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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Development of HELICS-Based High-Performance Cyber-Physical Co-Simulation Framework for Distributed Energy Resources Applications

The rapid growth of distributed energy resources (DERs) has prompted increasing interest in the monitoring and control of DERs through hybrid smart grid communications resulting in the typical smart grid cyber-physical system. To fully understand the interdependency between them, we propose to integrate the Network Simulator 3 (NS3) into the High Engine for Large-Scale Infrastructure Co-Simulation (HELICS), a new open-source, cyber-physical-energy co-simulation platform. This paper aims to the development and case study of the HELICS-based high performance distribution-communication co-simulation framework for the DER coordination. The novel co-simulation framework for the NS3 integrating into the HELICS is developed. The DER monitoring application about hybrid smart grid communication network design is simulated and validated on this proposed HELICS-based cyber-physical co-simulation platform.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2015

On the rejection-based algorithm for simulation and analysis of large-scale reaction networks

Stochastic simulation for in silico studies of large biochemical networks requires a great amount of computational time. We recently proposed a new exact simulation algorithm, called the rejection-based stochastic simulation algorithm (RSSA) [Thanh et al., J. Chem. Phys. 141(13), 134116 (2014)], to improve simulation performance by postponing and collapsing as much as possible the propensity updates. In this paper, we analyze the performance of this algorithm in detail, and improve it for simulating large-scale biochemical reaction networks. We also present a new algorithm, called simultaneous RSSA (SRSSA), which generates many independent trajectories simultaneously for the analysis of the biochemical behavior. SRSSA improves simulation performance by utilizing a single data structure across simulations to select reaction firings and forming trajectories. The memory requirement for building and storing the data structure is thus independent of the number of trajectories. The updating of the data structure when needed is performed collectively in a single operation across the simulations. The trajectories generated by SRSSA are exact and independent of each other by exploiting the rejection-based mechanism. We test our new improvement on real biological systems with a wide range of reaction networks to demonstrate its applicability and efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

CP‐SyNet: A tool for generating customised cyber‐power synthetic network for distribution systems with distributed energy resources

Abstract The integration of distributed energy resources and advancement in information technology has enabled the transition of traditional power distribution systems to active cyber‐physical distribution systems. A growing amount of research has been done on the modelling, analysis, and optimisation of power distribution system behaviour. However, existing publicly available distribution test feeders are limited in numbers and have minimal features. Furthermore, these test feeders do not include cyber models and are not customisable. To bridge this gap, we propose and develop Cyber‐physical synthetic distribution system network (CP‐SyNet), a tool for generating customisable cyber‐physical synthetic distribution test feeders. CP‐SyNet generates three‐phase unbalanced test feeders according to users' requirements, while simultaneously considering both the cyber side and the physical side of the network for cyber‐physical analysis. The physical test network is developed using a graph‐theoretical approach that employs information from existing test feeders. The cyber side considers an equivalent communication network by transforming the physical topology into possible and feasible simulated network. Two examples are presented to demonstrate the feasibility of the proposed framework to generate cyber‐physical test feeders.

Wang, Lusha↗
DOE OSTI2022

Quantum Networks for Resilient Power Grids: Theory and Simulated Evaluation

Quantum networks are considered the future of secure communication in the coming quantum era. Yet there lack significant efforts on developing practical quantum networks for power grids. Here, in this paper, we establish a quantum network-based power grid (QNetGrid) framework and develops a real-time, reliable, flexible, programmable, and cost-effective QNetGrid software testbed containing repeater-based quantum communication, quantum routing, real software-defined networking (SDN) switches, and real-time networked microgrids (NMs) operations. It makes the following contributions: 1) a repeater and routing based quantum network simulator (QNSim) is developed, 2) repeaters with and without quantum memories are respectively simulated in QNSim, 3) different routing scenarios in QNetGrid are investigated, 4) a real-time QNetGrid software testbed is built in RTDS incorporating QNSim and real SDN switches, and 5) various test cases are designed, and experimental results produced with the QNetGrid testbed provide valuable insights for building quantum networks in power grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Hybrid Communication Architectures for Distributed Smart Grid Applications

Wired and wireless communications both play an important role in the blend of communications technologies necessary to enable future smart grid communications. Hybrid networks exploit independent mediums to extend network coverage and improve performance. However, whereas individual technologies have been applied in simulation networks, as far as we know there is only limited attention that has been paid to the development of a suite of hybrid communication simulation models for the communications system design. Hybrid simulation models are needed to capture the mixed communication technologies and IP address mechanisms in one simulation. To close this gap, we have developed a suite of hybrid communication system simulation models to validate the critical system design criteria for a distributed solar Photovoltaic (PV) communications system, including a single trip latency of 300 ms, throughput of 9.6 Kbps, and packet loss rate of 1%. In conclusion, the results show that three low-power wireless personal area network (LoWPAN)-based hybrid architectures can satisfy three performance metrics that are critical for distributed energy resource communications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations

Here, we discuss the development of an agent-based modelling software development kit, and the implementation and validation of a model using it that integrates dynamic simulation of travel demand, network supply and network operations. A description is given of the core utilities in the kit: a parallel discrete event engine, interprocess exchange engine, and memory allocator, as well as a number of ancillary utilities: visualization library, database IO library, and scenario manager. The overall framework emphasizes the design goals of: generality, code agility, and high performance. This framework allows the modeling of several aspects of transportation system that are typically done with separate stand-alone software applications, in a high-performance and extensible manner. The issue of integrating such models as dynamic traffic assignment and disaggregate demand models has been a long standing issue for transportation modelers. The integrated approach shows a possible way to resolve this difficulty. The simulation model built from the POLARIS framework is a single, shared-memory process for handling all aspects of the integrated urban simulation. The resulting gains in computational efficiency and performance allow planning models to be extended to include previously separate aspects of the urban system, enhancing the utility of such models from the planning perspective. Lastly, initial tests with case studies involving traffic management center impacts on various network events such as accidents, congestion and weather events, show the potential of the system.

42 ENGINEERING↗
DOE OSTI2024

Automatic Extraction of Network Configurations for Realistic Simulation and Validation

Popular HPC network interconnection simulators such as SST Macro provide a variety of configurable parameters to explore the design space of hardware components such as network links and switches. While such knobs provide flexibility to explore design trade-offs for novel hardware, manually configuring simulations for existing hardware to focus on topology exploration can be cumbersome and error-prone, leading to widely inaccurate simulations. This challenge is compounded when specifications of various (proprietary) technologies are not readily available or are intentionally omitted. In this work, we provide a methodology to automatically tune the simulation configuration of the multiple network models running within SST Macro using Bayesian optimization. We perform this optimization in the context of multiple messaging regimes (i.e., small to large and latency to bandwidth-bound messages) and provide a detailed analysis of the simulation error for four systems. With our automated framework, we achieve a 5x improvement in accuracy over best-effort configurations based on available hardware specifications.

Suetterlein, Joshua D.↗
DOE OSTI2022

Active learning of chemical reaction networks via probabilistic graphical models and Boolean reaction circuits

Discerning networks of many reactions among multiple interconverting species is challenging. Here, we present a reaction network identification methodology. Our methodology enumerates all stoichiometrically and chemically feasible reactions and requires statistical evidence from effluent concentrations for the inclusion or exclusion of each from the reaction network, contrasting with the commonly seen incremental approach and other work of relying heavily upon chemical intuition and assuming the reactions occurring. Using graph theory alongside an active learning design of experiments that propose maximally informative feeds, we identify the underlying reaction network with minimal laboratory runs. Here, we introduce chemistry-probabilistic graphical modeling and Boolean reaction circuits to statistically quantify which reactions occur from effluent concentrations. Our methodology accurately discerns active reactions, as showcased upon a laboratory network of cross-ketonization of furoic and lauric acid and validated upon simulated networks of thermal and CO 2 -assisted ethane dehydrogenation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Deep‐learning‐based power distribution network switch action identification leveraging dynamic features of distributed energy resources

This study proposes a data‐driven approach for identifying switch actions in power distribution networks. Simulated micro‐phasor measurement unit data is utilised to train a convolutional neural network (CNN) model. The trained CNN model can identify multi‐phase multi‐switch actions. Instead of working as a blackbox, the proposed approach extracts the features from the hidden layers of the trained CNN for engineering interpretation and error check. In addition, a random‐forest‐based feature ranking algorithm is proposed to identify the most important features. The proposed approach is validated on the IEEE 123‐node feeder modelled in GridLAB‐D. The CNN model is built and trained using TensorFlow. The proposed approach achieves 96.57% identification accuracy.

42 ENGINEERING↗
DOE OSTI2022

Multiplicative Shot-Noise: A New Route to Stability of Plastic Networks

Fluctuations of synaptic weights, among many other physical, biological, and ecological quantities, are driven by coincident events of two “parent” processes. Here we propose a multiplicative shot-noise model that can capture the behaviors of a broad range of such natural phenomena, and analytically derive an approximation that accurately predicts its statistics. We apply our results to study the effects of a multiplicative synaptic plasticity rule that was recently extracted from measurements in physiological conditions. Using mean-field theory analysis and network simulations, we investigate how this rule shapes the connectivity and dynamics of recurrent spiking neural networks. The multiplicative plasticity rule is shown to support efficient learning of input stimuli, and it gives a stable, unimodal synaptic-weight distribution with a large fraction of strong synapses. The strong synapses remain stable over long times but do not “run away.” Our results suggest that the multiplicative shot-noise offers a new route to understand the tradeoff between flexibility and stability in neural circuits and other dynamic networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Robust Event Simulation Variants Endowed to Ns-3 for General Exploration

Sandia's ns-3 contributions are modifications to the open source ns-3 network simulator that enable simulation speedup. One improvement, for example, removes the need for simulating the transmission and receipt of packets between nodes that are too distant to actually be able to communicate. In large-scale simulations this optimization has shown significant gains in performance. Additional contributions will pave the way for parallel discrete simulation (PDES) in ns-3. SAND2020-12456 O 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.

Dickson, Joseph↗
DOE OSTI2018

Effective restoration strategies of interdependent power system and communication network

The power system is continuously evolving towards a smarter grid by adding intelligent electrical devices that leverage communication technologies to provide visualisation, control and automation. The integration of power system and communication network has brought challenges into the resilience of interdependent networks. Following an outage or blackout, system operators cannot only restore one network while simply ignoring the interconnection with the other network. Effective restoration strategies that involve co‐ordination between power system and communication network have become critical in enhancing the resilience. An optimisation‐based restoration strategy is developed to coordinate the restoration actions between two networks. The problem is modelled as a mixed integer linear programming problem, with the objective of activating every node in both networks with the minimum number of activation/energisation of branches. The developed strategies are tested on the modified IEEE 14‐bus system incorporating communication network. Simulation results demonstrate that the coordinated restoration strategies are effective and critical in the successful recovery of interdependent networks. The developed model could also provide insights in network design and planning to enhance the resilience of interdependent power system and communication network.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Topology-Aware Performance Optimization and Modeling of Adaptive Mesh Refinement Codes for Exascale

Here, we introduce a topology-aware performance optimization and modeling workflow for AMR simulation that includes two new modeling tools, ProgrAMR and Mota Mapper, which interface with the BoxLib AMR framework and the SSTmacro network simulator. ProgrAMR allows us to generate and model the execution of task dependency graphs from high-level specifications of AMR-based applications, which we demonstrate by analyzing two example AMR-based multigrid solvers with varying degrees of asynchrony. Mota Mapper generates multiobjective, network topology-aware box mappings, which we apply to optimize the data layout for the example multigrid solvers. While the sensitivity of these solvers to layout and execution strategy appears to be modest for balanced scenarios, the impact of better mapping algorithms can be significant when performance is highly constrained by network hop latency. Furthermore, we show that network latency in the multigrid bottom solve is the main contributing factor preventing good scaling on exascale-class machines.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

High Fidelity Simulations of Large-scale Wireless Networks (Part II - FY2017)

The ability to simulate wireless networks at large-scale for meaningful amount of time is considerably lacking in today's network simulators. For this reason, many published work in this area often limit their simulation studies to less than a 1,000 nodes and either over-simplify channel characteristics or perform studies over time scales much less than a day. In this report, we show that one can overcome these limitations and study problems of high practical consequence. This work presents two key contributions to high fidelity simulation of large-scale wireless networks: (a) wireless simulations can be sped up by more than 100X in runtime using ideas from spatial indexing algorithms and clipping of negligible signals and (b) clustering and task-oriented programming paradigm can be used to reduce inter- process communication in a parallel discrete event simulation resulting in a better scaling efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Self-consistent core-pedestal transport simulations with neural network accelerated models

Fusion whole device modeling simulations require comprehensive models that are simultaneously physically accurate, fast, robust, and predictive. In this paper we describe the development of two neural-network (NN) based models as a means to perform a snon-linear multivariate regression of theory-based models for the core turbulent transport fluxes, and the pedestal structure. Specifically, we find that a NN-based approach can be used to consistently reproduce the results of the TGLF and EPED1 theory-based models over a broad range of plasma regimes, and with a computational speedup of several orders of magnitudes. These models are then integrated into a predictive workflow that allows prediction with self-consistent core-pedestal coupling of the kinetic profiles within the last closed flux surface of the plasma. Finally, the NN paradigm is capable of breaking the speed-accuracy trade-off that is expected of traditional numerical physics models, and can provide the missing link towards self-consistent coupled core-pedestal whole device modeling simulations that are physically accurate and yet take only seconds to run.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Trans-oceanic Remote Power Hardware-in-the-Loop: Multi-site Hardware, Integrated Controller, and Electric Network Co-simulation

Electric system operators are increasingly concerned with the potential system-wide impacts of the large-scale integration of distributed energy resources (DERs) including voltage control, protection coordination, and equipment wear. This prompts a need for new simulation techniques that can simultaneously capture all the components of these large integrated smart grid systems. This paper describes a novel platform that combines three emerging research areas: power systems co-simulation, power hardware in the loop (PHIL) simulation, and lab-lab links. The platform is distributed, real-time capable, allows for easy internet-based connection from geographically-dispersed participants, and is software platform agnostic. We demonstrate its utility by studying real-time PHIL co-simulation of coordinated solar PV firming control of two inverters connected in multiple electric distribution network models, prototypical of U.S. and Australian systems. Here, the novel trans-pacific closed-loop system simulation was conducted in real-time using a power network simulator and physical PV/battery inverter at power at the National Renewable Energy Laboratory in Golden, CO, USA and a physical PV inverter at power at the Commonwealth Scientific and Industrial Research Organisation's Energy Centre in Newcastle, NSW, Australia. This capability enables smart grid researchers throughout the world to leverage their unique simulation capabilities for multi-site collaborations that can effectively simulate and validate emerging smart grid technology solutions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Nuclear fission reaction simulations in compact stars

Type-Ia supernovas are powerful stellar explosions that provide important distance indicators in cosmology. Recently, we proposed a new Type-Ia supernova mechanism that involves a nuclear fission chain-reaction in an isolated white dwarf [Phys. Rev. Lett. 126, 131101 (2021)]. Here, in this work, we perform novel reaction network simulations of the actinide-rich first solids in a cooling white dwarf. The network includes neutron-capture and fission reactions on a range of U and Th isotopes with various possible values for 235 U enrichment. We find, for modest 235 U enrichments, neutron capture on 238 U and 232 Th can breed additional fissile nuclei so that a significant fraction of all U and Th nuclei may fission during the chain reaction. Finally, we compute the energy release from the fission chain reaction for various uranium enrichments; a novel result that is a necessary input for thermal diffusion simulations of carbon ignition.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Spread Spectrum Time Domain Reflectometry (SSTDR) Digital Twin Simulation of Photovoltaic Systems for Fault Detection and Location

Utilizing spread spectrum time domain reflectometry (SSTDR) to detect, locate, and characterize faults in photovoltaic (PV) systems is examined in this paper. We present a method to obtain the model parameters that are needed to produce digital twin SSTDR responses for PV systems. The digital twin SSTDR responses could be used to predict faults within the PV systems. Here, the model parameters are the reflection and transmission coefficients at each impedance discontinuity in the PV system along with the propagation coefficients across each PV cable segment. We obtain model parameter by applying inverse modeling techniques to experimental SSTDR data associated with PV systems. Our model parameters can be used in any digital twin simulation method for modeling reflectometry in frequency-dependent and complex loads. For validation, we used the model parameters in a graph network simulation engine and adapted it to be used for SSTDR digital twin simulations in PV systems. We produced simulations for 0 to 10 PV modules connected in series. We also simulated SSTDR responses for open circuit disconnections in a PV setup containing 10 PV modules in series. Results show that all but one simulated disconnect locations match experimental disconnection locations of the same setup with an error of less than 5%.

14 SOLAR ENERGY↗