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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Development of a Discrete Event Simulation Model for Pyroprocessing Safeguardability

Advanced nuclear energy systems offer benefits for energy sustainability, independence, security, economic growth, and carbon emission reduction. Nuclear fuel fabricated for these systems utilize remotely-handled facilities for batch processing in hot cells. Because special nuclear material is in both different physical and chemical form than that of the contemporary fuel cycle, new challenges arise in terms of safeguardability. To that end, we are developing a system model for a commercial pyroprocessing facility that can also produce quantitative metrics to assess facility design. This paper presents the progress of a first build of a material throughput model using discrete event simulation for the fuel fabrication subsystem. The intent is to study the Type I error, or false alarm, as a metric for safeguardability. Initiating events that could cause false alarms arise from safeguards, safety, and security. False alarms can necessitate a facility shutdown until the anomaly is resolved by facility inspection. A high number would be costly for the facility operator both in terms of operation throughput goals and possible compensatory penalties. Therefore, different conceptual facility designs exhibit different frequencies for the false alarm per initiating event, and this can provide a measure of the safeguardability of each design. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Computer Science Research Needs for Parallel Discrete Event Simulation (PDES)

Historically, scientific computing efforts have demonstrated the clear need for, and effective use of, supercomputing with traditional time-stepped simulations. Nevertheless, there are several areas in the mission spaces of the U.S. Department of Energy and other agencies waiting to tap advanced computing research using a different, discrete event style of modeling, simulation, and analysis. These span a wide spectrum of applications including energy grid resilience, urban planning and policy, transportation science, building technologies, emergency response and planning, environmental impact analysis, computational epidemiology, Internet communications, cyber security, and cyber-physical systems, to name only a few. Even within traditional scientific applications, the role of discrete event modes of execution is increasing in the form of new event-based mathematical solvers such as quantized state integration methods and discrete-continuous hybrid system solvers. Co-design of advanced supercomputing hardware systems is another area that exploits discrete event simulation at its core for effective analyses. Complex systems, entity behaviors and interconnections play a significant role in all these applications, which are mapped to large-scale models with discrete event formulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A Study into the Benefits of Using Discrete Event Simulation in TRU and Mixed Low-Level Waste (MLLW) Disposal at Advanced Mixed Waste Treatment Plant (AMWTP) - 19536

As part of the 1995 Settlement agreement reached between the Idaho State Government and the Idaho National laboratory, it was agreed that the approximately 65,000 cubic metres of waste stored at the AMWTP site was to be shipped out of the state to the waste isolation pilot plant (WIPP) in New Mexico or a similar disposal facility by the end of December 2018. In June 2016, the operation of AMWTP was taken over by Fluor Idaho, a subsidiary of Fluor Inc. To improve their capability of processing all of the varied waste contained on the extensive site, Fluor Idaho requested the assistance of DBD Ltd. We were tasked with producing an all-encompassing operational research (OR) model/discrete event simulation (DES) model of the site, which is able to produce live end-date predictions based on current inventory and waste tracking against milestones. The DES model, is capable of predicting years worth of plant operation in a matter of minutes including the variation caused by such complexities as resource/staff availability, equipment breakdown and maintenance, prioritisation of different waste types, and varying shift patterns of both staff and facilities. The DES model can be run from its own bespoke dashboard, that can be customised to suit the audience it is being used to inform, producing clear key performance indicator (KPI) graphics and high-level overviews for senior management, down to drum by drum breakdowns of the predicted feed plans for each individual facility on the AMWTP site for a work planner to analyse and compare to the current operations. Simulation can produce these types of outputs for very low cost and low risk, as it avoids experimentation to find potential alternatives to the status-quo that could result in loss of production or time. These methods can also be used to produce cost benefit analyses for suggested processing options feeding into optioneering studies and cost reduction strategies. As a tool for plant optimisation and tracking, discrete event simulation can provide detailed results quickly and reliably. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Modeling Large-Scale Slim Fly Networks Using Parallel Discrete-Event Simulation

As supercomputers approach exascale performance, the increased number of processors translates to an increased demand on the underlying network interconnect. We present that the slim fly network topology, a new low-diameter, low-latency, and low-cost interconnection network, is gaining interest as one possible solution for next-generation supercomputing interconnect systems. In this article, we present a high-fidelity slim fly packet-level model leveraging the Rensselaer Optimistic Simulation System (ROSS) and Co-Design of Exascale Storage (CODES) frameworks. We validate the model with published work before scaling the network size up to an unprecedented 1 million compute nodes and confirming that the slim fly observes peak network throughput at extreme scale. In addition to synthetic workloads, we evaluate large-scale slim fly models with real communication workloads from applications in the Design Forward program with over 110,000 MPI processes. We show strong scaling of the slim fly model on an Intel cluster achieving a peak network packet transfer rate of 2.3 million packets per second and processing over 7 billion discrete events using 128 MPI tasks. Enabled by the strong performance capabilities of the model, we perform a detailed application trace and routing protocol performance study. Lastly, through analysis of metrics such as packet latency, hop count, and congestion, we find that the slim fly network is able to leverage simple minimal routing and achieve the same performance as more complex adaptive routing for tested DOE benchmark applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Chapter 16 - Simulating Discrete-Event Models in the Classic Worldviews: A New Approach to Simultaneous Events and Parallel Execution

This chapter describes a new approach to the activity scanning worldview of discrete-event simulation. With this new approach it becomes possible to realize activity scanning simulations with a parallel computer while resolving simultaneous events in a repeatable manner. The motivation for this approach is the two-phase simulation procedure intrinsic to cellular automata, coupled difference equations, and the discrete-event system specification. This new approach is introduced via an intuitively appealing construction of a new simulation procedure for activity scanning models, and this introduction is followed by an embedding of the new approach into the discrete-event system specification. In doing so, we illustrate a practical approach to distributed simulation and demonstrate how the proposed approach can be applied to the event scheduling and process-oriented worldviews.

Nutaro, James J.↗
DOE OSTI2016

Enabling parallel simulation of large-scale HPC network systems

Here, with the increasing complexity of today’s high-performance computing (HPC) architectures, simulation has become an indispensable tool for exploring the design space of HPC systems—in particular, networks. In order to make effective design decisions, simulations of these systems must possess the following properties: (1) have high accuracy and fidelity, (2) produce results in a timely manner, and (3) be able to analyze a broad range of network workloads. Most state-of-the-art HPC network simulation frameworks, however, are constrained in one or more of these areas. In this work, we present a simulation framework for modeling two important classes of networks used in today’s IBM and Cray supercomputers: torus and dragonfly networks. We use the Co-Design of Multi-layer Exascale Storage Architecture (CODES) simulation framework to simulate these network topologies at a flit-level detail using the Rensselaer Optimistic Simulation System (ROSS) for parallel discrete-event simulation. Our simulation framework meets all the requirements of a practical network simulation and can assist network designers in design space exploration. First, it uses validated and detailed flit-level network models to provide an accurate and high-fidelity network simulation. Second, instead of relying on serial time-stepped or traditional conservative discrete-event simulations that limit simulation scalability and efficiency, we use the optimistic event-scheduling capability of ROSS to achieve efficient and scalable HPC network simulations on today’s high-performance cluster systems. Third, our models give network designers a choice in simulating a broad range of network workloads, including HPC application workloads using detailed network traces, an ability that is rarely offered in parallel with high-fidelity network simulations

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Estimating manufacturing electricity costs by simulating dependence between production parameters

The electricity costs associated with manufacturing generally consist of an energy charge and a demand charge, which are calculated by total kWh × kWh and peak kW × kW, respectively. While these two charges have been extensively studied with production scheduling, dependence between production parameters has not been thoroughly investigated for production energy costs when the production parameters are assumed to be independent. Such dependence, however, can significantly affect the peak kW and resulting energy costs. Therefore, industrial practitioners need a tool to simulate the production power demand and cost to evaluate energy costs for each production plan and for any dependence in the parameters. In this paper, we propose a method for simulating the power demand at the machine and facility levels, focusing on the dependence in milling machine parameters. Using probabilistic techniques with copulas, we quantify the degree of dependence between two milling parameters (processing times and processing amounts) and build a power demand simulator combining discrete event simulation and numerical simulation models. From 35 illustrative examples, we show that a production strategy to adjust dependence can reduce the peak kW and energy costs by more than 15% and 4%, respectively. In addition to dependence, less variability in processing times results in less peak kW and energy costs. Other factors such as a marginal probability distribution and copula types having the same degree of variability have been found to have limited effects on reducing energy costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

WOMBAT (Windfarm Operations and Maintenance cost-Benefit Analysis Tool) WISDEM® [SWR-21-68]

The windfarm operations and maintenance cost-benefit analysis tool (WOMBAT) is software to simulate the operations and maintenance phase of either onshore or offshore windfarms. WOMBAT is a medium-fidelity tool that is designed to model turbine, cable, and substation failures at the subassembly level, but is flexible enough that individual component or asset-level modeling can also be performed. WOMBAT enables users to model technological improvements, the use of a wide range of service equipment from drone repairs and remote resets to heavy lift vessels and crawler cranes, to analyze cost trends with varying trade-offs in conjunction with energy production, availability and a growing range of metrics. This library provides a tool to simulate the operation and maintenance phase (O&M) of distributed, land-based, and offshore windfarms using a discrete event simulation framework. WOMBAT is written around the SimPy discrete event simulation framework. Additionally, this is supported using a flexible and modular object-oriented code base, which enables the modeling of arbitrarily large (or small) windfarms with as many or as few failure and maintenance tasks that can be encoded. Please note that this is still heavily under development, so you may find some functionality to be incomplete at the current moment, but rest assured the functionality is expanding. With that said, it would be greatly appreciated for issues or PRs to be submitted for any improvements at all, from fixing typos (guaranteed to be a few) to features to testing. Also available at: https://pypi.org/project/wombat/

Cooperman, Aubryn↗
DOE OSTI2015

High Fidelity Simulations of Large-Scale Wireless Networks

The worldwide proliferation of wireless connected devices continues to accelerate. There are 10s of billions of wireless links across the planet with an additional explosion of new wireless usage anticipated as the Internet of Things develops. Wireless technologies do not only provide convenience for mobile applications, but are also extremely cost-effective to deploy. Thus, this trend towards wireless connectivity will only continue and Sandia must develop the necessary simulation technology to proactively analyze the associated emerging vulnerabilities. Wireless networks are marked by mobility and proximity-based connectivity. The de facto standard for exploratory studies of wireless networks is discrete event simulations (DES). However, the simulation of large-scale wireless networks is extremely difficult due to prohibitively large turnaround time. A path forward is to expedite simulations with parallel discrete event simulation (PDES) techniques. The mobility and distance-based connectivity associated with wireless simulations, however, typically doom PDES and fail to scale (e.g., OPNET and ns-3 simulators). We propose a PDES-based tool aimed at reducing the communication overhead between processors. The proposed solution will use light-weight processes to dynamically distribute computation workload while mitigating communication overhead associated with synchronizations. This work is vital to the analytics and validation capabilities of simulation and emulation at Sandia. We have years of experience in Sandia’s simulation and emulation projects (e.g., MINIMEGA and FIREWHEEL). Sandia’s current highly-regarded capabilities in large-scale emulations have focused on wired networks, where two assumptions prevent scalable wireless studies: (a) the connections between objects are mostly static and (b) the nodes have fixed locations.

Onunkwo, Uzoma↗
DOE OSTI2020

Toward a Theory of Superdense Time in Simulation Models

In this work, we develop a theory of superdense time that encompasses existing uses of superdense time in discrete event simulations and points to new forms that have not previously been explored. A central feature of our development is a set of axioms for superdense time. Furthermore, the sufficiency of these axioms is demonstrated by using them to prove that a general model of a discrete event simulation procedure, expressed in terms of a mathematical system, constitutes a state transition function. Several forms of superdense time, both known and novel, are shown to satisfy the axioms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Discrete event cellular automata: A new approach to cellular automata for computational material science

Here, we explore the computational advantages of discrete event simulation for cellular automata models of grain growth. These benefits include a reduction in execution time by up to an order of magnitude and the elimination of numerical errors that stem from overshooting grain capture events and approximating a Poisson process with a Bernoulli process. The fundamental mechanisms speeding up the discrete event simulation are uncovered, and with these we create a speedup model that explains our experimental outcomes.

36 MATERIALS SCIENCE↗