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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

High Fidelity Simulations of Large-Scale Wireless Networks (Part I)

Wireless systems and networks have experienced rapid growth over the last decade with the advent of smart devices for everyday use. These systems, which include smartphones, vehicular gadgets, and internet-of-things devices, are becoming ubiquitous and ever-more important. They pose interesting research challenges for design and analysis of new network protocols due to their large scale and complexity. In this work, we focus on the challenging aspect of simulating the inter-connectivity of many of these devices in wireless networks. The quantitative study of large scale wireless networks, with counts of wireless devices in the thousands, is a very difficult problem with no known acceptable solution. By necessity, simulations of this scale have to approximate reality, but the algorithms employed in most modern-day network simulators can be improved for wireless network simulations. In this report, we present advances that we have made and propositions for continuation of progress towards a framework for high fidelity simulations of wireless networks. This work is not complete in that a final simulation framework tool is yet to be produced. However, we highlight the major bottlenecks and address them individually with initial results showing enough promise.

42 ENGINEERING↗
DOE OSTI2015

Redesigning rain gauges network in Johor using geostatistics and simulated annealing

Recently, many rainfall network design techniques have been developed, discussed and compared by many researchers. Present day hydrological studies require higher levels of accuracy from collected data. In numerous basins, the rain gauge stations are located without clear scientific understanding. In this study, an attempt is made to redesign rain gauge network for Johor, Malaysia in order to meet the required level of accuracy preset by rainfall data users. The existing network of 84 rain gauges in Johor is optimized and redesigned into a new locations by using rainfall, humidity, solar radiation, temperature and wind speed data collected during the monsoon season (November - February) of 1975 until 2008. This study used the combination of geostatistics method (variance-reduction method) and simulated annealing as the algorithm of optimization during the redesigned proses. The result shows that the new rain gauge location provides minimum value of estimated variance. This shows that the combination of geostatistics method (variance-reduction method) and simulated annealing is successful in the development of the new optimum rain gauge system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Cyber Security Research Frameworks For Coevolutionary Network Defense

Several architectures have been created for developing and testing systems used in network security, but most are meant to provide a platform for running cyber security experiments as opposed to automating experiment processes. In the first paper, we propose a framework termed Distributed Cyber Security Automation Framework for Experiments (DCAFE) that enables experiment automation and control in a distributed environment. Predictive analysis of adversaries is another thorny issue in cyber security. Game theory can be used to mathematically analyze adversary models, but its scalability limitations restrict its use. Computational game theory allows us to scale classical game theory to larger, more complex systems. In the second paper, we propose a framework termed Coevolutionary Agent-based Network Defense Lightweight Event System (CANDLES) that can coevolve attacker and defender agent strategies and capabilities and evaluate potential solutions with a custom network defense simulation. The third paper is a continuation of the CANDLES project in which we rewrote key parts of the framework. Attackers and defenders have been redesigned to evolve pure strategy, and a new network security simulation is devised which specifies network architecture and adds a temporal aspect. We also add a hill climber algorithm to evaluate the search space and justify the use of a coevolutionary algorithm.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

A sweep algorithm for massively parallel simulation of circuit-switched networks

A new massively parallel algorithm is presented for simulating large asymmetric circuit-switched networks, controlled by a randomized-routing policy that includes trunk-reservation. A single instruction multiple data (SIMD) implementation is described, and corresponding experiments on a 16384 processor MasPar parallel computer are reported. A multiple instruction multiple data (MIMD) implementation is also described, and corresponding experiments on an Intel IPSC/860 parallel computer, using 16 processors, are reported. By exploiting parallelism, our algorithm increases the possible execution rate of such complex simulations by as much as an order of magnitude.

Gaujal, Bruno↗
DOE OSTI2022

Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation

The combination of neural networks and quantum Monte Carlo methods has arisen as a promising path forward for highly accurate electronic structure calculations. Previous proposals have combined equivariant neural network layers with a final antisymmetric layer in order to satisfy the antisymmetry requirements of the electronic wavefunction. However, to date it is unclear if one can represent antisymmetric functions of physical interest, and it is difficult to precisely measure the expressiveness of the antisymmetric layer. Here, this work attempts to address this problem by introducing explicitly antisymmetrized universal neural network layers. This approach has a computational cost which increases factorially with respect to the system size, but we are nonetheless able to apply it to small systems to better understand how the structure of the antisymmetric layer affects its performance. We first introduce a generic antisymmetric (GA) neural network layer, which we use to replace the entire antisymmetric layer of the highly accurate ansatz known as the FermiNet. We demonstrate that the resulting FermiNet-GA architecture can yield effectively the exact ground state energy for small atoms and molecules. We then consider a factorized antisymmetric (FA) layer which more directly generalizes the FermiNet by replacing the products of determinants with products of antisymmetrized neural networks. We find, interestingly, that the resulting FermiNet-FA architecture does not significantly outperform the FermiNet. This strongly suggests that the sum of products of antisymmetries is a key limiting aspect of the FermiNet architecture. To explore this further, we investigate a slight modification of the FermiNet, called the full determinant mode, which replaces each product of determinants with a single combined determinant. We find that the full single-determinant FermiNet closes a large part of the gap between the standard single-determinant FermiNet and FermiNet-GA on small atomic and molecular problems. Surprisingly, on the nitrogen molecule at a dissociating bond length of 4.0 Bohr, the full single-determinant FermiNet can outperform the largest standard FermiNet calculation with 64 determinants.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Large eddy simulation of a reacting spray flame with multiple realizations under compression ignition engine conditions

An n-dodecane spray flame (Spray A from Engine Combustion Network) was simulated using a detailed combustion model along with a dynamic structure LES model to evaluate its performance at engine-relevant conditions and understand the transient behavior of this turbulent flame. The liquid spray was treated with a traditional Lagrangian method and the gas-phase reaction was modeled using a detailed combustion model. A 103-species skeletal mechanism was used for the n-dodecane chemical kinetic model. Significantly different flame structures and ignition processes are observed for the LES compared to those of RANS predictions. The LES data suggests that the first ignition initiates in lean mixture and propagates to rich mixture, and the main ignition happens in rich mixture, preferable less than 0.14 in mixture fraction space. LES was observed to have multiple ignition spots in the mixing layer simultaneously while the main ignition initiates in a clearly asymmetric fashion. The temporal flame development also indicates the flame stabilization mechanism is auto-ignition controlled and modulated by flame propagation. Soot predictions by LES present much better agreement with experiments compared to RANS both qualitatively and quantitatively. Multiple realizations for LES were performed to understand the realization to realization variation and to establish best practices for ensemble-averaging diesel spray flames. The relevance index analysis suggests that an average of 2 and 5 realizations can reach 99\% of similarity to the target average of 16 realizations on the temperature and mixture fraction fields, respectively. However, more realizations are necessary for OH and soot mass fraction due to their high fluctuations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Non-unimodal and non-concave relationships in the network Macroscopic Fundamental Diagram caused by hierarchical streets

Unimodal, concave relationships between average network productivity and accumulation or density aggregated across spatially compact regions of urban networks—so called network Macroscopic Fundamental Diagrams (MFDs)—have recently been shown to exist on homogeneous street networks. When present, MFD relationships facilitate the modeling of traffic congestion at a regional level and have led to the development of various regional traffic control strategies. However, real street networks are not homogeneous—they generally have a hierarchical structure where some streets (e.g., arterials) promote higher mobility than others (e.g., local roads). Here, this paper examines how the presence of hierarchical roadway structures may potentially cause non-unimodal patterns in a network's MFD. These are observed using three types of tools: analytical models of simple network structures, simulations of various idealized roadway networks, and empirical data. The impacts of street hierarchy depend on how vehicles use different roadway types to move within the network; i.e., their routing strategy. The findings suggest that the presence of roadway hierarchies may lead to MFDs that have non-unimodal or non-concave patterns on the free-flow branch when vehicles route themselves according to user equilibrium principles, which is closest to what would be observed in realistic situations. Such patterns are contrary to what is traditionally assumed in most MFD-based modeling frameworks. However, the unimodal and concave MFD should be expected under system optimal routing conditions that maximize network productivity for a given traffic state.

42 ENGINEERING↗
NASA NTRSDate not supplied

m:N Operations High Complexity Simulation

The Human Autonomy Teaming Laboratory at NASA Ames Research Center (ARC) is planning to conduct an experiment investigating system-level effects operational vertiport disruptions on Urban Air Mobility (UAM) traffic in a simulated vertiport network in Denver, CO. The simulation will involve two parties, one at NASA ARC and another at NASA Langley Research Center (LaRC), each playing the role of a UAM Operator. This study, referred to as the "High Complexity Sim," features a 3-by-2-by-2 mixed subjects design. The variables to be manipulated are the scale of the disruption (Disruption, three levels: Small, Medium, and Large, affecting 1, 2, and 3+, vertiports, respectively), m:N ratio (Ratio, two levels: 2:6 and 2:12), and the assumption of UAM corridors (Corridors, two levels: With and Without). Local and system-wide capacity (i.e., throughput), efficiency (scale and number of flight delays, filed-vs-flown flight times), and human performance metrics (workload, situation awareness, heart rate/heart rate variability, eye gaze/fixation and saccades, stress) will be measured. This presentation details the experimental design and planned timeline for the study.

multi-vehicle↗
DOE OSTI2015

Hydrogen adsorption and desorption with 3D silicon nanotube-network and film-network structures: Monte Carlo simulations

Hydrogen is clean, sustainable, and renewable, thus is viewed as promising energy carrier. However, its industrial utilization is greatly hampered by the lack of effective hydrogen storage and release method. Carbon nanotubes (CNTs) were viewed as one of the potential hydrogen containers, but it has been proved that pure CNTs cannot attain the desired target capacity of hydrogen storage. In this paper, we present a numerical study on the material-driven and structure-driven hydrogen adsorption of 3D silicon networks and propose a deformation-driven hydrogen desorption approach based on molecular simulations. Two types of 3D nanostructures, silicon nanotube-network (Si-NN) and silicon film-network (Si-FN), are first investigated in terms of hydrogen adsorption and desorption capacity with grand canonical Monte Carlo simulations. It is revealed that the hydrogen storage capacity is determined by the lithium doping ratio and geometrical parameters, and the maximum hydrogen uptake can be achieved by a 3D nanostructure with optimal configuration and doping ratio obtained through design optimization technique. For hydrogen desorption, a mechanical-deformation-driven-hydrogen-release approach is proposed. Compared with temperature/pressure change-induced hydrogen desorption method, the proposed approach is so effective that nearly complete hydrogen desorption can be achieved by Si-FN nanostructures under sufficient compression but without structural failure observed. The approach is also reversible since the mechanical deformation in Si-FN nanostructures can be elastically recovered, which suggests a good reusability. This study may shed light on the mechanism of hydrogen adsorption and desorption and thus provide useful guidance toward engineering design of microstructural hydrogen (or other gas) adsorption materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Idaho National Laboratory Quality Of Service Dataset

The code is designed to run tests to generate and collect data from a Wi-Fi network using OPENWRT or a simulated a 5G network using Open5gs and UERANSIM. The tests simulate the network performing downloads or uploads of various files with a varying number of concurrent users. The tests use tcpdump to collect the network traffic but only stores the summarized data. The summarized datasets will be included.

Krome, Cameron [Idaho National Laboratory (INL), I↗
DOE OSTI2020

Fast predictions of liquid-phase acid-catalyzed reaction rates using molecular dynamics simulations and convolutional neural networks

The rates of liquid-phase, acid-catalyzed reactions relevant to the upgrading of biomass into high-value chemicals are highly sensitive to solvent composition and identifying suitable solvent mixtures is theoretically and experimentally challenging. We show that the complex atomistic configurations of reactant–solvent environments generated by classical molecular dynamics simulations can be exploited by 3D convolutional neural networks to enable accurate predictions of Brønsted acid-catalyzed reaction rates for model biomass compounds. We develop a 3D convolutional neural network, which we call SolventNet, and train it to predict acid-catalyzed reaction rates using experimental reaction data and corresponding molecular dynamics simulation data for seven biomass-derived oxygenates in water–cosolvent mixtures. We show that SolventNet can predict reaction rates for additional reactants and solvent systems an order of magnitude faster than prior simulation methods. This combination of machine learning with molecular dynamics enables the rapid, high-throughput screening of solvent systems and identification of improved biomass conversion conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1993

The role of simulation in the design of a neural network chip

An iterative, simulation-based design procedure for a neural network chip is introduced. For this design procedure, the goal is to produce a chip layout for a neural network in which the weights are determined by transistor gate width-to-length ratios. In a given iteration, the current layout is simulated using the circuit simulator SPICE, and layout adjustments are made based on conventional gradient-decent methods. After the iteration converges, the chip is fabricated. Monte Carlo analysis is used to predict the effect of statistical fabrication process variations on the overall performance of the neural network chip.

Desai, Utpal↗
NASA NTRS1998

Simulation of a NASA LEO Satellite Hybrid Network

Various issues associated with "Simulation of a NASA LEO Satellite Hybrid Network" are presented in viewgraph form. Specific topics include: 1) Objective of the investigation; 2) Satellite orbit description and included nodes; 3) TCP and FTP simulation descriptions; 4) Simulation results; and 5) Derived conclusions.

Wallett, Thomas M.↗
DOE OSTI2022

Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits at Exascale

The numerical simulation of quantum circuits is an indispensable tool for development, verification, and validation of hybrid quantum-classical algorithms intended for near-term quantum co-processors. The emergence of exascale high-performance computing (HPC) platforms presents new opportunities for pushing the boundaries of quantum circuit simulation. Here, we present a modernized version of the Tensor Network Quantum Virtual Machine (TNQVM) that serves as the quantum circuit simulation backend in the eXtreme-scale ACCelerator (XACC) framework. The new version is based on the scalable tensor network processing library ExaTN (Exascale Tensor Networks). It provides multiple configurable quantum circuit simulators that perform either an exact quantum circuit simulation via the full tensor network contraction or an approximate simulation via a suitably chosen tensor factorization scheme. Upon necessity, stochastic noise modeling from real quantum processors is incorporated into the simulations by modeling quantum channels with Kraus tensors. By combining the portable XACC quantum programming frontend and the scalable ExaTN numerical processing backend, we introduce an end-to-end virtual quantum development environment that can scale from laptops to future exascale platforms. We report initial benchmarks of our framework, which include a demonstration of the distributed execution, incorporation of quantum decoherence models, and simulation of the random quantum circuits used for the certification of quantum supremacy on Google’s Sycamore superconducting architecture.

Nguyen, Thien↗
DOE OSTI2025

Hydrologic Model Data for the East Fork Poplar Creek Watershed Simulated with the Advanced Terrestrial Simulator (ATS): Streamflow and Network Expansion–Contraction Dynamics

This dataset supports hydrologic modeling and stream network expansion–contraction analysis for the East Fork Poplar Creek (EFPC) Watershed in Tennessee. It includes a Jupyter notebook for model setup, model configuration files, simulation outputs, and derived products used to evaluate model performance and investigate stream dynamics under varying hydrologic conditions. The dataset was generated using the Watershed Workflow Python package and the Advanced Terrestrial Simulator (ATS), enabling integrated surface–subsurface hydrologic simulations using a stream-aligned mesh. Outputs include high-resolution time series of streamflow, active network length, water table depth, and related hydrologic variables. Also included are spatially explicit stream persistency indices and classifications of reaches as perennial or non-perennial. These data facilitate reproducibility and support further research on stream intermittency and variability in network extent.The model data archive is organized in following directories:1) model_setup_inputsContains the Watershed Workflow Jupyter notebooks (accessed through any open source code editor), selected input datasets, and resulting ATS input files, including XML files (access through any open source code editor), computational mesh (.exo files can be viewed using Paraview), and meteorological forcing files (.h5 files can be accessed through h5py python package and HDFView open source software). 2) model_outputsIncludes ATS simulation outputs relevant to this study. Time series of spatially integrated or averaged variables (e.g., streamflow, water table depth) are provided as CSV files. Select spatial fields (e.g., ponded depth and water table depth) are saved as pickled Python objects to reduce file size, and can be accessed through pickle package in Python. Key geometry objects from Watershed Workflow—such as the surface mesh and river tree—are also included to support analysis of streamflow persistency and expansion–contraction dynamics. These files can also be accessed through Watershed Workflow Python package.3) model_evaluationProvides observed streamflow time series and field survey-based flow regime classifications used to evaluate model performance. Jupyter notebooks for processing ATS outputs and comparing model predictions with observations to build confidence in the model prior to scientific analysis are also included.4) Q_L_relationshipsContains workflows for generating time series of discharge, active network length, and related hydrologic variables used in the stream network expansion–contraction analysis. Includes routines for delineating baseflow-dominated periods. For each catchment, notebooks and processed data (as pickled DataFrames accessed through Pandas Python package) are provided. 5) figure_scriptsProvides the Jupyter notebooks used to generate the figures presented in the paper.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Understanding the surface wave characteristics using 2D particle-in-cell simulation and deep neural network

Here, the characteristics of the surface waves along the interface between a plasma and a dielectric material have been investigated using kinetic particle-in-cell simulations. A microwave source of GHz frequency has been used to trigger the surface wave in the system. The outcome indicates that the surface wave gets excited along the interface of plasma and the dielectric tube and appears as light and dark patterns in the electric field profiles. The dependency of radiation pressure on the dielectric permittivity and supplied input frequency has been investigated. Further, we assessed the capabilities of neural networks to predict the radiation pressure for a given system. The proposed deep neural network model is aimed at developing accurate and efficient data-driven plasma surface wave devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Distributed Macroscopic Traffic Simulation with Open Traffic Models

This paper presents OTM-MPI, an extension of the Open Traffic Models platform (OTM) for running macroscopic traffic simulations in high-performance computing environments. OTM-MPI represents the first open-source, distributed-memory, macroscopic simulation model developed for modern high performance parallel machines and large networks. Macroscopic simulations are appropriate for studying regional traffic scenarios when aggregate trends are of interest, rather than individual vehicle traces. They are also appropriate for studying the routing behavior of classes of vehicles, such as app-informed vehicles. The network partitioning was performed with METIS. Inter-process communication was done with MPI (message-passing interface). Results are provided for two networks: one realistic network which was obtained from Open Street Maps for Chattanooga, TN, and another larger synthetic grid network. The software recorded a speedups of 198x using 256 cores for Chattanooga, and 475x with 1,024 cores for the synthetic network.

macro-scopic traffic simulation↗