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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2010

Introduction to the Buildings Sector Module of SEDS

SEDS is a stochastic engineering-economics model that forecasts economy-wide energy consumption in the U.S. to 2050. It is the product of multi-laboratory collaboration among the National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), Argonne National Laboratory (ANL), Lawrence Berkeley National Laboratory (LBNL), and Lumina Decision Systems. Among national energy models, SEDS is unique, as it is the only model written to explicitly incorporate uncertainty in its inputs and outputs. The primary purpose of SEDS is to estimate the impact of various US Department of Energy (DOE)R&D and policy programs on the performance and subsequent adoption rates of technologies relating to every energy consuming sector of the economy (shown below). It has previously been used to assist DOE in complying with the Government Performance and Results Act of 1993 (GPRA). The focus of LBNL research has been exclusively on develop the buildings model (SBEAM), which is capable of running as a stand-alone forecasting model, or as a part of SEDS as a whole. The full version of SEDS, containing all sectors and interaction is also called the 'integrated' version and is managed by NREL. Forecasts from SEDS are often compared to those coming from National Energy Modeling System (NEMS). The intention of this document is to present new users and developers with a general description of the purpose, functionality and structure of the buildings module within the Stochastic Energy Deployment System (SEDS). The Buildings module, which is capable of running as a standalone model, is also called the Stochastic Buildings Energy and Adoption Model (SBEAM). This document will focus exclusively on SBEAM and its interaction with other major sector modules present within SEDS. The methodologies and major assumptions employed in SBEAM will also be discussed. The organization of this report will parallel the organization of the model itself, being divided into major submodules. As the description progresses, the nature of modules will change from broad, easily understood concepts to lower-level data manipulation. Because SBEAM contains dozens of submodules and hundreds of variables, it would not be relevant or useful to describe each and every one. Rather, the investigation will focus more generally on the operations performed throughout the model. This manual is by no means a complete description of SBEAM; however it should provide the foundation for an introductory understanding of the model. The manual assumes a basic level of understating of Analytica{reg_sign}, the platform on which SEDS and SBEAM have been developed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

The MCDB System for Management and Analysis of Petabyte-Scale Uncertain Data

Analysts working with very large data sets often use statistical models to “guess” at unknown, inaccurate, or missing information associated with the data. For example, a distant object viewed through an optical lens will have its position slightly shifted by imperfections in the lens. Thus, rather than considering the object’s observed position to be absolutely correct, it makes sense to take into account the lens’s imperfections to obtain a probabilistic guess as to the object’s true position. For another example, it might be important to associate some sort of error distribution with each of the individual sensors in an array of magnetometers. This error distribution may be complex and include spatially-driven covariances, because errors in nearby sensors are likely to be correlated (caused, for example, by the presence of some nearby, fixed metal object). This project is concerned with the design and implementation of a prototype data management system called the Monte Carlo Database System, or MCDB for short. MCDB allows an expert-level analyst or statistician to attach arbitrary stochastic models to very large data sets in order to “guess” the values for unknown or inaccurate data, such as the actual position of the observed object in the lens example above. When the resulting data set is analyzed, the underlying stochastic models are used to generate hundreds or thousands of possible data set instances, and each of those possible instances is analyzed separately by MCDB. Thus, MCDB does not just give a single answer to the analysis, but it actually gives an empirical distribution of query results that embody the underlying uncertainty, and can in turn be analyzed using standard statistical techniques. The stochastic models in MCDB are implemented as user-defined, external C++ libraries called Variable Generation functions (VG functions for short). Because the VG function interface is exceedingly general, it allows MCDB to be used in a very wide variety of application domains, in conjunction with virtually any stochastic model. Ultimately, we ended up calling the system that we developed over the course of the project SimSQL, to denote the fact that it allowed for stochastic simulation of data. The software developed during the lifetime of the project is available for download from the SimSQL project website: http://cmj4.web.rice.edu/SimSQL/SimSQL.html.

42 ENGINEERING↗
NASA NTRS1985

An APL program for the distribution of energy deposition by charged particles passing through thin absorbers

An APL program which numerically evaluates the probability density function (PDF) for the energy deposited in a thin absorber by a charged particle is proposed, with application to the construction, pointing, and control of spacecraft. With this program, the PDF of the restricted energy loss distribution of Watts (1973) is derived, and Vavilov's (1957) distribution is obtained by proper parameter selection. The method is demonstrated with the example of the effect of charged particle induced radiation on the Hubble Space Telescope (HST) pointing accuracy. A Monte Carlo study simulates the photon noise caused by charged particles passing through the photomultiplier tube window, and the stochastic variation of energy loss is introduced into the simulation by generating random energy losses from a power law distribution. The program eliminates annoying loop procedures, and model parameter sensitivity can be studied using the graphical output.

Howell, L. W.↗
DOE OSTI2018

Stochastic correlation analysis to rank the impact of intermittent wind generation on unreliability margins of power systems

Power systems are vulnerable to extreme contingencies (like an outage of a major generating substation or transmission line) that can cause significant generation and load loss and can lead to further cascading outages of other transmission facilities and generators in the system. Power systems operators take time-ahead actions (real time to day ahead) actions based on forecasted data as well as telemetry. However, there is a probability of extreme swings in wind generation that can push the power system that is already close to system boundaries into unreliable states. Increasing penetration on wind generation and uncertainties in short term forecast can move the system into operational states that can violate N-1 reliability criteria. While the power system planning accounts for worst case analyses to mitigate such emergencies, it is necessary to understand the spatial and temporal correlation of load buses with intermittent wind generation. The ranking of wind generators based on stochastic correlation to unreliability margins of load buses provides an insight of system vulnerabilities that can be corrected by appropriate emergency controls. A Monte-Carlo simulation of wind uncertainty forecast is performed to identify the probability distributions of unreliability margins. Ranking of wind generators is performed based on stochastic correlation analysis of the wind power generated and the load losses at the buses. This helps in not only identifying weak buses in the power system but also in identifying the vulnerable wind locations to plan for system reinforcements. Power system modeling and hybrid simulation to calculate unreliability margins are performed using DCAT and demonstrated on the 32-bus Nordic test system.

generators, wind forecasting, wind power generatio↗
NASA NTRSDate not supplied

Global Climate Impacts of Greenland and Antarctic Glacial Melt: A Comparative Study

A 10-member ensemble simulation using the NASA GISS-E2-1-G climate model shows a clear bifurcation in the Atlantic Meridional Overturning Circulation (AMOC) strength under the SSP2-4.5 future scenario, leading to 8 stable AMOC-on and 2 stable AMOC-off climate states after 400 years of integration, despite identical forcing of each ensemble member. Ocean models have previously shown a tipping point where sufficient forcing (e.g. by freshwater inputs) can shut down the AMOC. A variety of fully coupled models have demonstrated this, either through hosing experiments or increased precipitation and warming at high latitudes due to increased concentrations of greenhouse gases. In the GISS simulations, there are no external freshwater perturbations. The bifurcation arises freely in the coupled system and is the result of stochastic variability associated with sea-ice transport and melting in the Irminger Sea following a slowing of the GHG concentration increase at the end of the twenty-first century. We believe this is the first time that a CMIP-class model has shown such a bifurcation across an initial condition ensemble.

GISS ModelE2.1↗
NASA NTRS2005

Modeling and Simulation for Realistic Propagation Environments of Communications Signals at SHF Band

In this article, most of widely accepted radio wave propagation models that have proven to be accurate in practice as well as numerically efficient at SHF band will be reviewed. Weather and terrain data along the signal's paths can be input in order to more accurately simulate the propagation environments under particular weather and terrain conditions. Radio signal degradation and communications impairment severity will be investigated through the realistic radio propagation channel simulator. Three types of simulation approaches in predicting signal's behaviors are classified as: deterministic, stochastic and attenuation map. The performance of the simulation can be evaluated under operating conditions for the test ranges of interest. Demonstration tests of a real-time propagation channel simulator will show the capabilities and limitations of the simulation tool and underlying models.

simulation↗
DOE OSTI2016

EBR-II Static Neutronic Calculations by PHISICS / MCNP6 codes

The International Atomic Energy Agency (IAEA) launched a Coordinated Research Project (CRP) on the Shutdown Heat Removal Tests (SHRT) performed in the 80's at the Experimental fast Breeder Reactor EBR-II, USA. The scope of the CRP is to improve and validate the simulation tools for the study and the design of the liquid metal cooled fast reactors. Moreover, training of the next generation of fast reactor analysts is being also considered the other scope of the CRP. In this framework, a static neutronic model was developed, using state-of-the art neutron transport codes like SCALE/PHISICS (deterministic solution) and MCNP6 (stochastic solution). Preliminary criticality calculations have been performed using both models (deterministic and stochastic). Results show the good agreement between different models. It should also be noted that deterministic solution could still be further improved by implementing super-homogenization factors and by increasing the transport calculation order.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Dispersal-Based Microbial Community Assembly Decreases Biogeochemical Function

Ecological mechanisms influence relationships among microbial communities, which in turn impact biogeochemistry. In particular, microbial communities are assembled by deterministic (e.g., selection) and stochastic (e.g., dispersal) processes, and the relative balance of these two process types is hypothesized to alter the influence of microbial communities over biogeochemical function. We used an ecological simulation model to evaluate this hypothesis, defining biogeochemical function generically to represent any biogeochemical reaction of interest. We assembled receiving communities under different levels of dispersal from a source community that was assembled purely by selection. The dispersal scenarios ranged from no dispersal (i.e., selection-only) to dispersal rates high enough to overwhelm selection (i.e., homogenizing dispersal). We used an aggregate measure of community fitness to infer a given community’s biogeochemical function relative to other communities. We also used ecological null models to further link the relative influence of deterministic assembly to function. We found that increasing rates of dispersal decrease biogeochemical function by increasing the proportion of maladapted taxa in a local community. Niche breadth was also a key determinant of biogeochemical function, suggesting a tradeoff between the function of generalist and specialist species. Finally, we show that microbial assembly processes exert greater influence over biogeochemical function when there is variation in the relative contributions of dispersal and selection among communities. Taken together, our results highlight the influence of spatial processes on biogeochemical function and indicate the need to account for such effects in models that aim to predict biogeochemical function under future environmental scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Electron heating in low pressure capacitive discharges revisited

The electrons in capacitively coupled plasmas (CCPs) absorb energy via ohmic heating due to electron-neutral collisions and stochastic heating due to momentum transfer from high voltage moving sheaths. We use Particle-in-Cell (PIC) simulations to explore these heating mechanisms and to compare the PIC results with available theories on ohmic and stochastic heating. The PIC results for ohmic heating show good agreement with the ohmic heating calculation of Lafleur et al. [Phys. Plasmas 20, 124503 (2013)]. The PIC results for stochastic heating in low pressure CCPs with collisionless sheaths show good agreement with the stochastic heating model of Kaganovich et al. [IEEE Trans. Plasma Sci. 34, 696 (2006)], which revises the hard wall asymptotic model of Lieberman [IEEE Trans. Plasma Sci. 16, 638 (1988)] by taking current continuity and bulk oscillation into account.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Stochastic optimization of GeantV code by use of genetic algorithms

GeantV is a complex system based on the interaction of different modules needed for detector simulation, which include transport of particles in fields, physics models simulating their interactions with matter and a geometrical modeler library for describing the detector and locating the particles and computing the path length to the current volume boundary. The GeantV project is recasting the classical simulation approach to get maximum benefit from SIMD/MIMD computational architectures and highly massive parallel systems. This involves finding the appropriate balance between several aspects influencing computational performance (floating-point performance, usage of off-chip memory bandwidth, specification of cache hierarchy, etc.) and handling a large number of program parameters that have to be optimized to achieve the best simulation throughput. This optimization task can be treated as a black-box optimization problem, which requires searching the optimum set of parameters using only point-wise function evaluations. Here, the goal of this study is to provide a mechanism for optimizing complex systems (high energy physics particle transport simulations) with the help of genetic algorithms and evolution strategies as tuning procedures for massive parallel simulations. One of the described approaches is based on introducing a specific multivariate analysis operator that could be used in case of resource expensive or time consuming evaluations of fitness functions, in order to speed-up the convergence of the black-box optimization problem.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1973

A stochastic model of the electromyogram

The quantitative regularities of interference pattern formation by motor unit action potentials is investigated. The parameters of a single motor unit and how they relate to the Fourier transform analysis of an EMG are considered. The Fourier transform of the simulated electromyogram is compared with the Fourier transform of the actual EMG recorded from various human muscles using surface electrodes.

Agarwal, G. C.↗
DOE OSTI2013

Localised distributions and criteria for correctness in complex Langevin dynamics

Complex Langevin dynamics can solve the sign problem appearing in numerical simulations of theories with a complex action. In order to justify the procedure, it is important to understand the properties of the real and positive distribution, which is effectively sampled during the stochastic process. In the context of a simple model, we study this distribution by solving the Fokker–Planck equation as well as by brute force and relate the results to the recently derived criteria for correctness. We demonstrate analytically that it is possible that the distribution has support in a strip in the complexified configuration space only, in which case correct results are expected. -- Highlights: •Characterisation of the equilibrium distribution sampled in complex Langevin dynamics. •Connection between criteria for correctness and breakdown. •Solution of the Fokker–Planck equation in the case of real noise. •Analytical determination of support in complexified space.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

A luminosity model of RHIC gold runs

In this note, we present a luminosity model for RHIC gold runs. The model is applied to the physics fills in 2007 run without cooling, and with the longitudinal cooling applied to one beam only. Having good comparison, the model is used to project a fill with the longitudinal cooling applied to both beams. Further development and possible applications of the model are discussed. To maximize the integrated luminosity, usually the higher beam intensity, smaller longitudinal and transverse emittance, and smaller {beta} are the directions to work on. In past 10 years, the RHIC gold runs have demonstrated a path toward this goal. Most recently, a successful commissioning of the bunched beam stochastic cooling, both longitudinal and transverse, has offered a chance of further RHIC luminosity improvement. With so many factors involved, a luminosity model would be useful to identify and project gains in the machine development. In this article, a preliminary model is proposed. In Section 2, several secondary factors, which are not yet included in the model, are identified based on the RHIC operation condition and experience in current runs. In Section 3, the RHIC beam store parameters used in the model are listed, and validated. In Section 4, the factors included in the model are discussed, and the luminosity model is presented. In Section 5, typical RHIC gold fills without cooling, and with partial cooling are used for comparison with the model. Then a projection of fills with more coolings is shown. In Section 6, further development of the model is discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2010

THE SPARSEST CLUSTERS WITH O STARS

There is much debate on how high-mass star formation varies with environment, and whether the sparsest star-forming environments are capable of forming massive stars. To address this issue, we have observed eight apparently isolated OB stars in the Small Magellanic Cloud (SMC) using Hubble Space Telescope's Advanced Camera for Surveys. Five of these objects appear as isolated stars, two of which are confirmed to be runaways. The remaining three objects are found to exist in sparse clusters, with {approx}<10 companion stars revealed, having masses of 1-4 M{sub sun}. Stochastic effects dominate in these sparse clusters, so we perform Monte Carlo simulations to explore how our observations fit within the framework of empirical, galactic cluster properties. We generate clusters using a simplistic -2 power-law distribution for either the number of stars per cluster (N{sub *}) or cluster mass (M{sub cl}). These clusters are then populated with stars randomly chosen from a Kroupa initial mass function (IMF). We find that simulations with cluster lower-mass limits of M{sub cl,lo} {>=} 20 M{sub sun} and N{sub *,lo} {>=} 40 match best with observations of SMC and Galactic OB star populations. We examine the mass ratio of the second most massive and most massive stars m{sub max,2}/m{sub max}, finding that our observations all exist below the 20th percentile of our simulated clusters. However, all of our observed clusters lie within the parameter space spanned by the simulated clusters, although some are in the lowest 5th percentile frequency. These results suggest that clusters are built stochastically by randomly sampling stars from a universal IMF with a fixed stellar upper-mass limit. In particular, we see no evidence to suggest an m{sub max}-M{sub cl} relation. Our results may be more consistent with core accretion models of star formation than with competitive accretion models, and they are inconsistent with the proposed steepening of the integrated galactic stellar initial mass function (IGIMF).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids

In this work, we introduce a generalized machine learning framework to probabilistically parameterize upper-scale models in the form of nonlinear PDEs consistent with a continuum theory, based on coarse-grained atomistic simulation data of mechanical deformation and flow processes. The proposed framework utilizes a hypothesized coarse-graining methodology with manifold learning and surrogate-based optimization techniques. Coarse-grained high-dimensional data describing quantities of interest of the multiscale models are projected onto a nonlinear manifold whose geometric and topological structure is exploited for measuring behavioral discrepancies in the form of manifold distances. A surrogate model is constructed using Gaussian process regression to identify a mapping between stochastic parameters and distances. Derivative-free optimization is employed to adaptively identify a unique set of parameters of the upper-scale model capable of rapidly reproducing the system's behavior while maintaining consistency with coarse-grained atomic-level simulations. The proposed method is applied to learn the parameters of the shear transformation zone (STZ) theory of plasticity that describes plastic deformation in amorphous solids as well as coarse-graining parameters needed to translate between atomistic and continuum representations. We show that the methodology is able to successfully link coarse-grained microscale simulations to macroscale observables and achieve a high-level of parity between the models across scales.

36 MATERIALS SCIENCE↗
DOE OSTI2013

A GENERALIZED STOCHASTIC COLLOCATION APPROACH TO CONSTRAINED OPTIMIZATION FOR RANDOM DATA IDENTIFICATION PROBLEMS

We present a scalable, parallel mechanism for stochastic identification/control for problems constrained by partial differential equations with random input data. Several identification objectives will be discussed that either minimize the expectation of a tracking cost functional or minimize the difference of desired statistical quantities in the appropriate $L^p$ norm, and the distributed parameters/control can both deterministic or stochastic. Given an objective we prove the existence of an optimal solution, establish the validity of the Lagrange multiplier rule and obtain a stochastic optimality system of equations. The modeling process may describe the solution in terms of high dimensional spaces, particularly in the case when the input data (coefficients, forcing terms, boundary conditions, geometry, etc) are affected by a large amount of uncertainty. For higher accuracy, the computer simulation must increase the number of random variables (dimensions), and expend more effort approximating the quantity of interest in each individual dimension. Hence, we introduce a novel stochastic parameter identification algorithm that integrates an adjoint-based deterministic algorithm with the sparse grid stochastic collocation FEM approach. This allows for decoupled, moderately high dimensional, parameterized computations of the stochastic optimality system, where at each collocation point, deterministic analysis and techniques can be utilized. The advantage of our approach is that it allows for the optimal identification of statistical moments (mean value, variance, covariance, etc.) or even the whole probability distribution of the input random fields, given the probability distribution of some responses of the system (quantities of physical interest). Our rigorously derived error estimates, for the fully discrete problems, will be described and used to compare the efficiency of the method with several other techniques. Numerical examples illustrate the theoretical results and demonstrate the distinctions between the various stochastic identification objectives.

Webster, Clayton G↗
DOE OSTI2016

Variational Bayesian strategies for high-dimensional, stochastic design problems

This paper is concerned with a lesser-studied problem in the context of model-based, uncertainty quantification (UQ), that of optimization/design/control under uncertainty. The solution of such problems is hindered not only by the usual difficulties encountered in UQ tasks (e.g. the high computational cost of each forward simulation, the large number of random variables) but also by the need to solve a nonlinear optimization problem involving large numbers of design variables and potentially constraints. We propose a framework that is suitable for a class of such problems and is based on the idea of recasting them as probabilistic inference tasks. To that end, we propose a Variational Bayesian (VB) formulation and an iterative VB–Expectation-Maximization scheme that is capable of identifying a local maximum as well as a low-dimensional set of directions in the design space, along which, the objective exhibits the largest sensitivity. We demonstrate the validity of the proposed approach in the context of two numerical examples involving thousands of random and design variables. In all cases considered the cost of the computations in terms of calls to the forward model was of the order of 100 or less. The accuracy of the approximations provided is assessed by information-theoretic metrics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1994

Analysis of subgrid models using direct and large-eddy simulations of isotropic turbulence

Direct and large eddy simulations of forced and decaying isotropic turbulence have been performed using a pseudospectral and a finite-difference code. Subgrid models that include a one-equation subgrid kinetic energy model with and without a stochastic backscatter forcing term and a new scale similarity model have been analyzed in both Fourier space and physical space. The Fourier space analysis showed that the energy transfer across the cutoff wavenumber k(sub c) is dominated by local interaction. The correlation between the exact and the modeled (by a spectral eddy viscosity) nonlinear terms and the subgrid energy transfer in physical space was found to be quite low. In physical space, a similar correlation analysis was carried out using top hat filtering. Results show that the subgrid stress and the energy flux predicted by the subgrid models correlates very well with the exact data. The scale similarity model showed very high correlation for reasonable grid resolution. However, with decrease in grid resolution, the scale similarity model became more uncorrelated, when compared to the kinetic energy subgrid model. The subgrid models were then used for large-eddy simulations for a range of Reynolds number. It was determined that the dissipation was modeled poorly and that the correlation with the exact results was quite low for all the models. In general, for coarse grid resolution, the scale similarity model consistently showed very low correlation while the kinetic energy model showed a relatively higher correlation. These results suggest that to use the scale similarity model relatively fine grid resolution may be required, whereas, the kinetic energy model could be used even in coarse grid.

Menon, S.↗