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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Developing Data-Driven Synthetic Infrastructure Models for Resilience Analysis

Research on infrastructure resilience has produced promising methods to simulate and optimize complex networks to improve performance. However, restrictions on sharing infrastructure models and the steep cost of developing and maintaining infrastructure models presents a roadblock to adoption. To overcome this limitation, this research focuses on methods to create data-driven infrastructure models that will help improve infrastructure resilience and security. The analysis couples incomplete utility data, geospatial data, machine learning, and synthetic network generation methods to rapidly develop and update infrastructure models. The methods are validated using realistic utility models and site-specific data, with a focus on Puerto Rico due to its unique infrastructure challenges and available data. This research highlights promising opportunities for the use of synthetic network generation and machine learning to create infrastructure models when very little data is available. Results demonstrate that hybrid methods, which combine sparse utility data with synthetic models, can enhance model accuracy, and machine learning can predict model attributes using training data from other models. However, the complexity of infrastructure systems means that even minor changes in network connectivity can significantly impact simulation results. Resilience analysis using synthetic infrastructure models shows that while some system behaviors are preserved, the magnitude of disruptions may not be accurately represented, indicating the need for more research and validation before using synthetic models for critical infrastructure investment decisions. The framework outlined in this report represents a significant advance to infrastructure model development and could be applied to additional domains and sites. Future research will continue to streamline and validate methods to help reduce roadblocks to resilience analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1990

Optical interconnections and networks; Proceedings of the Meeting, The Hague, Netherlands, Mar. 14, 15, 1990

The conference presents papers on interconnections, clock distribution, neural networks, and components and materials. Particular attention is given to a comparison of optical and electrical data interconnections at the board and backplane levels, a wafer-level optical interconnection network layout, an analysis and simulation of photonic switch networks, and the integration of picosecond GaAs photoconductive devices with silicon circuits for optical clocking and interconnects. Consideration is also given to the optical implementation of neural networks, invariance in an optoelectronic implementation of neural networks, and the recording of reversible patterns in polymer lightguides.

Bartelt, Hartmut↗
DOE OSTI2021

Cyber-Power Co-Simulation for End-to-End Synchrophasor Network Analysis and Applications

The resiliency, reliability and security of the next generation cyber-power smart grid depend upon efficiently leveraging advanced communication and computing technologies. Also, developing real-time data-driven applications is critical to enable wide-area monitoring and control of the cyber-power grid given high-resolution data from Phasor Measurement Units (PMUs). North American Synchrophasor Initiative Network (NASPlnet) provides guidance for PMU data exchanges. With the advancement in networking and grid operation, it is necessary to evaluate the performance of different data flow architectures suggested by NASPInet and analyze the impact on applications. Therefore, we need a cyber-power co-simulation framework that supports very large-scale co-simulation capable of running in parallel, high-performance computing platforms and capturing real-life network behavior. This work presents an end-to-end automated and user-driven cyber-power co-simulation using NS3 to model communication networks, GridPACK to model the power grid, and HELICS as a co-simulation engine. Comparative analysis of latency in synchrophasor networks and a performance evaluation of a power system stabilizer application utilizing PMU data in an IEEE 39 bus test system is presented using this cosimulation testbed.

Mustafa, Hussain M.↗
DOE OSTI2024

Do not forget the electrons: Extending moderately-sized nuclear networks for multidimensional hydrodynamic codes

Context.Nuclear networks are widely used coupled with hydrodynamical simulations of explosive scenarios to account for the change of nuclear species and energy generation rate due to nuclear reactions. In this way, there is a feedback mechanism between the hydrodynamical state and the nuclear processes. Unfortunately, the timescale of nuclear reactions is orders of magnitude smaller than the dynamical timescale that drives hydrodynamical simulations. Therefore, these nuclear networks are usually very small, reduced in most cases to a dozen elements, especially when simulations are carried out in more than one dimension. Aims.We present here an extended nuclear network, with 90 species, designed for being coupled with hydrodynamic simulations, which includes neutrons, protons, electrons, positrons, and the corresponding neutrino and anti-neutrino emission. This network is also coupled with temperature, making it extremely robust and, together with its size, unique of its kind. The inclusion of electron captures on free protons makes the network very appropriate for multidimensional studies of Type Ia supernova explosions, especially when the exploding object is a massive white dwarf. Methods.We perform several tests that are relevant to simulate explosive scenarios, such as Type Ia supernovae and core-collapse supernovae. We compare the results of the 90 nuclei network with a standardα-chain network with 14 elements to evaluate the differences in the energy generation rate. We also evaluate the relevance of including the electrons in the network in terms of generated yields and how it affects the pressure of a degenerate fluid such as that of white dwarfs. The results obtained with the 90-nuclei network have been verified with a much larger 2000-nuclei network built from REACLIB (WinNet), in terms of nuclear energy generation rate, pressure, and produced yields. Results.The results obtained with the proposed medium-sized network compare fairly well, to a few percent, with those computed withWinNetin scenarios reproducing the gross physical conditions of current Type Ia supernova explosion models. In those cases where the carbon and oxygen fuel ignites at high density, the high-temperature plateau typical of the nuclear statistical equilibrium regime is well defined and stable, allowing large integration time steps. We show that the inclusion of electron captures on free protons substantially improves the estimation of the electron fraction of the mixture. Therefore, the pressure is better determined than in networks where electron captures are excluded, which will ultimately lead to more reliable hydrodynamic models. Explosive combustion of helium at low density, occurring near the surface layer of a white dwarf, is also better described with the proposed network, which gives nuclear energy generation rates much closer toWinNetthan typical reduced alpha networks. Conclusions.A nuclear network withN= 90 species, including electrons, aimed at multidimensional calculations of supernova explosions is described and verified. The proposed network is suitable for the study of Type Ia supernova explosions because it provides better values of pressure and electron abundance than other existing networks with smaller or even a similar size but without including electron capture processes.

Astronomy & Astrophysics↗
NASA NTRS1993

Distributed simulation using a real-time shared memory network

The Advanced Control Technology Branch of the NASA Lewis Research Center performs research in the area of advanced digital controls for aeronautic and space propulsion systems. This work requires the real-time implementation of both control software and complex dynamical models of the propulsion system. We are implementing these systems in a distributed, multi-vendor computer environment. Therefore, a need exists for real-time communication and synchronization between the distributed multi-vendor computers. A shared memory network is a potential solution which offers several advantages over other real-time communication approaches. A candidate shared memory network was tested for basic performance. The shared memory network was then used to implement a distributed simulation of a ramjet engine. The accuracy and execution time of the distributed simulation was measured and compared to the performance of the non-partitioned simulation. The ease of partitioning the simulation, the minimal time required to develop for communication between the processors and the resulting execution time all indicate that the shared memory network is a real-time communication technique worthy of serious consideration.

Simon, Donald L.↗
NASA NTRS2007

An Overview of the Distributed Space Exploration Simulation (DSES) Project

This paper describes the Distributed Space Exploration Simulation (DSES) Project, a research and development collaboration between NASA centers which investigates technologies, and processes related to integrated, distributed simulation of complex space systems in support of NASA's Exploration Initiative. In particular, it describes the three major components of DSES: network infrastructure, software infrastructure and simulation development. With regard to network infrastructure, DSES is developing a Distributed Simulation Network for use by all NASA centers. With regard to software, DSES is developing software models, tools and procedures that streamline distributed simulation development and provide an interoperable infrastructure for agency-wide integrated simulation. Finally, with regard to simulation development, DSES is developing an integrated end-to-end simulation capability to support NASA development of new exploration spacecraft and missions. This paper presents the current status and plans for these three areas, including examples of specific simulations.

Crues, Edwin Z.↗
DOE OSTI2026

Approximation of refrigerant thermophysical properties using neural networks to speed up transient thermofluid simulations

Accurate and efficient evaluations of refrigerant thermophysical properties and their partial derivatives are essential for transient simulations of thermofluid systems, where several computations need to be executed at each integration time step. Since the utilization of an Equation of State for retrieving properties based on a pair of independent inputs typically involves numerical iterations in solution procedures, when the input variables differ from the refrigerant state variables employed in dynamic models, a variety of approaches including lookup table interpolation and curve fitting have been developed to explicitly approximate these properties based on the state variables, and consequently eliminate internal iterations. This paper presents an alternative method that exploits derivative-informed neural networks to model refrigerant properties explicitly from inputs of pressure and enthalpy, while ensuring consistent partial derivatives generated by differentiating the neural networks. Computational speed and accuracy of the proposed approach are demonstrated via transient simulations of a discretized heat exchanger model in Modelica, and comparisons against other property evaluation routines. Simulation results indicate that the proposed approach can realize a significant speedup with negligible discrepancies in predicted transients. The method is implemented in an open-source Modelica library.

Ma, Jiacheng↗
DOE OSTI2014

Towards a unified description of the hydrogen bond network of liquid water: A dynamics based approach

The definition of a hydrogen bond (H-bond) is intimately related to the topological and dynamic properties of the hydrogen bond network within liquid water. The development of a universal H-bond definition for water is an active area of research as it would remove many ambiguities in the network properties that derive from the fixed definition employed to assign whether a water dimer is hydrogen bonded. This work investigates the impact that an electronic-structure based definition, an energetic, and a geometric definition of the H-bond has upon both topological and dynamic network behavior of simulated water. In each definition, the use of a cutoff (either geometric or energetic) to assign the presence of a H-bond leads to the formation of transiently bonded or broken dimers, which have been quantified within the simulation data. The relative concentration of transient species, and their duration, results in two of the three definitions sharing similarities in either topological or dynamic features (H-bond distribution, H-bond lifetime, etc.), however no two definitions exhibit similar behavior for both classes of network properties. In fact, two networks with similar local network topology (as indicated by similar average H-bonds) can have dramatically different global network topology (as indicated by the defect state distributions) and altered H-bond lifetimes. A dynamics based correction scheme is then used to remove artificially transient H-bonds and to repair artificially broken bonds within the network such that the corrected network exhibits the same structural and dynamic properties for two H-bond definitions (the properties of the third definition being significantly improved). The algorithm described represents a significant step forward in the development of a unified hydrogen bond network whose properties are independent of the original hydrogen bond definition that is employed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

ON FIBRILS AND FIELD LINES: THE NATURE OF Hα FIBRILS IN THE SOLAR CHROMOSPHERE

Observations of the solar chromosphere in the line core of the Hα line show dark elongated structures called fibrils that show swaying motion. We performed a three-dimensional radiation-MHD simulation of a network region and computed synthetic Hα images from this simulation to investigate the relation between fibrils and the magnetic field lines in the chromosphere. The periods, amplitudes, and phase speeds of the simulated fibrils are consistent with observations. We find that some fibrils trace out the same field line along the fibril’s length, while other fibrils sample different field lines at different locations along their length. Fibrils sample the same field lines on a timescale of ∼200 s. This is shorter than their own lifetime. Fibril-threading field lines carry slow-mode waves, as well as transverse waves propagating with the Alfvén speed. Transverse waves propagating in opposite directions cause an interference pattern with complex apparent phase speeds. The relationship between fibrils and field lines is governed by constant migration and swaying of the field lines, their mass loading and draining, and their visibility in Hα. Field lines are visible where they lie close to the optical depth unity surface. The location of the latter is at a height at which the column mass reaches a certain fixed value. The visibility of the field line is thus determined by its own mass density and by the mass density of the material above it. Using the swaying motion of fibrils as a tracer of chromospheric transverse oscillations must be done with caution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Scaling neural simulations in STACS

Abstract As modern neuroscience tools acquire more details about the brain, the need to move towards biological-scale neural simulations continues to grow. However, effective simulations at scale remain a challenge. Beyond just the tooling required to enable parallel execution, there is also the unique structure of the synaptic interconnectivity, which is globally sparse but has relatively high connection density and non-local interactions per neuron. There are also various practicalities to consider in high performance computing applications, such as the need for serializing neural networks to support potentially long-running simulations that require checkpoint-restart. Although acceleration on neuromorphic hardware is also a possibility, development in this space can be difficult as hardware support tends to vary between platforms and software support for larger scale models also tends to be limited. In this paper, we focus our attention on Simulation Tool for Asynchronous Cortical Streams (STACS), a spiking neural network simulator that leverages the Charm++ parallel programming framework, with the goal of supporting biological-scale simulations as well as interoperability between platforms. Central to these goals is the implementation of scalable data structures suitable for efficiently distributing a network across parallel partitions. Here, we discuss a straightforward extension of a parallel data format with a history of use in graph partitioners, which also serves as a portable intermediate representation for different neuromorphic backends. We perform scaling studies on the Summit supercomputer, examining the capabilities of STACS in terms of network build and storage, partitioning, and execution. We highlight how a suitably partitioned, spatially dependent synaptic structure introduces a communication workload well-suited to the multicast communication supported by Charm++. We evaluate the strong and weak scaling behavior for networks on the order of millions of neurons and billions of synapses, and show that STACS achieves competitive levels of parallel efficiency.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1979

SAINT: A combined simulation language for modeling man-machine systems

SAINT (Systems Analysis of Integrated Networks of Tasks) is a network modeling and simulation technique for design and analysis of complex man machine systems. SAINT provides the conceptual framework for representing systems that consist of discrete task elements, continuous state variables, and interactions between them. It also provides a mechanism for combining human performance models and dynamic system behaviors in a single modeling structure. The SAINT technique is described and applications of the SAINT are discussed.

Seifert, D. J.↗
DOE OSTI2026

Scaling kinetic Monte-Carlo simulations of grain growth with combined convolutional and graph neural networks

Graph neural networks (GNN) have emerged as a promising machine learning method for microstructure simulations such as grain growth. However, accurate modeling of realistic grain boundary networks requires large simulation cells, which GNN has difficulty scaling up to. To alleviate the computational costs and memory footprint of GNN, we suggest a hybrid architecture combining a convolutional neural network (CNN) based bijective autoencoder to compress the spatial dimensions, and a GNN that evolves the microstructure in the latent space of reduced spatial sizes. Our results demonstrate that the new design significantly reduces computational costs with using fewer message passing layer (from 12 down to 3) compared with GNN alone. The reduction in computational cost becomes more pronounced as the spatial size increases, indicating strong computational scalability. For the largest mesh evaluated (160 3 ), our method reduces memory usage and runtime in inference by 117× and 115×, respectively, compared with GNN-only baseline. More importantly, it shows higher accuracy and stronger spatiotemporal capability than the GNN-only baseline, especially in long-term testing. Such combination of scalability and accuracy is essential for simulating realistic material microstructures over extended time scales. The improvements can be attributed to the bijective autoencoder’s ability to compress information losslessly from spatial domain into a high dimensional feature space, thereby producing more expressive latent features for the GNN to learn from, while also contributing its own spatiotemporal modeling capability. Training data are generated from stochastic grain growth simulations, providing realistic variability for learning robust microstructure evolution. Comprehensive system validation confirms that the model is accurate, robust, and scalable.

36 MATERIALS SCIENCE↗
NASA NTRS2006

Simulation Modeling and Performance Evaluation of Space Networks

In space exploration missions, the coordinated use of spacecraft as communication relays increases the efficiency of the endeavors. To conduct trade-off studies of the performance and resource usage of different communication protocols and network designs, JPL designed a comprehensive extendable tool, the Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE). The design and development of MACHETE began in 2000 and is constantly evolving. Currently, MACHETE contains Consultative Committee for Space Data Systems (CCSDS) protocol standards such as Proximity-1, Advanced Orbiting Systems (AOS), Packet Telemetry/Telecommand, Space Communications Protocol Specification (SCPS), and the CCSDS File Delivery Protocol (CFDP). MACHETE uses the Aerospace Corporation s Satellite Orbital Analysis Program (SOAP) to generate the orbital geometry information and contact opportunities. Matlab scripts provide the link characteristics. At the core of MACHETE is a discrete event simulator, QualNet. Delay Tolerant Networking (DTN) is an end-to-end architecture providing communication in and/or through highly stressed networking environments. Stressed networking environments include those with intermittent connectivity, large and/or variable delays, and high bit error rates. To provide its services, the DTN protocols reside at the application layer of the constituent internets, forming a store-and-forward overlay network. The key capabilities of the bundling protocols include custody-based reliability, ability to cope with intermittent connectivity, ability to take advantage of scheduled and opportunistic connectivity, and late binding of names to addresses. In this presentation, we report on the addition of MACHETE models needed to support DTN, namely: the Bundle Protocol (BP) model. To illustrate the use of MACHETE with the additional DTN model, we provide an example simulation to benchmark its performance. We demonstrate the use of the DTN protocol and discuss statistics gathered concerning the total time needed to simulate numerous bundle transmissions

network protocols↗
NASA NTRS2010

Preliminary Investigation of Civil Tiltrotor in NextGen Airspace

Presentation intro: Tiltrotor aircraft have long been envisioned as being a potentially viable means of commercial aviation transport. Preliminary results from an ongoing study into the operational and technological considerations of Civil Tiltrotor (CTR) operation in the Next Generation airspace, circa the 2025 time-frame, are presented and discussed. In particular, a fleet of CTR aircraft has been conceptually designed. The performance characteristics of this CTR fleet was subsequently translated into BADA (Base of Aircraft DAta) models that could be used as input to emulate CTR aircraft operations in the ACES and AvTerminal airspace and terminal area simulation tools. A network of nine North-Eastern corridor airports is the focus of the airspace simulation effort; the results from this airport network viII then be extrapolated to provide insights into systemic impact of CTRs on the National Airspace System (NAS). Future work will also be detailed as to attempts to model the systemic effects of noise and emissions from this fleet of new aircraft as well as assess their leveraged impact on public service missions, in time of need, such as major regional/national disaster relief efforts. The ideal outcome of this study is a set of results whereby Next Gen airspace CONOPs can be refined to reflect potential CTR capabilities and, conversely, CTR technology development efforts can be better informed as to key performance requirement thresholds needed to be met in order to successfully introduce these aircraft into civilian aviation operation.

Young, Larry A.↗
NASA NTRS2011

Delay and Disruption Tolerant Networking MACHETE Model

To verify satisfaction of communication requirements imposed by unique missions, as early as 2000, the Communications Networking Group at the Jet Propulsion Laboratory (JPL) saw the need for an environment to support interplanetary communication protocol design, validation, and characterization. JPL's Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE), described in Simulator of Space Communication Networks (NPO-41373) NASA Tech Briefs, Vol. 29, No. 8 (August 2005), p. 44, combines various commercial, non-commercial, and in-house custom tools for simulation and performance analysis of space networks. The MACHETE environment supports orbital analysis, link budget analysis, communications network simulations, and hardware-in-the-loop testing. As NASA is expanding its Space Communications and Navigation (SCaN) capabilities to support planned and future missions, building infrastructure to maintain services and developing enabling technologies, an important and broader role is seen for MACHETE in design-phase evaluation of future SCaN architectures. To support evaluation of the developing Delay Tolerant Networking (DTN) field and its applicability for space networks, JPL developed MACHETE models for DTN Bundle Protocol (BP) and Licklider/Long-haul Transmission Protocol (LTP). DTN is an Internet Research Task Force (IRTF) architecture providing communication in and/or through highly stressed networking environments such as space exploration and battlefield networks. Stressed networking environments include those with intermittent (predictable and unknown) connectivity, large and/or variable delays, and high bit error rates. To provide its services over existing domain specific protocols, the DTN protocols reside at the application layer of the TCP/IP stack, forming a store-and-forward overlay network. The key capabilities of the Bundle Protocol include custody-based reliability, the ability to cope with intermittent connectivity, the ability to take advantage of scheduled and opportunistic connectivity, and late binding of names to addresses.

Segui, John S.↗
DOE OSTI2021

Exactness of OPF Relaxation on Three-phase Radial Networks with Delta Connections

Simulations have shown that while semi-definite relaxations of AC optimal power flow (AC-OPF) on three-phase radial networks with only wye connections tend to be exact, the presence of delta connections seem to render them inexact. This paper shows that such inexactness originates from the non-uniqueness of relaxation solutions and numerical errors amplified by the non-uniqueness. This finding motivates two algorithms to recover the exact solution of AC-OPF in unbalanced distribution networks featuring both wye and delta connections. In simulations using IEEE 13, 37 and 123-bus systems, the proposed algorithms provide exact optimal solutions up to numerical precision.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Catalyst: Fast and flexible modeling of reaction networks

We introduce Catalyst.jl, a flexible and feature-filled Julia library for modeling and high-performance simulation of chemical reaction networks (CRNs). Catalyst supports simulating stochastic chemical kinetics (jump process), chemical Langevin equation (stochastic differential equation), and reaction rate equation (ordinary differential equation) representations for CRNs. Through comprehensive benchmarks, we demonstrate that Catalyst simulation runtimes are often one to two orders of magnitude faster than other popular tools. More broadly, Catalyst acts as both a domain-specific language and an intermediate representation for symbolically encoding CRN models as Julia-native objects. This enables a pipeline of symbolically specifying, analyzing, and modifying CRNs; converting Catalyst models to symbolic representations of concrete mathematical models; and generating compiled code for numerical solvers. Leveraging ModelingToolkit.jl and Symbolics.jl, Catalyst models can be analyzed, simplified, and compiled into optimized representations for use in numerical solvers. Finally, we demonstrate Catalyst’s broad extensibility and composability by highlighting how it can compose with a variety of Julia libraries, and how existing open-source biological modeling projects have extended its intermediate representation.

59 BASIC BIOLOGICAL SCIENCES↗