Results for “Network simulation” — Page 25

DOE OSTI2020

Probing black hole microstate evolution with networks and random walks

We model black hole microstates and quantum tunneling transitions between them with networks and simulate their time evolution using well-established tools in network theory. In particular, we consider two models based on Bena-Warner three-charge multi-centered microstates and one model based on the D1-D5 system; we use network theory methods to determine how many centers (or D1-D5 string strands) we expect to see in a typical late-time state. We find three distinct possible phases in parameter space for the late-time behaviour of these networks, which we call ergodic, trapped, and amplified, depending on the relative importance and connectedness of microstates. We analyze in detail how these different phases of late-time behavior are related to the underlying physics of the black hole microstates. Our results indicate that the expected properties of microstates at late times cannot always be determined simply by entropic arguments; typicality is instead a highly non-trivial, emergent property of the full Hilbert space of microstates.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS2006

Reconfigurable Control with Neural Network Augmentation for a Modified F-15 Aircraft

Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

Burken, John J.↗

NASA NTRS2006

Adaptive Control Using Neural Network Augmentation for a Modified F-15 Aircraft

Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

Burken, John J.↗

NASA NTRS2010

MACHETE: Environment for Space Networking Evaluation

Space Exploration missions requires the design and implementation of space networking that differs from terrestrial networks. In a space networking architecture, interplanetary communication protocols need to be designed, validated and evaluated carefully to support different mission requirements. As actual systems are expensive to build, it is essential to have a low cost method to validate and verify mission/system designs and operations. This can be accomplished through simulation. Simulation can aid design decisions where alternative solutions are being considered, support trade-studies and enable fast study of what-if scenarios. It can be used to identify risks, verify system performance against requirements, and as an initial test environment as one moves towards emulation and actual hardware implementation of the systems. We describe the development of Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) and its use cases in supporting architecture trade studies, protocol performance and its role in hybrid simulation/emulation. The MACHETE environment contains various tools and interfaces such that users may select the set of tools tailored for the specific simulation end goal. The use cases illustrate tool combinations for simulating space networking in different mission scenarios. This simulation environment is useful in supporting space networking design for planned and future missions as well as evaluating performance of existing networks where non-determinism exist in data traffic and/or link conditions.

Jennings, Esther H.↗

NASA NTRS1967

Analog simulation of wind turbulence

Mathematical model of random wind velocity for synthesis of analog computer network to simulate wind turbulence

Finn, D. L.↗

DOE OSTI2019

Analysis of Hybrid Smart Grid Communication Network Designs for Distributed Energy Resources Coordination

The rapid growth of distributed energy resources (DERs) has prompted increasing interest in the monitoring and control of DERs through hybrid smart grid communications. This paper develops a new method to investigate the impact of DER penetrations and applications on hybrid communications network designs. A suite of hybrid communications simulation models are developed using Network Simulator 3 (ns-3). The smart grid communications topologies with different DER penetrations are built on the utility-scale distribution grid. The simulation results show that (i) with increasing photovoltaic penetrations, nine hybrid communications designs achieve the expected latency albeit with higher packet loss rate; and (ii) both BPLC-Ethernet and BPLC-WiMAX designs can accommodate the DER applications with a high data rate up to 100 Mbps.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1985

Analysis of adaptive algorithms for an integrated communication network

Techniques were examined that trade communication bandwidth for decreased transmission delays. When the network is lightly used, these schemes attempt to use additional network resources to decrease communication delays. As the network utilization rises, the schemes degrade gracefully, still providing service but with minimal use of the network. Because the schemes use a combination of circuit and packet switching, they should respond to variations in the types and amounts of network traffic. Also, a combination of circuit and packet switching to support the widely varying traffic demands imposed on an integrated network was investigated. The packet switched component is best suited to bursty traffic where some delays in delivery are acceptable. The circuit switched component is reserved for traffic that must meet real time constraints. Selected packet routing algorithms that might be used in an integrated network were simulated. An integrated traffic places widely varying workload demands on a network. Adaptive algorithms were identified, ones that respond to both the transient and evolutionary changes that arise in integrated networks. A new algorithm was developed, hybrid weighted routing, that adapts to workload changes.

Reed, Daniel A.↗

NASA NTRS2018

Evaluation of MERRA-2-Based Ozone Profile Simulations with the Global Ozonesonde Network

Chemical transport model (CTM) hindcasts of ozone (O3) are useful for filling in observational gaps and providing context for observed O3 variability and trends. We use global networks of ozonesonde stations to evaluate the O3 profiles in two simulations running versions of the NASA Global Modeling Initiative (GMI) chemical mechanism. Both simulations are tied to the NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) meteorological reanalysis: 1) The GMI CTM, and 2) The MERRA-2 GMI Replay (M2 GMI). Both simulations start in 1980, and are compared against >50,000 ozonesonde profiles from 37 global stations from the tropics to the poles. The comparisons allow us to evaluate how the Replay technique affects modeled O3 distribution, how an updated chemical mechanism in the GMI CTM affects simulated tropospheric O3 amounts, and how observed O3 distributions compare to the full set of model output. In general, M2 GMI O3 is ~10% higher than in the GMI CTM, and shows global near-surface and tropical upper troposphere/lower stratosphere (UT/LS) high biases. The updated chemical mechanism in the GMI CTM reduces these high biases. Both simulations show similar negative biases in tropical free-tropospheric O3, especially during typical biomass burning seasons. The simulations are highly-correlated with ozonesonde measurements, particularly in the UT/LS (r > 0.8), showing the ability of MERRA-2 to capture tropopause height variations. Both simulations show improved correlations with ozonesonde data and smaller O3 biases in recent years. We expect to use the sonde/model comparisons to diagnose causes of disagreement and to gauge the feasibility of calculating multidecadal O3 trends from the model output.

Stauffer, Ryan↗

NASA NTRS2018

Evaluation of MERRA-2-Based Ozone Profile Simulations with the Global Ozonesonde Network

Chemical transport model (CTM) hindcasts of ozone (O3) are useful for filling in observational gaps and providing context for observed O3 variability and trends. We use global networks of ozonesonde stations to evaluate the O3 profiles in two simulations running versions of the NASA Global Modeling Initiative (GMI) chemical mechanism. Both simulations are tied to the NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) meteorological reanalysis: 1) The GMI CTM, and 2) The MERRA-2 GMI Replay (M2 GMI). Both simulations start in 1980, and are compared against >50,000 ozonesonde profiles from 37 global stations from the tropics to the poles. The comparisons allow us to evaluate how the Replay technique affects modeled O3 distribution, how an updated chemical mechanism in the GMI CTM affects simulated tropospheric O3 amounts, and how observed O3 distributions compare to the full set of model output. In general, M2 GMI O3 is ~10% higher than in the GMI CTM, and shows global near-surface and tropical upper troposphere/lower stratosphere (UT/LS) high biases. The updated chemical mechanism in the GMI CTM reduces these high biases. Both simulations show similar negative biases in tropical free-tropospheric O3, especially during typical biomass burning seasons. The simulations are highly-correlated with ozonesonde measurements, particularly in the UT/LS (r > 0.8), showing the ability of MERRA-2 to capture tropopause height variations. Both simulations show improved correlations with ozonesonde data and smaller O3 biases in recent years. We expect to use the sonde/model comparisons to diagnose causes of disagreement and to gauge the feasibility of calculating multidecadal O3 trends from the model output.

Stauffer, Ryan M.↗

NASA NTRS1994

Application of a distributed network in computational fluid dynamic simulations

A general-purpose 3-D, incompressible Navier-Stokes algorithm is implemented on a network of concurrently operating workstations using parallel virtual machine (PVM) and compared with its performance on a CRAY Y-MP and on an Intel iPSC/860. The problem is relatively computationally intensive, and has a communication structure based primarily on nearest-neighbor communication, making it ideally suited to message passing. Such problems are frequently encountered in computational fluid dynamics (CDF), and their solution is increasingly in demand. The communication structure is explicitly coded in the implementation to fully exploit the regularity in message passing in order to produce a near-optimal solution. Results are presented for various grid sizes using up to eight processors.

Deshpande, Manish↗

NASA NTRS1990

Error analysis for earth orientation recovery from GPS data

The use of GPS navigation satellites to study earth-orientation parameters in real-time is examined analytically with simulations of network geometries. The Orbit Analysis covariance-analysis program is employed to simulate the block-II constellation of 18 GPS satellites, and attention is given to the budget for tracking errors. Simultaneous solutions are derived for earth orientation given specific satellite orbits, ground clocks, and station positions with tropospheric scaling at each station. Media effects and measurement noise are found to be the main causes of uncertainty in earth-orientation determination. A program similar to the Polaris network using single-difference carrier-phase observations can provide earth-orientation parameters with accuracies similar to those for the VLBI program. The GPS concept offers faster data turnaround and lower costs in addition to more accurate determinations of UT1 and pole position.

Zelensky, N.↗

DOE OSTI2017

NetFlow Dynamics

NetFlow Dynamics is a web-accessible analysis environment for simulating dynamic flows of materials on model networks. Performing a simulation requires both the NetFlow Dynamics application and a network model which is a description of the structure of the nodes and edges of a network including the flow capacity of each edge and the storage capacity of each node, and the sources and sinks of the material flowing on the network. NetFlow Dynamics consists of databases for storing network models, algorithms to calculate flows on networks, and a GIS-based graphical interface for performing simulations and viewing simulation results. Simulated flows are dynamic in the sense that flows on each edge of the network and inventories at each node change with time and can be out of equilibrium with boundary conditions. Any number of network models could be simulated using Net Flow Dynamics. To date, the models simulated have been models of petroleum infrastructure. The main model has been the National Transportation Fuels Model (NTFM), a network of U.S. oil fields, transmission pipelines, rail lines, refineries, tank farms, and distribution terminals. NetFlow Dynamics supports two different flow algorithms, the Gradient Flow algorithm and the Inventory Control algorithm, that were developed specifically for the NetFlow Dynamics application. The intent is to add additional algorithms in the future as needed. The ability to select from multiple algorithms is desirable because a single algorithm never covers all analysis needs. The current algorithms use a demand-driven capacity-constrained formulation which means that the algorithms strive to use all available capacity and stored inventory to meet desired flows to sinks, subject to the capacity constraints of each network component. The current flow algorithms are best suited for problems in which a material flows on a capacity-constrained network representing a supply chain in which the material supplied can be stored at each node of the network. In the petroleum models, the flowing materials are crude oil and refined products that can be stored at tank farms, refineries, or terminals (i.e. the nodes of the network). Examples of other network models that could be simulated are currency flowing in a financial network, agricultural products moving to market, or natural gas flowing on a pipeline network.

Corbet Jr., Thomas F↗

NASA NTRS2000

Modeling a Wireless Network for International Space Station

This paper describes the application of wireless local area network (LAN) simulation modeling methods to the hybrid LAN architecture designed for supporting crew-computing tools aboard the International Space Station (ISS). These crew-computing tools, such as wearable computers and portable advisory systems, will provide crew members with real-time vehicle and payload status information and access to digital technical and scientific libraries, significantly enhancing human capabilities in space. A wireless network, therefore, will provide wearable computer and remote instruments with the high performance computational power needed by next-generation 'intelligent' software applications. Wireless network performance in such simulated environments is characterized by the sustainable throughput of data under different traffic conditions. This data will be used to help plan the addition of more access points supporting new modules and more nodes for increased network capacity as the ISS grows.

Alena, Richard↗

DOE OSTI2024

Structure Development in Cross-Linked, Soybean Oil-based Waterborne Polyurethanes

Development of waterborne polyurethanes (WPU) using bio-based sources represents a step towards sustainable materials science and industry. We synthesized bio-based cationic water-dispersed crosslinked polyurethanes from high oleic soybean oil (HOSO) polyol, isophorone diisocyanate, and methyldiethanol amine, with varying ionic group contents after neutralization with acetic acid. Our primary objective was to analyze how crosslinking affects the dispersion process and film properties in multifunctional systems. The synthesis-structure-property relationship is elucidated through comprehensive analyses of the products at different stages of the synthesis. The dispersion of the WPU particles in water must occur prior to gelation during the final preparation, leading to incomplete conversion and the formation of imperfect networks. Insight into the synthesis process and polymer structure was gained by simulating polymer network parameters. Morphological analyses using synchrotron-based X-ray scattering and atomic force microscopy revealed a hierarchical structure within the WPU films. Importantly, all the films prepared in this study, without using coalescence agents, have low water absorption and high water contact angles, demonstrating their potential for textile and leather coatings and other applications.

bio-based polymers↗

DOE OSTI2021

MeshGraphNets

A PyTorch implementation of "Learning Mesh-based Simulation with Graph Networks" (Pfaff et al., 2021) to build and train graph neural network surrogates for CFD simulation.

Bartoldson, BrianR.↗

NASA NTRS1990

Experimental fault characterization of a neural network

The effects of a variety of faults on a neural network is quantified via simulation. The neural network consists of a single-layered clustering network and a three-layered classification network. The percentage of vectors mistagged by the clustering network, the percentage of vectors misclassified by the classification network, the time taken for the network to stabilize, and the output values are all measured. The results show that both transient and permanent faults have a significant impact on the performance of the measured network. The corresponding mistag and misclassification percentages are typically within 5 to 10 percent of each other. The average mistag percentage and the average misclassification percentage are both about 25 percent. After relearning, the percentage of misclassifications is reduced to 9 percent. In addition, transient faults are found to cause the network to be increasingly unstable as the duration of a transient is increased. The impact of link faults is relatively insignificant in comparison with node faults (1 versus 19 percent misclassified after relearning). There is a linear increase in the mistag and misclassification percentages with decreasing hardware redundancy. In addition, the mistag and misclassification percentages linearly decrease with increasing network size.

Tan, Chang-Huong↗

DOE OSTI2022

GSAS Tools

SAND2023-06684O GSAS Tools is a web application that manages user access to modeling and simulation tools and promotional material. This software, which is a spiking neural network (SNN) simulator, represents an SNN as a graph of stochastic differential equations and simulates the time-evolution of these equations. It has the capability of reading inputs from file and writing outputs to file, and generally supports experimentation, analysis, and algorithm development using SNNs. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Noel, Todd↗

DOE OSTI2024

Device Feasibility Analysis of Multi-level FeFETs for Neuromorphic Computing

As an emerging non-volatile memory device technology, Ferroelectric Field-Effect Transistors (FeFETs) can enable low-power, adaptive intelligent system design. However, device dimension and operating voltage dependent reliability issues of scaled FeFETs can ultimately lead to degraded performance in solving machine learning tasks. In this article, detailed experimental characterization of FeFET devices of different dimensions have been carried out to explicitly evaluate the non-ideal behavior in device conductance programming properties like number of programming states, cycle-to-cycle (C2C) variations, device-to-device (D2D) variations, and state retention. A hardware-aware software simulation approach has been adopted to capture the adversarial effects of the non-idealities on recognition accuracy through algorithm-level performance assessment by including them in NeuroSim, a popular neural network hardware simulator, to execute a neural network model considering all other hardware constraints. With the added non-idealities, significant accuracy degradation has been observed compared to the ideal scenarios where D2D variations play the most critical role. Thereafter, feasibility of a variation-aware training method has been evaluated to tackle the accuracy drop.

42 ENGINEERING↗