerical optimization model reduction scheme are proposed and investigated. Large structural systems, such as large space structures, offer new challenges to both structural dynamicists and control engineers. One such challenge is that of dimensionality. Indeed these distributed parameter systems can be modeled either by infinite dimensional mathematical models (typically partial differential equations) or by high dimensional discrete models (typically finite element models) often exhibiting thousands of vibrational modes usually closely spaced and with little, if any, damping. Clearly, some form of model reduction is in order, especially for the control engineer who can actively control but a few of the modes using system identification based on a limited number of sensors. Inasmuch as the amount of 'control spillover' (in which the control inputs excite the neglected dynamics) and/or 'observation spillover' (where neglected dynamics affect system identification) is to a large extent determined by the choice of particular reduced model (RM), the way in which this model reduction is carried out is often critical.

Becus, Georges A.↗
DOE OSTI2013

Thermal stability and lifetime estimates of a high temperature epoxy by T g reduction

Thermal degradation of a high temperature epoxy network is studied in terms glass transition temperature (T g ) reduction over a temperature window encompassing the T g of the network. The T g is shown to decrease as the network is thermally aged at elevated temperatures in air and in argon. The duration of the aging experiments is extended to long time such that the absolute T g reduction approaches a long time reduction plateau. Degradation is dominated by non-oxidative pyrolysis with a small contribution from diffusion limited thermal oxidative degradation at the surface. A time–temperature superposition is constructed from the extent of T g reduction of samples aged in air and the thermal shift factors are shown to have Arrhenius scaling behavior. An activation energy is extracted that agrees with previous activation energy measurements derived from other property measurements of the same network aged under similar conditions. The agreement of the activation energy with past results shows that T g reduction is controlled by the same degradation mechanism and may be used as an observable for lifetime estimates when thermal degradation is pyrolytic in nature. The extent of T g reduction is modeled with an autocatalytic rate expression and compared to previous property measurements to show the difference in sensitivity of observable material properties on degradation.

36 MATERIALS SCIENCE↗
NASA NTRS1974

Development of the Apollo Control Network

Description of activities associated with photogrammetric reduction of photography and support data gathered by Apollo spacecraft in lunar orbit. The aim of these activities is to combine all data from Apollo 15, 16, and 17 missions for the purpose of forming a unified control system on the moon called the Apollo Control Network. The status of efforts at reduction of data from each of these missions is summarized along with a proposed scheme for combining the three missions in a simultaneous reduction. Plans to expand the network by use of Apollo oblique mapping photography are also discussed, and the results of an oblique triangulation test are evaluated.

Hassell, J. R.↗
DOE OSTI2019

Casting Nanoporous Platinum in Metal–Organic Frameworks

Nanocasting based on porous templates is a powerful strategy in accessing materials and structures that are difficult to form by bottom-up syntheses in a controlled fashion. A facile synthetic strategy for casting ordered, nanoporous platinum (NP-Pt) networks with a high degree of control by using metal-organic frameworks (MOFs) as templates is reported here. The Pt precursor is first infiltrated into zirconium-based MOFs and subsequently transformed to 3D metallic networks via a chemical reduction process. It is demonstrated that the dimensions and topologies of the cast NP-Pt networks can be accurately controlled by using different MOFs as templates. The Brunauer-Emmett-Teller surface areas of the NP-Pt networks are estimated to be >100 m 2 g -1 and they exhibit excellent catalytic activities in the methanol electrooxidation reaction (MEOR). Finally, this new methodology presents an attractive route to prepare well-defined nanoporous materials for diverse applications ranging from energy to sensing and biotechnology.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Data reduction through optimized scalar quantization for more compact neural networks

Raw data generation for several existing and planned large physics experiments now exceeds TB/s rates, generating untenable data sets in very little time. Those data often demonstrate high dimensionality while containing limited information. Meanwhile, Machine Learning algorithms are now becoming an essential part of data processing and data analysis. Those algorithms can be used offline for post processing and post data analysis, or they can be used online for real time processing providing ultra low latency experiment monitoring. Both use cases would benefit from data throughput reduction while preserving relevant information: one by reducing the offline storage requirements by several orders of magnitude and the other by allowing ultra fast online inferencing with low complexity Machine Learning models. Moreover, reducing the data source throughput also reduces material cost, power and data management requirements. In this work we demonstrate optimized nonuniform scalar quantization for data source reduction. This data reduction allows lower dimensional representations while preserving the relevant information of the data, thus enabling high accuracy Tiny Machine Learning classifier models for online fast inferences. We demonstrate this approach with an initial proof of concept targeting the CookieBox, an array of electron spectrometers used for angular streaking, that was developed for LCLS-II as an online beam diagnostic tool. We used the Lloyd-Max algorithm with the CookieBox dataset to design an optimized nonuniform scalar quantizer. Optimized quantization lets us reduce input data volume by 69% with no significant impact on inference accuracy. When we tolerate a 2% loss on inference accuracy, we achieved 81% of input data reduction. Finally, the change from a 7-bit to a 3-bit input data quantization reduces our neural network size by 38%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Interpretation of autoencoder-learned collective variables using Morse–Smale complex and sublevelset persistent homology: An application on molecular trajectories

Dimensionality reduction often serves as the first step toward a minimalist understanding of physical systems as well as the accelerated simulations of them. In particular, neural network-based nonlinear dimensionality reduction methods, such as autoencoders, have shown promising outcomes in uncovering collective variables (CVs). However, the physical meaning of these CVs remains largely elusive. In this work, we constructed a framework that (1) determines the optimal number of CVs needed to capture the essential molecular motions using an ensemble of hierarchical autoencoders and (2) provides topology-based interpretations to the autoencoder-learned CVs with Morse–Smale complex and sublevelset persistent homology. Furthermore, this approach was exemplified using a series of n-alkanes and can be regarded as a general, explainable nonlinear dimensionality reduction method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗