Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “large scale network”

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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Three-State Majority-vote Model on Scale-Free Networks and the Unitary Relation for Critical Exponents

Abstract We investigate the three-state majority-vote model for opinion dynamics on scale-free and regular networks. In this model, an individual selects an opinion equal to the opinion of the majority of its neighbors with probability 1 − q , and different to it with probability q . The parameter q is called the noise parameter of the model. We build a network of interactions where z neighbors are selected by each added site in the system, a preferential attachment network with degree distribution k − λ , where λ = 3 for a large number of nodes N . In this work, z is called the growth parameter. Using finite-size scaling analysis, we obtain that the critical exponents $$\beta /\bar{\nu }$$ β / ν ¯ and $$\gamma /\bar{\nu }$$ γ / ν ¯ associated with the magnetization and the susceptibility, respectively. Using Monte Carlo simulations, we calculate the critical noise parameter q c as a function of z for the scale-free networks and obtain the phase diagram of the model. We find that the critical exponents add up to unity when using a special volumetric scaling, regardless of the dimension of the network of interactions. We verify this result by obtaining the critical noise and the critical exponents for the two and three-state majority-vote model on cubic lattice networks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Understanding the Impact of Data Staging for Coupled Scientific Workflows

We report the rate of data generated by cutting-edge experimental science facilities and large-scale simulations enabled by current high-performance computing (HPC) systems has continued to grow at a far greater pace than the development of the network and storage capabilities on which these systems rely. To cope with this challenge, scientist are moving toward the creation of autonomous experiments and HPC simulations using machine learning. However, efficiently moving, storing, and processing large amounts of data away from the point of origin presents an incredible challenge. In-memory computing, in situ analysis, data staging, and data streaming are recognized viable alternatives to traditional file-based methods for transferring data between coupled workflows. However, the performance trade-offs and limitations for these methods are not fully understood when used in HPC applications. This article presents a comprehensive performance assessment of the current solutions for data staging when applied to applications that are not necessary I/O intensive which makes them not ideal candidates for these methods. Our study is based on experiments running at scale on Oak Ridge National Laboratory's Summit supercomputer using applications and simulations that cover typical computational motifs and patterns. We investigated the usability and cost/benefit trade-offs of staging algorithms for HPC applications under different scenarios and highlight opportunities for optimizing the dataflow between coupled simulation workflows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Differentiable Predictions for Large Scale Structure with SHAMNet

In simulation-based models of the galaxy-halo connection, theoretical predictions for galaxy clustering and lensing are typically made based on Monte Carlo realizations of a mock universe. In this paper, we use Subhalo Abundance Matching (SHAM) as a toy model to introduce an alternative to stochastic predictions based on mock population, demonstrating how to make simulation-based predictions for clustering and lensing that are both exact and differentiable with respect to the parameters of the model. Conventional implementations of SHAM are based on iterative algorithms such as Richardson-Lucy deconvolution; here we use the JAX library for automatic differentiation to train SHAMNet, a neural network that accurately approximates the stellar-to-halo mass relation (SMHM) defined by abundance matching. In our approach to making differentiable predictions of large-scale structure, we map parameterized PDFs onto each simulated halo, and calculate gradients of summary statistics of the galaxy distribution by using autodiff to propagate the gradients of the SMHM through the statistical estimators used to measure one- and two-point functions. Our techniques are quite general, and we conclude with an overview of how they can be applied in tandem with more complex, higher-dimensional models, creating the capability to make differentiable predictions for the multi-wavelength universe of galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Continuous Emulation and Multiscale Visualization of Traffic Flow Using Stationary Roadside Sensor Data

With the advent of the next-generation traffic monitoring systems, there has been a significant increase in the spatial-temporal resolution of vehicle mobility data in many cities. Effective analysis and visualization of such data can provide transportation planners with data-driven insights, which can facilitate the understanding of multiscale traffic dynamics. In this paper, we present a web-based traffic emulator for emulating and visualizing near-real-time and historical traffic flows on highways using data from road-side sensors. To construct a continuous traffic flow, the emulator adopts an analytical pipeline that can (a) integrate traffic data collected from discrete road-side radar detection sensors, (b) interpolate traffic conditions (vehicle speed and volume) on unmeasured road segments based on traffic flow theory, and (c) generate lane-specific vehicle trajectories and movements using a mathematically optimized representation of the road network. Our app also provides an integrated visual workflow that allows users to explore the interconnected traffic dynamics using an appropriate traffic flow visualization selected based on the level of detail. We devise two innovative geo-visualization techniques that utilize an animated strips-network representation and a lane usage matrix to visualize lane performances. To ensure a smooth emulation of large-scale traffic flow in an easy-to-access web environment, we implement the emulator using client-side GPU-accelerated techniques. Lastly, we close with a case study that visualizes traffic dynamics of two scenarios - an afternoon peak hour and a traffic accident - in Chattanooga, Tennessee. Our app visualizes the responses of traffic dynamics during different traffic conditions, and to the presence of the traffic accident at different spatial scales.

42 ENGINEERING↗
DOE OSTI2023

Functional Assessment of Shell Recharge Solutions' SKY™ Platform OpenADR Capability

The project’s overall objective is to research, develop, and conduct a wide-scale demonstration of a utility smart charge management (SCM) system to develop optimal managed charging structures for grid value, evaluate the impact of EV charging on local distribution utility operations, and evaluate the utilities’ ability to control electronic vehicle (EV) charging load based on grid conditions. This report covers Argonne’s validation testing in the first period of the project, the objective of which was to determine the current managed charging functionality of the EVSE hardware, the network and the vehicle telematics software solution and understand gaps that need to be addressed prior to a large-scale demonstration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Discrete Fracture Network Modeling to Estimate Upscaled Parameters for the Topopah Spring, Lava Flow, and Tiva Canyon Aquifers at Pahute Mesa, Nevada National Security Site

This report describes the results of Discrete Fracture Network (DFN) simulations for the Topopah Spring Aquifer (TSA), Lava Flow Aquifer, and Tiva Canyon Aquifer (TCA), at Pahute Mesa on the Nevada National Security Site (NNSS), formerly the Nevada Test Site. The research focuses on calculating upscaled groundwater flow and contaminant transport parameters using DFNs generated according to fracture characteristics observed in the TSA, LFA and TCA at Pahute Mesa. The highly fractured and heterogeneous nature of these aquifers makes them candidates for stochastic DFN modeling of radionuclide transport on a small scale with subsequent upscaling. One hundred independent DFN realizations are generated for each aquifer, and the upscaled parameters for continuum simulations of subsurface flow and transport in fractured media at Pahute Mesa are calculated. Our goal is to implement a modeling approach that can translate parameters to larger-scale models that account for local-scale flow and transport processes, such as channelization of flow and transport along a few well connected, large fractures. Additionally, to simulate advective and advective-diffusive transport through the fracture networks, the Time Domain Random Walk (TDRW) approach is applied to account for matrix diffusion into a finite half-space. Moreover, a novel approach to calculate dynamic (active) fracture surface area to reflect flow channeling is implemented. This work will improve the representation of radionuclide transport processes in largescale, regulatory-focused models by providing estimates of hard-to-measure flow and contaminant transport parameters at large scales. In this report, we (1) show recent results of flow and transport simulations on multiple DFN realizations of the TSA, LFA, TCA; (2) discuss the resulting distributions of estimated upscaled parameters; (3) describe the estimation of upscaled parameters for an equivalent parallel-plate continuum model and (4) present a comparison between simulated transport from the equivalent continuum model and an actual DFN.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Optically heralded microwave photon addition

Photons with optical frequencies of a few hundred terahertz are perhaps the only way to distribute quantum information over long distances. Superconducting qubits, which are one of the most promising approaches for realizing large-scale quantum machines, operate on microwave photons at frequencies that are ~40,000 times lower. To network these quantum machines across appreciable distances, we must bridge this frequency gap. Here we implement and demonstrate a transducer that can generate correlated optical and microwave photons. In this study, we use it to show that by detecting an optical photon we generate an added microwave photon with an efficiency of ~35%. Our device uses a gigahertz nanomechanical resonance as an intermediary, which efficiently couples to optical and microwave channels through strong optomechanical and piezoelectric interactions. We show continuous operation of the transducer with 5% frequency conversion efficiency, input-referred added noise of ~100, and pulsed microwave photon generation at a heralding rate of 15 Hz. Optical absorption in the device generates thermal noise of less than two microwave photons. Improvements of the system efficiencies and device performance are necessary to realize a high rate of entanglement generation between distant microwave-frequency quantum nodes, but these enhancements are within reach.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

ReNCAT: The Resilient Node Cluster Analysis Tool

ReNCAT is a software application that suggests microgrid portfolios that reduce the impact of large-scale disruptions to power, as measured by the Social Burden Metric. ReNCAT examines a power distribution network to identify regions that can be isolated into microgrids that enable critical services to be provided even if the remainder of the study area is left without power. ReNCAT operates on a simplified representation of the power grid, one that aggregates and approximates loads and conductors. Microgrids are formed within the power network by setting switch states to split or join portions of the grid. ReNCAT identifies candidate microgrid portfolios with varying tradeoffs between cost and service availability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1985

Development of large wind energy power generation system

The background and development of an experimental 100 kW wind-energy generation system are described, and the results of current field tests are presented. The experimental wind turbine is a two-bladed down-wind horizontal axis propeller type with a 29.4 m diameter rotor and a tower 28 m in height. The plant was completed in March, 1983, and has been undergoing trouble-free tests since then. The present program calls for field tests during two years from fiscal 1983 to 1984. The development of technologies relating to the linkage and operation of wind-energy power generation system networks is planned along with the acquisition of basic data for the development of a large-scale wind energy power generation system.

Source record↗
NASA NTRS1999

Cross-Sensor Calibration of the GAI Long Range Detection Network

The long range component of the North American Lightning Detection Network has been providing experimental data products since July 1996, offering cloud-to-ground lightning coverage throughout the Atlantic and Western Pacific oceans, as well as south to the Intertropical Convergence Zone. The network experiences a strong decrease in detection efficiency with range, which is also significantly modulated by differential propagation under day, night and terminator-crossing conditions. A climatological comparison of total lightning data observed by the Optical Transient Detector (OTD) and CG lightning observed by the long range network is conducted, with strict quality control and allowance for differential network performance before and after the activation of the Canadian Lightning Detection Network. This yields a first-order geographic estimate of long range network detection efficiency and its spatial variability. Intercomparisons are also performed over the continental US, allowing large scale estimates of the midlatitude climatological IC:CG ratio and its possible dependence on latitude.

Boccippio, Dennis J.↗
NASA NTRS2019

Building a Knowledge Graph for the Air Traffic Management Community

Historically, most of the focus in the knowledge graph community has been on the support for web, social network, or product search applications. This paper describes some of our experience in developing a large-scale applied knowledge graph for a more technical audience with more specialized information access and analysis needs - the air traffic management community. We describe ATMGRAPH (NASA's Air Traffic Management (ATM) Knowledge Graph), a knowledge graph created by integrating various sources of structured aviation data, provided in large part by US federal agencies. We review some of the practical challenges we faced in creating this knowledge graph.

Air Traffic Information Management↗
NASA NTRS2019

Automatically Finding Ship-Tracks to Enable Large-Scale Analysis of Aerosol-Cloud Interactions

Ship tracks appear as long winding linear features in satellite images and are produced by aerosols from ship exhausts changing low cloud properties. They are one of the best examples of aerosol‐cloud interaction experiments. However, manually finding ship tracks from satellite data on a large scale is prohibitively costly while a large number of samples are required to improve our understanding. Here we train a deep neural network to automate finding ship tracks. The neural network model generalizes well as it not only finds ship tracks labeled by human experts but also detects those that are occasionally missed by humans. It finds more ship tracks than all previous studies combined and produces a map of ship track distributions off the California coast that matches well with known shipping traffic. Our technique will enable studying aerosol effects on low clouds using ship tracks on a large scale, which will potentially narrow the uncertainty of the aerosol‐cloud interactions.

aerosol cloud interactions↗
DOE OSTI2022

Power System Event Identification with Transfer Learning Using Large-scale Real-world Synchrophasor Data in the United States

The lack of sufficient labeled events and long training time limit the applicability of deep neural network-based power system event identification using synchrophasor data. In this paper, we propose to leverage transfer learning technique to boost the reliability and reduce the required training time of neural classifier for power system event identification. We use the weights of a neural classifier trained on one transmission system as the initial parameters of another neural classifier for a different transmission system. Numerical tests with real-world synchrophasor data from the Eastern and Western Interconnections of the United States show that the proposed transfer learning approach is very effective in not only improving the training reliability but also reducing the training time.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2013

Ridge Orientations of the Ridge-Forming Unit, Sinus Meridiani, Mars-A Fluvial Explanation

Imagery and MOLA data were used in an analysis of the ridge-forming rock unit (RFU) exposed in Sinus Meridiani (SM). This unit shows parallels at different scales with fluvial sedimentary bodies. We propose the terrestrial megafan as the prime analog for the RFU, and likely for other members of the layered units. Megafans are partial cones of fluvial sediment, with radii up to hundreds of km. Although recent reviews of hypotheses for the RFU units exclude fluvial hypotheses [1], inverted ridges in the deserts of Oman have been suggested as putative analogs for some ridges [2], apparently without appreciating The wider context in which these ridges have formed is a series of megafans [3], a relatively unappreciated geomorphic feature. It has been argued that these units conform to the megafan model at the regional, subregional and local scales [4]. At the regional scale suites of terrestrial megafans are known to cover large areas at the foot of uplands on all continents - a close parallel with the setting of the Meridiani sediments at the foot of the southern uplands of Mars, with its incised fluvial systems leading down the regional NW slope [2, 3] towards the sedimentary units. At the subregional scale the layering and internal discontinuities of the Meridiani rocks are consistent, inter alia, with stacked fluvial units [4]. Although poorly recognized as such, the prime geomorphic environment in which stream channel networks cover large areas, without intervening hillslopes, is the megafan [see e.g. 4]. Single megafans can reach 200,000 km2 [5]. Megafans thus supply an analog for areas where channel-like ridges (as a palimpsest of a prior landscape) cover the intercrater plains of Meridiani [6]. At the local, or river-reach scale, the numerous sinuous features of the RFU are suggestive of fluvial channels. Cross-cutting relationships, a common feature of channels on terrestrial megafans, are ubiquitous. Desert megafans show cemented paleo-channels as inverted topography [4] with all these characteristics.

Wilkinson, M. Justin↗
DOE OSTI2023

Hierarchical reconstruction of 3D well-connected porous media from 2D exemplars using statistics-informed neural network

The relationships between porous microstructures and transport properties are of fundamental importance in various scientific and engineering applications. Due to the intricacy, stochasticity and heterogeneity of porous media, reliable characterization and modeling of transport properties often require a complete dataset of internal microstructure samples. However, it is often an unbearable cost to acquire sufficient 3D digital microstructures by purely using microscopic imaging systems. Herein this paper presents a machine learning-based technique to hierarchically reconstruct 3D well-connected porous microstructures from one isotropic or several anisotropic low-cost 2D exemplar(s). To compactly characterize the large-scale microstructural features, a Gaussian image pyramid is built for each 2D exemplar. Local morphology patterns are collected from the Gaussian image pyramids, and then they serve as the training data to embed the 2D morphological statistics into feed-forward neural networks at multiple length levels. By using a specially-developed morphology integration scheme, the 3D morphological statistics at different levels can be inferred from the statistics-informed neural networks. Gibbs sampling is adopted to hierarchically reconstruct 3D microstructures by using multi-level 3D morphological statistics, where the large-scale, regional and local morphological patterns are statistically generated and successively added to the same 3D random field. The proposed method is tested on a series of porous media with distinct morphologies, and the statistical equivalence between the reconstructed and the real microstructures is systematically evaluated by comparing morphological descriptors and transport properties. The results demonstrate that the proposed 2D-to-3D microstructure reconstruction method is a universal and efficient approach to generating morphologically and physically realistic samples of porous media.

42 ENGINEERING↗
DOE OSTI2022

Understanding the Seismic Ground Motion Spatial Variability Using Network Analysis Community Detection

This project is to explore ground motion spatial distribution using a new approach graph-based network analysis. In this study, we combine a large-N seismic array and graph analytics to explore spatial variability and correlation at a local scale using small local and regional earthquakes. In this method, each seismic station is modeled as a node and the similarities of the waveforms that represent ground motions between two stations are modeled as edges. By analyzing this graph network using the similarity matrices and community detection algorithm, we can group the stations spatially with similar patterns. A random forest algorithm is used to reveal the important features that affect the spatial grouping. The result suggests site conditions, and how they interact with the incident seismic wavefield, strongly condition the spatial correlation of ground motion. Future progress in characterizing ground motion spatial variability will require dense wavefield measurements, either through nodal deployments, or perhaps distributed acoustic sensing measurements of seismic wavefields.

58 GEOSCIENCES↗
DOE OSTI2023

Online evolutionary neural architecture search for multivariate non-stationary time series forecasting

Time series forecasting (TSF) is one of the most important tasks in data science. TSF models are usually pre-trained with historical data and then applied on future unseen datapoints. However, real-world time series data is usually non-stationary and models trained offline usually face problems from data drift. Models trained and designed in an offline fashion can not quickly adapt to changes quickly or be deployed in real-time. To address these issues, this work presents the Online NeuroEvolution-based Neural Architecture Search (ONE-NAS) algorithm, which is a novel neural architecture search method capable of automatically designing and dynamically training recurrent neural networks (RNNs) for online forecasting tasks. Without any pre-training, ONE-NAS utilizes populations of RNNs that are continuously updated with new network structures and weights in response to new multivariate input data. ONE-NAS is tested on real-world, large-scale multivariate wind turbine data as well as the univariate Dow Jones Industrial Average (DJIA) dataset. These results demonstrate that ONE-NAS outperforms traditional statistical time series forecasting methods, including online linear regression, fixed long short-term memory (LSTM) and gated recurrent unit (GRU) models trained online, as well as state-of-the-art, online ARIMA strategies. Additionally, results show that utilizing multiple populations of RNNs which are periodically repopulated provide significant performance improvements, allowing this online neural network architecture design and training to be successful.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Scalable Architecture for Multihop Wireless ad Hoc Networks

A scalable architecture for wireless digital data and voice communications via ad hoc networks has been proposed. Although the details of the architecture and of its implementation in hardware and software have yet to be developed, the broad outlines of the architecture are fairly clear: This architecture departs from current commercial wireless communication architectures, which are characterized by low effective bandwidth per user and are not well suited to low-cost, rapid scaling in large metropolitan areas. This architecture is inspired by a vision more akin to that of more than two dozen noncommercial community wireless networking organizations established by volunteers in North America and several European countries.

Arabshahi, Payman↗