Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “network analyzer”

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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Causal Evaluations for Identifying Differences between Observations and Earth System Models

We use a nascent data-driven causal discovery method to find and compare causal relationships in observed data and climate model output. We consider ten different features in the Arctic climate collected from public databases on observational and Energy Exascale Earth System Model (E3SM) data. In identifying and analyzing the resulting causal networks, we make meaningful comparisons between observed and climate model interdependencies. This work demonstrates our ability to apply the PCMCI causal discovery algorithm to Arctic climate data, that there are noticeable similarities between observed and simulated Arctic climate dynamics, and that further work is needed to identify specific areas for improvement to better align models with natural observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Socioeconomically-inspired modeling to justify use of fine-grain mobility data

When designing measures to control infectious disease spread, it is crucial to understand the structure of the population for which interventions are being implemented. Recent work has highlighted the need for models that incorporate demographic heterogeneity not just in age structure but also by socioeconomic status (SES). Appropriately capturing additional sources of population heterogeneity requires considerable data and model development. To understand the potential disagreement between SES-explicit or SES-agnostic disease models, we adapted Sandia’s Adaptive Recovery Model (ARM) model to consider differences in contact structure and mortality by Social Vulnerability Index (SVI) on a theoretical network. We also incorporated an Average network that did not consider SVI. By exploring disparities in vaccine and PPE uptake by SES and comparing to Average networks, as well as analyzing the influence of global vs. local contact, we found that the two model constructions often predicted different outcomes. Whether these differences are truly reflective of incorporating SES, and which model most closely represents reality, merits further investigation.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

InterGraph-CPS: A Graph-Theoretic Approach to Characterize Cross-Domain Cyber-Physical Interdependencies and Uncertainties in Electric Grid Systems for Improved Decision-Making in Operation and Response

Critical infrastructure systems such as the electric grid are increasingly cyber-physical; yet, despite the cyber-physical characteristics of critical infrastructure systems, the physical process system and communication/control network system are traditionally analyzed in siloes. As these systems become more cyber-physical, it is crucial that models and methods are available to assess the cyber physical system (CPS) interdependencies, characteristics, and event propagation for improved planning, operation, and response. Thus, we proposed an integrated structural and temporal CPS interdependency analysis framework, InterGraph-CPS, that provides insight into the CPS function during normal operation as well as disturbances. This integrated structural and temporal interdependency framework is uniquely designed for assessing CPSs by account for the challenges of analyzing cyber and physical data streams together due to data availability, data type, and time scale differences. By leveraging both structural (e.g., graph analysis) and temporal (e.g., data analytics) techniques, different CPS behaviors and configurations can be accounted for.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

GHG Emissions Inventory Report for LvivTeploEnergo (LTE)

PNNL prepared the greenhouse gas (GHG) emissions inventory for LTE to help gain international funding for modernization requirements necessary to decarbonize LTE’s DH network. The inventory itself provides a comprehensive account of total greenhouse gas emissions from (a) LTE’s transportation (commuting and product transport), (b) purchased electricity (for water heating, heat production, and other uses), and (c) heat production for the DH network. The greenhouse gases analyzed in the inventory are Carbon Dioxide (CO 2 ), Carbon Monoxide (CO), and Nitrogen Oxide (NO x ). All data gathered for the inventory came directly from previous years of LTE data, with total emissions of each activity calculated by using emission factors from several internationally reputable sources (e.g., United National Framework Convention on Climate Change (UNFCCC), the ECLIPSE v6 dataset from the International Institute for Applied Systems Analysis (IIASA), the European Modeling and Evaluation Programme (EMEP), and the World Resources Institute (WRI)). This report further explains the methodology and justification for which scopes of emissions were included in the GHG emissions inventory spreadsheet and a description of references from which the emission factors were taken.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Real-Sim Interface: Enabling Multi-resolution Simulation and X-in-the-Loop Development for Connected and Automated Vehicles

We report connected and automated vehicles (CAVs) can bring safety, mobility, and energy benefits to transportation systems. Ideally, CAV applications would be fully evaluated and validated prior to real-world implementation. However, many technical challenges in both software and hardware hinder the process. To comprehensively evaluate all aspects of CAV applications, an integrated evaluation environment is needed with various simulation tools from different domains. In the current literature, there lacks a well-developed interface to enable multi-resolution simulation of vehicle, traffic, virtual environment, and hardware-in-the-loop (HIL) simulation. In this work, a modular and flexible interface is developed to enable multi-resolution vehicle and traffic co-simulation for CAV applications. This interface is built upon Oak Ridge National Laboratory’s (ORNL’s) Real-Sim approach, can support various simulation tools and real-time X-in-the-loop (XIL) simulations, and is based on network communication protocols. The network communication delays are analyzed for simultaneous connection of up to 20 simulators. Then, two example applications are studied to demonstrate the potential usage of the Real-Sim interface: (1) a centralized merging scenario where the dynamics of two ego vehicles are emulated with detailed vehicle and powertrain dynamics models and (2) a signal-controller-in-the-loop (SCIL) evaluation where one intersection of the simulated traffic scenario is controlled by a physical signal controller. This Real-Sim interface is a tool that allows researchers to bring real, tangible hardware and software into simulated environments to comprehensively evaluate and support a wide variety of CAV applications.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1973

NASTRAN analysis of an air storage piping system

The application of NASTRAN to a complex piping design evaluation problem is summarized. Emphasis is placed on structural modeling aspects, problems encountered in modeling and analyzing curved pipe sections, principal results, and relative merits of using NASTRAN as a pipe analysis and design tool. In addition, the piping and manifolding system was analyzed with SNAP (Structural Network Analysis Program). The parallel SNAP study provides a basis for limited comparisons between NASTRAN and SNAP as to solution agreement and computer execution time and costs.

Young, C. P., Jr.↗
NASA NTRS1982

Analysis of seismic body waves excited by the Mount Saint Helens eruption of May 18, 1980

Seismic body waves which were excited by eruption of Mt. St. Helens, and recorded by the Global Digital Seismographic Network (GDSN) stations are analyzed to determine the nature and the time sequence of the events associated with the eruption. The polarity of teleseismic P waves (period 20 sec) is identical at six stations which are distributed over a wide azimuthal range. This observation, together with a very small S to P amplitude ratio (at 20 sec), suggests that the source is a nearly vertical single force that represents the counter force of the eruption. The time history of the vertical force suggests two distinct groups of events, about two minutes apart, each consisting of several subevents with a duration of about 25 sec. The magnitude of the force is approximately 2.6 to the 17th power dyne. this vertical force is in contrast with the long period (approximately 150 sec) southward horizontal single force which was determined by a previous study and interpreted to be due to the massive landslide.

Kanamori, H.↗
NASA NTRS1983

NASCOM network ground communications availability report

A performance analysis of NASCOM Network circuits is presented. An objective of 99.80 percent availability has been established for all network circuits and an acceptable level of 99.50. A network narrative summary for the current month includes changes in network configurations, current month's totals for modes of service and trouble category losses, a discussion of trends, and significant losses that affected the performance indexes of individual or groups of circuits. A table and narrative summary of those circuits that failed to meet the objective of 99.80% availability for all network circuits and an acceptable level of 99.50. Lost time and interruption tables showing all circuits affected by outages, by trouble category, with their total time and events, scheduled operating hours, and individual availability indexes also are included. Selected circuits whose availabilities have or continue to affect the overall network availability are also analyzed.

Source record↗
NASA NTRS1984

Analysis of seismic body waves excited by the Mount St. Helens eruption of May 18, 1980

Seismic body waves which were excited by eruption of Mt. St. Helens, and recorded by the Global Digital Seismographic Network (GDSN) stations are analyzed to determine the nature and the time sequence of the events associated with the eruption. The polarity of teleseismic P waves (period 20 sec) is identical at six stations which are distributed over a wide azimuthal range. This observation, together with a very small S to P amplitude ratio (at 20 sec), suggests that the source is a nearly vertical single force that represents the counter force of the eruption. The time history of the vertical force suggests two distinct groups of events, about two minutes apart, each consisting of several subevents with a duration of about 25 sec. The magnitude of the force is approximately 2.6 to the 17th power dyne. This vertical force is in contrast with the long period (approximately 150 sec) southward horizontal single force which was determined by a previous study and interpreted to be due to the massive landslide. Previously announced in STAR as N83-15968

Kanamori, H.↗
NASA NTRS1986

Analysis of Doppler lidar wind measurements

Doppler lidar and multiple Doppler radar data were obtained in a convectively mixed planetary boundary layer. The lidar measurements were possible due to scattering from existing aerosols; radar reflecting chaff was released in the atmosphere to make it visible to the multiple Doppler radar network. The data were analyzed to obtain detailed horizontal wind structures. The divergence of the horizontal wind was calculated and the anelastic continuity equation integrated to obtain vertical air motions. Differences between the areally averaged quantities and the grid point values provided a measure of the fluctuations in the wind components or the turbulent wind fluctuations. Vertical profiles of the mean winds and quantities related to the turbulent kinetic energy components and the turbulent momentum transfers were also calculated.

Srivastava, R. C.↗
NASA NTRS2006

A Simulation Based Approach for Contingency Planning for Aircraft Turnaround Operation System Activities in Airline Hubs

Commercial aircraft undergo a significant number of maintenance and logistical activities during the turnaround operation at the departure gate. By analyzing the sequencing of these activities, more effective turnaround contingency plans may be developed for logistical and maintenance disruptions. Turnaround contingency plans are particularly important as any kind of delay in a hub based system may cascade into further delays with subsequent connections. The contingency sequencing of the maintenance and logistical turnaround activities were analyzed using a combined network and computer simulation modeling approach. Experimental analysis of both current and alternative policies provides a framework to aid in more effective tactical decision making.

Adeleye, Sanya↗
NASA NTRS2003

Time Analyzer for Time Synchronization and Monitor of the Deep Space Network

A software package has been developed to measure, monitor, and archive the performance of timing signals distributed in the NASA Deep Space Network. Timing signals are generated from a central master clock and distributed to over 100 users at distances up to 30 kilometers. The time offset due to internal distribution delays and time jitter with respect to the central master clock are critical for successful spacecraft navigation, radio science, and very long baseline interferometry (VLBI) applications. The instrument controller and operator interface software is written in LabView and runs on the Linux operating system. The software controls a commercial multiplexer to switch 120 separate timing signals to measure offset and jitter with a time-interval counter referenced to the master clock. The offset of each channel is displayed in histogram form, and "out of specification" alarms are sent to a central complex monitor and control system. At any time, the measurement cycle of 120 signals can be interrupted for diagnostic tests on an individual channel. The instrument also routinely monitors and archives the long-term stability of all frequency standards or any other 1-pps source compared against the master clock. All data is stored and made available for

Cole, Steven↗
NASA NTRS2012

Flow Analysis Tool White Paper

Faster networks are continually being built to accommodate larger data transfers. While it is intuitive to think that implementing faster networks will result in higher throughput rates, this is often not the case. There are many elements involved in data transfer, many of which are beyond the scope of the network itself. Although networks may get bigger and support faster technologies, the presence of other legacy components, such as older application software or kernel parameters, can often cause bottlenecks. Engineers must be able to identify when data flows are reaching a bottleneck that is not imposed by the network and then troubleshoot it using the tools available to them. The current best practice is to collect as much information as possible on the network traffic flows so that analysis is quick and easy. Unfortunately, no single method of collecting this information can sufficiently capture the whole endto- end picture. This becomes even more of a hurdle when large, multi-user systems are involved. In order to capture all the necessary information, multiple data sources are required. This paper presents a method for developing a flow analysis tool to effectively collect network flow data from multiple sources and provide that information to engineers in a clear, concise way for analysis. The purpose of this method is to collect enough information to quickly (and automatically) identify poorly performing flows along with the cause of the problem. The method involves the development of a set of database tables that can be populated with flow data from multiple sources, along with an easyto- use, web-based front-end interface to help network engineers access, organize, analyze, and manage all the information.

Boscia, Nichole K.↗
NASA NTRS2012

Impact of Ionospheric Scintillation on Spaceborne SAR Observations Studied Using GNSS

A survey of artifacts seen in JAXA's Phase Array type L-band synthetic aperture radar (PALSAR) data over South America during a low solar activity year is reported in this paper. A significant impact on the radar data is revealed: about 14% of the surveyed PALSAR images (totally 2779) are affected by the artifacts during a month and the artifacts occur on 74.2% of the surveyed days. The characteristics of the artifacts have led to a consideration that the artifacts are the effects of ionospheric scintillation. This raises not only a concern about scintillation effects on radar but also a question about active scintillation conditions during a low solar activity year. To assess and verify the scintillation conditions, GPS data collected from the constellation of FORMOSAT-3/COSMIC satellites and three ground-based GPS networks are processed and analyzed. The GPS data provides a global context and regional dense converge, respectively, of ionospheric irregularity and scintillation measurements. It is concluded tat even during a low solar activity year, L-band scintillation at low latitudes can occur frequently and affect L-band SAR significantly.

remote sensing↗
NASA NTRS2023

Damage Detection of a Pressure Vessel with Smart Sensing and Deep Learning

Structural Health Monitoring plays a crucial role in ensuring the safety and reliability of critical infrastructure, including pressure vessels involved in various applications. This research reports the damage detection of a pressure box employed in space habitat that operates in harsh environment where both structural failure and bolt joint loosening may occur. These failure modes are extremely hard to model based on first principles. We explore proper sensing mechanism and the associated inverse analysis algorithm that can elucidate the health condition of the pressure box. It is identified that piezoelectric impedance based active interrogation can provide necessary information for damage detection in such a system. Concurrently, deep learning technique leveraging spatial convolutional neural network is synthesized to analyze the raw data acquired and identify different types of damage. By training the deep learning model on a dataset of healthy and various damage scenarios, we can achieve high accuracy in identifying the presence of damage and its type. This research provides a data-driven methodology for structural damage detection using deep learning and has the potential to be extended to various systems with different failure modes.

Yang Zhang↗
DOE OSTI2022

An Autonomous Robot for Shell and Tube Heat Exchanger Inspection

Shell and tube heat exchangers (STHEs) are critical to energy conversion efficiency of power plants. Eddy current examination is a way to evaluate working conditions of these tubes. However, the current testing apparatus requires human to manually insert an eddy current testing (ECT) probe into and extract it out of individual tubes, and meanwhile monitor measurement results for diagnosis. It is a time-consuming and labor-intensive procedure even for an experienced technician. To tackle this challenge, in this work, we developed a robot enabled ECT system for autonomous inspection of STHEs. The robotic platform employs Mecanum wheeled chassis for high mobility, machine vision to locate tube bundle and tube inlets, a rotational Cartesian mechanism to operate at planes with all possible inclinations, and a task-specific mechanism for ECT probe delivery. Machine vision locates tube bundle and tube inlets by an April tag detection algorithm and a Circle Hough Transform (CHT) algorithm, respectively. Assisted by a guidance cone, the ECT probe is continuously fed into the tubes with a fill factor of 0.819. During this process, the eddy current data are automatically collected and real-time analyzed by convolutional neural networks (CNN), showing accuracy of nearly 100% for identifying defective and non-defective tubes and 85% for four types of defective tubes and non-defective tubes.

autonomy, deep learning, eddy current testing, hea↗
DOE OSTI2022

GNN-based end-to-end reconstruction in the CMS Phase 2 High-Granularity Calorimeter

We present the current stage of research progress towards a one-pass, completely Machine Learning (ML) based imaging calorimeter reconstruction. The model used is based on Graph Neural Networks (GNNs) and directly analyzes the hits in each HGCAL endcap. The ML algorithm is trained to predict clusters of hits originating from the same incident particle by labeling the hits with the same cluster index. We impose simple criteria to assess whether the hits associated as a cluster by the prediction are matched to those hits resulting from any particular individual incident particles. The algorithm is studied by simulating two tau leptons in each of the two HGCAL endcaps, where each tau may decay according to its measured standard model branching probabilities. The simulation includes the material interaction of the tau decay products which may create additional particles incident upon the calorimeter. Using this varied multiparticle environment we can investigate the application of this reconstruction technique and begin to characterize energy containment and performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗