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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Modification of the SAS4A Safety Analysis Code for Integration with the ADAPT Discrete Dynamic Event Tree Framework.

It is difficult to assess the consequences of a transient in a sodium-cooled fast reactor (SFR) using traditional probabilistic risk assessment (PRA) methods, as numerous safety-related sys- tems have passive characteristics. Often there is significant dependence on the value of con- tinuous stochastic parameters rather than binary success/failure determinations. One form of dynamic PRA uses a system simulator to represent the progression of a transient, tracking events through time in a discrete dynamic event tree (DDET). In order to function in a DDET environment, a simulator must have characteristics that make it amenable to changing physical parameters midway through the analysis. The SAS4A SFR system analysis code did not have these characteristics as received. This report describes the code modifications made to allow dynamic operation as well as the linking to a Sandia DDET driver code. A test case is briefly described to demonstrate the utility of the changes.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2017

A full-angle Monte-Carlo scattering technique including cumulative and single-event Rutherford scattering in plasmas [Theory of cumulative large-angle collisions in plasmas]

Here, we describe and justify a full-angle scattering (FAS) method to faithfully reproduce the accumulated differential angular Rutherford scattering probability distribution function (pdf) of particles in a plasma. The FAS method splits the scattering events into two regions. At small angles it is described by cumulative scattering events resulting, via the central limit theorem, in a Gaussian-like pdf; at larger angles it is described by single-event scatters and retains a pdf that follows the form of the Rutherford differential cross-section. The FAS method is verified using discrete Monte-Carlo scattering simulations run at small timesteps to include each individual scattering event. We identify the FAS regime of interest as where the ratio of temporal/spatial scale-of-interest to slowing-down time/length is from 10 -3 to 0.3–0.7; the upper limit corresponds to Coulomb logarithm of 20–2, respectively. Two test problems, high-velocity interpenetrating plasma flows and keV-temperature ion equilibration, are used to highlight systems where including FAS is important to capture relevant physics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Quantifying fine particle emission events from time-resolved measurements: Method description and application to 18 California low-income apartments

PM 2.5 exposure is associated with significant health risk. Exposures in homes derive from both outdoor and indoor sources, with emissions occurring primarily in discrete events. Data on emission event magnitudes and schedules are needed to support simulation-based studies of exposures and mitigations. For this study, we applied an identification and characterization algorithm to quantify time-resolved PM 2.5 emission events from data collected during 224 days of monitoring in 18 California apartments with low-income residents. We identified and characterized 836 distinct events with median and mean values of 12 and 30 mg emitted mass, 16 and 23 minutes emission duration, 37 and 103 mg/h emission rates, and pseudo-first–order decay rates of 1.3 and 2.0/h. Mean event-averaged concentrations calculated using the determined event characteristics agreed to within 6% of measured values for 14 of the apartments. There were variations in event schedules and emitted mass across homes, with few events overnight and most emissions occurring during late afternoons and evenings. Event characteristics were similar during weekdays and weekends. Emitted mass was positively correlated with number of residents (Spearman coefficient, ρ=.10), bedrooms (ρ=.08), house volume (ρ=.29), and indoor-outdoor CO 2 difference (ρ=.27). The event schedules can be used in probabilistic modeling of PM 2.5 in low-income apartments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

High-Fidelity Models and Fast EMT Simulation Algorithms for Isolated Multi-port Autonomous Reconfigurable Solar power plant (MARS)

The integration of hybrid photovoltaic (PV) and energy storage system (ESS) based plants has become a promising way of solving the intermittency of PV plants and providing frequency support to the power grid. The multi-port autonomous reconfigurable solar power plant (MARS) can integrate the PV systems and ESSs to an ac grid and dc lines. The proposed isolated MARS incorporates an isolated converter that connects to the PV arrays and is based on the dual active bridge (DAB) converter. The high frequency switching in the DAB and the means to control the DAB converter using delays between switching signals lead to the need for a small timestep in simulations. Moreover, several hundreds of modules that include a DAB converter are present in the MARS. The small timestep and the presence of several hundreds of modules lead to a significant rise in the overall simulation time. To address this issue, simulation algorithms like numerical stiffness-based hybrid discretization and the hysteresis relaxation technique are applied to the switched system model of isolated MARS. Additionally, an event-driven interpolating method is introduced to help increase the minimum timestep to simulate the conventional DAB converter model while maintaining high accuracy in the simulation results. The developed model is validated by comparison with its reference model built in the PSCAD/EMTDC and MATLAB software environments using library components.

Xia, Qian↗
DOE OSTI2016

Space radiation simulation using blowout plasma wakes at the SAMURAI Lab

We discuss the creation of broadband electron energy spectra using plasma wakefield acceleration at the SAMURAI Lab as part of the UCLA Particle Beam Physics Laboratory group. As the exploration and development of space increases, it will become important to efficiently replicate radiation conditions that exist in low earth orbit and planetary space. Missions such as satellite deployment and exploration of other planets, i.e. the Juno probe launched to explore Jupiter [1], require electronics that are able to withstand the electron radiation spectrum present in these locations. This requires Earth-based testing for development of robust electronics with respect to electron radiation exposure. Conventional accelerators are poorly suited for this purpose because they are designed for monoenergetic electron beams. Current simulation schemes involve the interpolation of discrete exposure tests, with time structures entirely different from space events. This idea poorly captures the radiation damage physics seen in the real world. Conversely, the wakefields produced in the blowout regime of beam plasma wakefield accelerators can be used to create the full spectrum of space radiation including high energy “killer” electrons by injecting a long bunch to excite the plasma blowout. With a long bunch ( k p σ z ≈ 2 ) the beam will feel both accelerating and decelerating fields, resulting in an exponential or power law energy spectrum distribution seen in regions outside the Earth and Jupiter. This can be achieved at the new SAMURAI lab at UCLA resulting in broadband energy spectra with maximum energies up to 100 MeV that correctly reflect the radiative conditions of space in low Earth orbit and around Jupiter.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

A rigorous sequential update strategy for parallel kinetic Monte Carlo simulation

The kinetic Monte Carlo (kMC) method is used in many scientific fields in applications involving rare-event transitions. Due to its discrete stochastic nature, efforts to parallelize kMC approaches often produce unbalanced time evolutions requiring complex implementations to ensure correct statistics. In the context of parallel kMC, the sequential update technique has shown promise by generating high quality distributions with high relative efficiencies for short-range systems. In this work, we provide an extension of the sequential update method in a parallel context that rigorously obeys detailed balance, which guarantees exact equilibrium statistics for all parallelization settings. Furthermore, our approach also preserves nonequilibrium dynamics with minimal error for many parallelization settings, and can be used to achieve highly precise sampling.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Ultrafaint dwarf galaxies—The lowest-mass relics from before reionization

New observations suggest that ultrafaint dwarf galaxies (UFDs)—the least luminous systems bound by dark matter halos (≲10{sup 5} L{sub ⊙})—may have formed before reionization. The extrapolated virial masses today are uncertain, with estimates ranging from 10{sup 8} M{sub ⊙} to as high as 10{sup 9} M{sub ⊙} depending on the assumed form of the underlying potential. We show that the progenitor halo masses of UFDs can, in principle, be as low as M{sub vir} ≈ 10{sup 7} M{sub ⊙}. Under the right conditions, such a halo can survive the energy input of a supernova (SN) and its radiative progenitor. A clumpy (fractal) medium is much less susceptible to both internal and external injections of energy. It is less prone to SN sweeping (particularly if it is off-centered) because the coupling efficiency of the explosive energy is much lower than for a diffuse interstellar medium. With the aid of the 3D hydro/ionization code Fyris, we show that sufficient baryons are retained to form stars following a single SN event in dark matter halos down to M{sub vir} ≈ 10{sup 7} M{sub ⊙} in the presence of radiative cooling. In these models, the gas survives the SN explosion, is enriched with the specific abundance yields of the discrete events, and reaches surface densities where low-mass stars can form. Our highest-resolution simulations reveal why cooling is so effective in retaining gas compared to any other factor. In the early stages, the super-hot metal-enriched SN ejecta exhibit strong cooling, leading to much of the explosive energy being lost. Consistent with earlier work, the baryons do not survive in smooth or adiabatic models in the event of an SN. The smallest galaxies may not contribute a large fraction of matter to the formation of galaxies, but they carry signatures of the earliest epochs of star formation, as we show. These signatures may allow us to distinguish a small primordial galaxy from one that was stripped down to its present size through tidal interaction. We discuss these results in the context of local UFDs and damped Lyα systems (z ∼ 2) at very low metallicity ([Fe/H] ∼ −3). We show that both classes of objects are consistent with primordial low-mass systems that have experienced only a few enrichment events.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Enhancing Power Plant Safety through Coupling Plant Simulators to Cyber Digital Architecture

There are differences in how cyber-attack, sabotage, or discrete component failure mechanisms manifest within power plants and what these events would look like within the control room from an operator's perspective. This research focuses on understanding how a cyber event would affect the operation of the plant, how an operator would perceive the event, and if the operator's actions based on those perceptions will allow him/her to maintain plant safety. This research is funded as part of Sandia's Laboratory Directed Research and Development (LDRD) program to develop scenarios with cyber induced failure of plant systems coupled with a generic pressurized water reactor plant training simulator. The cyber scenario s w ere developed separately and injected into the simulator operational state to simulate an attack. These scenarios will determine if Nuclear Power Plant (NPP) operators can 1) recognize that the control room indicators were presenting incorrect or erroneous information and 2) take appropriate actions to keep the plant safe. This will also provide the opportunity to assess the operator cognitive workload during such events and identify where improvements might be made. This paper will review results of a pilot study run with NPP operators to investigate performance under various cyber scenarios. The discussion will provide an overview of the approach, scenario selection, metrics captured, resulting insights into operator actions and plant response to multiple scenarios of the NPP system.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Hardware-in-the-Loop Simulation of a Distribution System with Air Conditioners under Model Predictive Control: Preprint

Many have proposed that responsive load provided by distributed energy resources (DERs) and demand response (DR) are an option to provide flexibility to the grid and especially to distribution feeders. However, because responsive load involves a complex interplay between tariffs and DER and DR technologies, it is challenging to test and evaluate options without negatively impacting customers. This paper describes a hardware-in-the-loop (HIL) simulation system that has been developed to reduce the cost of evaluating the impact of advanced controllers (e.g., model predictive controllers) and technologies (e.g., responsive appliances). The HIL simulation system combines large-scale software simulation with a small set of representative building equipment hardware. It is used to perform HIL simulation of a distribution feeder and the loads on it under various tariff structures. In the reported HIL simulation, loads include many simulated air conditioners and one physical air conditioner. Independent model predictive controllers manage operations of all air conditioners under a time-of-use tariff. Results from this HIL simulation and a discussion of future development work of the system are presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Blueprints for Training Information Bottlenecks for Collider Analyses

Dimensionality reduction is a crucial aspect of data analysis in high energy physics, even if accompanied by information loss. Several methods, including histogram- and kernel-based analyses, are only computationally feasible for low-dimensional data. Furthermore, simulation models used in HEP can often only be validated for low-dimensional data. We provide several blueprints for using machine learning to create low-dimensional data representations (continuous event variables and discrete classification labels) for use in signal discovery and parameter estimation tasks. We also describe how to design the learned representation to facilitate a) searches with unknown model parameters and b) validation of simulation models in data control regions.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

Extension of the ADAPT Framework for Multiple Simulators

Probabilistic Risk Assessment (PRA) uses fault tree/event tree analysis to evaluate the environmental impacts of nuclear power plants due to internal or external initiating events. In a fault tree, basic failure events are assembled using primarily And/Or logic to determine the combinations of failures that lead to failure of the system as a whole. An event tree is forward-facing, and begins with a single initiating event. From there the analysis considers what event may occur next, and branches out among the possible configurations of occurrence and non-occurrence of that event. This branching continues until end states are reached. End states may include a safe and stable configuration of the plant, or any number of differing failure states. Both fault and event trees require basic event probabilities as inputs to provide insight on the likelihood of different outcomes. PRA has been applied to nuclear power plants as an analysis tool since the 1975 Reactor Safety Study. In a traditional PRA, the order of events is prescribed by the analyst and each event is typically a binary aleatory uncertainty: occurrence or non-occurrence. Discrete dynamic event tree (DDET) analysis eliminates this subjectivity in ordering of events by using the output of a dynamic system model (simulator) to inform the branching. Branching conditions are triggered by the existence of a relevant plant state in the code, and therefore only occur as physically appropriate. The DDET approach also allows consideration of both epistemic and aleatory uncertainties on a phenomenologically and stochastically consistent platform. One limitation of the DDET driver codes developed to date (namely ADAPT, ADS, and MCDET ) is that the tree is driven by a single simulator. The goal of this work is a general multi-simulator driver. This limits the parameter space that may be explored in a single DDET to that which may be covered by a single simulator. In this work, the Analysis of Dynamic Accident Progression Trees (ADAPT) code is extended to support multiple arbitrary simulators. This capability will increase the depth and breadth of phenomena that may be analyzed in a single DDET. For example, a large-scale system code may pass the analysis to a more detailed code if certain conditions are met, in order to capture relevant phenomena that would be lost if only the system code were to be used. In addition, the time required to cover a given uncertainty space may be reduced by dynamically identifying and running only the situations that are appropriate given the plant conditions in each simulator. The use of multiple simulators is expected to further reduce subjectivity in PRA by enabling high fidelity modeling of reality. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2012

Relational Blackboard

The Relational Blackboard (RBB) is an extension of the H2 Relational Database to support discrete events and timeseries data. The original motivation for RBB is as a knowledge base for cognitive systems and simulations. It is useful wherever there is a need for persistent storage of timeseries (i.e. samples of a continuous process generating numerical data) and semantic labels for the data. The RBB is an extension to the H2 Relational Database, which is open-source. RBB is a set of stored procedures for H2 allowing data to be labeled, queried, and resampled.

Abbott, RobertG↗
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 OSTI2012

ACCELERATION OF VERY SMALL DUST GRAINS DUE TO RANDOM CHARGE FLUCTUATIONS

We study the acceleration of very small dust grains including polycyclic aromatic hydrocarbons arising from electrostatic interactions of dust grains that have charge fluctuating randomly in time. Random charge fluctuations of very small grains due to discrete charging events (i.e., sticking collisions with electrons and ions in plasma, and emission of photoelectrons by UV photons) are simulated using the Monte Carlo (MC) method. The motion of dust grains in randomly fluctuating electric fields induced by surrounding charged grains is studied using MC simulations. We identify the acceleration induced by random charge fluctuations as a dominant acceleration mechanism for very small grains in the diffuse interstellar medium (ISM). We find that this acceleration mechanism is efficient for environments with a low degree of ionization (i.e., large Debye length), where charge fluctuations are slow but have a large amplitude. The implications of the present acceleration mechanism for grain coagulation and shattering in the diffuse ISM and dark clouds are also discussed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Variance Reduction within Implicit Monte Carlo Thermal Radiation Transport using the Local Importance Function Transform [Slides]

System of equations derived by Fleck and Cummings to model time-dependent thermal radiative transfer (TRT) problems: Solution of radiation specific intensity (I), non-linearly coupled to material internal energy (U); Absorption and re-emission approximated by effective scattering events. Useful for high-energy density physics simulations: i.e., astrophysics, inertial confinement fusion (ICF). Whereas deterministic methods are fully discretized and free of statistical noise, Monte Carlo methods allow for dynamic sampling of the phase space at the price of statistical noise; Monte Carlo methods are also characterized by a slow $\frac{1}{√Ν}$ convergence rate.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Recovering the microseismic response from a geomechanical simulation

The assessment of microseismic events is one of a few tools available for characterizing processes that occur within the subsurface. Here, we have developed a method for modeling microseismic activity in the subsurface that allows us to model microseismic events at the reservoir scale. By embedding this method in a fully coupled hydromechanical numerical code, we simulate the development of hydraulic fractures in an unconventional reservoir with an explicitly represented discrete fracture network for a relatively simple synthetic model and a more complicated model based upon field observations. The findings demonstrate that the model can be effectively calibrated against measured microseismic activity, and it can be used to make predictions regarding the timing, location, and amplitude of events in the subsurface.

58 GEOSCIENCES↗
DOE OSTI2016

Conditional Tree Reduction in the ADAPT Framework

Probabilistic Risk Assessment (PRA) uses fault tree/event tree analysis to evaluate the environmental impacts of nuclear power plants due to internal or external initiating events. In a fault tree, basic failure events are assembled using primarily And/Or logic to determine the combinations of failures that lead to failure of the system as a whole. An event tree is forward-facing, and begins with a single initiating event. From there the analysis considers what event may occur next, and branches out among the possible configurations of occurrence and non-occurrence of that event. This branching continues until end states are reached. End states may include a safe and stable configuration of the plant, or any number of differing failure states. Both fault and event trees require basic event probabilities as inputs to provide insight on the likelihood of different outcomes. PRA has been applied to nuclear power plants as an analysis tool since the 1975 Reactor Safety Study. In a traditional PRA, the order of events is prescribed by the analyst and each event is typically a binary aleatory uncertainty: occurrence or non-occurrence. Discrete dynamic event tree (DDET) analysis eliminates this subjectivity in ordering of events by using the output of a dynamic system model (simulator) to inform the branching. Branching conditions are triggered by the existence of a relevant plant state in the code, and therefore only occur as physically appropriate. The DDET approach also allows consideration of both epistemic and aleatory uncertainties on a phenomenologically and stochastically consistent platform. A DDET may be orders of magnitude larger than a traditional event tree for the same initiating event, as more of the uncertainty space is likely to be explored and the tree does not require manual assembly. Because of the size, manual inspection of the entire DDET is often infeasible. This problem may be approached from the front by reducing the number of sampled branching values or by online pruning of the tree, thus reducing the total number of branches. Utilizing post-processing techniques to draw actionable insights via clustering has also been proposed. One post-processing tool to reduce the DDET according to user-defined rules has been recently implemented in the Analysis of Dynamic Accident Progression Trees (ADAPT) DDET driver code. The tool creates a 'slice' of the DDET comprised of the set of branches that meet a set of user-input rules, for immediate visualization or for further calculations conditional on the rules being met. This extension is expected to ease analysis of the results of large and complex DDETs, leading to greater applicability of DDETs in PRA. It should be emphasized that this tool is a visualization aide and does not impact the theoretical basis of the DDET approach. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Dynamic Importance Measures in the ADAPT Framework

Probabilistic Risk Assessment (PRA) uses fault tree/event tree analysis to evaluate the environmental impacts of nuclear power plants due to internal or external initiating events. In a fault tree, basic failure events are assembled using primarily And/Or logic to determine the combinations of failures that lead to failure of the system as a whole. An event tree is forward-facing, and begins with a single initiating event. From there the analysis considers what event may occur next, and branches out among the possible configurations of occurrence and non-occurrence of that event. This branching continues until user-defined end states are reached. End states may include a safe and stable configuration of the plant, or any number of differing failure states. Both fault and event trees require basic event probabilities as inputs to provide insight on the likelihood of different outcomes. PRA has been applied to nuclear power plants as an analysis tool since the 1975 Reactor Safety Study. In a traditional PRA, the order of events is prescribed by the analyst and each event is typically a binary aleatory uncertainty: occurrence or non-occurrence. Discrete dynamic event tree (DDET) analysis eliminates this subjectivity in ordering of events by using the output of a dynamic system model (simulator) to inform the branching. Branching conditions are triggered by the existence of a relevant plant state in the code, and therefore only occur as physically appropriate. The DDET approach also allows consideration of both epistemic and aleatory uncertainties on a phenomenologically and stochastically consistent platform. One current limitation of DDETs is the assessment of results, which is not as well developed as in traditional PRA. A concept that is used in PRA to assess the significance of a basic event is the concept of importance measures (IMs). In traditional PRA, IMs only consider the likelihood of occurrence of non-occurrence of a basic event. The application of importance measures to DDETs must take into consideration not only the occurrence and non-occurrence of an event, but also uncertain timing or severity. This work describes three general dynamic importance measures (DIMs) as implemented in the Analysis of Dynamic Accident Progression Trees (ADAPT) DDET driver code. The measures account for the change in progression of the DDET resulting from the different values of each uncertain variable under investigation. The DIMs are applied to a sample DDET to demonstrate the type of insights that may be gained from each one. These measures will facilitate comparison of the impact of dynamic branching events on a consequence of interest, which also allow DDETs to be used in prioritizing plant investments. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗