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At least 1,603 records · Page 89

All sources NASA NTRS DOE OSTI

DOE OSTI2018

Scale-dependent galaxy bias, CMB lensing-galaxy cross-correlation, and neutrino masses

One of the most powerful cosmological datasets when it comes to constraining neutrino masses is represented by galaxy power spectrum measurements, P gg ($k$). The constraining power of P gg ($k$) is however severely limited by uncertainties in the modeling of the scale-dependent galaxy bias b($k$). In this work we present a new proof-of-principle for a method to constrain b($k$) by using the cross-correlation between the Cosmic Microwave Background (CMB) lensing signal and galaxy maps (C$Kg\atop{l}$) using a simple but theoretically well-motivated parametrization for b($k$). We apply the method using C$Kg\atop{l}$ measured by cross-correlating Planck lensing maps and the Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 11 (DR11) CMASS galaxy sample, and P gg ($k$) measured from the BOSS DR12 CMASS sample. We detect a non-zero scale-dependence at moderate significance, which suggests that a proper modeling of b($k$) is necessary in order to reduce the impact of non-linearities and minimize the corresponding systematics. The accomplished increase in constraining power of P gg ($k$) is demonstrated by determining a 95% confidence level upper bound on the sum of the three active neutrino masses M $v$ of M $v$ < 0:19 eV. This limit represents a significant improvement over previous bounds with comparable datasets. Our method will prove especially powerful and important as future large-scale structure surveys will overlap more significantly with the CMB lensing kernel providing a large cross-correlation signal.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

Network-Cognizant Model Reduction of Grid-Tied Three-Phase Inverters

Power-electronics inverters are expected to satisfy a significant fraction of system load in next-generation power networks with the growing integration of renewable resources and flexible loads. Typical dynamical models for grid-tied inverters are nonlinear and composed of a large number of states; therefore it is impractical to study systems with many inverters when their full dynamics are retained. In our previous work, we have shown that a system of parallel-connected grid-tied three-phase inverters can be modeled as one aggregated inverter unit with the same structure and state-space dimension as any individual inverter in the system. Here, we extend this result to networks with arbitrary topologies by leveraging a classical aggregation method for coherent synchronous generators in transmission networks, and a linear approximation of the AC power-flow equations to ease computational burden. Numerical simulation results for a prototypical distribution feeder demonstrate the accuracy and computational benefits of the approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Active Subspaces for Wind Plant Surrogate Modeling

Understanding the uncertainty in wind plant performance is crucial to their cost-effective design and operation. However, conventional approaches to uncertainty quantification (UQ), such as Monte Carlo techniques or surrogate modeling, are often computationally intractable for utility-scale wind plants because of poor congergence rates or the curse of dimensionality. In this paper we demonstrate that wind plant power uncertainty can be well represented with a low-dimensional active subspace, thereby achieving a significant reduction in the dimension of the surrogate modeling problem. We apply the active sub-spaces technique to UQ of plant power output with respect to uncertainty in turbine axial induction factors, and find a single active subspace direction dominates the sensitivity in power output. When this single active subspace direction is used to construct a quadratic surrogate model, the number of model unknowns can be reduced by up to 3 orders of magnitude without compromising performance on unseen test data. We conclude that the dimension reduction achieved with active subspaces makes surrogate-based UQ approaches tractable for utility-scale wind plants.

17 WIND ENERGY↗

NASA NTRS1973

Digital simulation of the serpentuator using MARSYAS

Serpentuator is a serpentine teleoperator device for intravehicular and extravehicular activities in space. The serpentuator is simulated using simulation software system MARSYAS and using the Component-Connection Simulation model and the Direct Simulation model. A comparison of the results for the two cases shows that under identical conditions, simulation execution time in the Component-Connection model case is reduced by a factor of the order of 100. A visual display of the serpentuator positions is obtained using the AMTRAN system on the Datacraft DC 6024 computer.

Singh, S., P.↗

NASA NTRS1998

Analysis of earth albedo effect on sun sensor measurements based on theoretical model and mission experience

Analysis of flight data from previous missions indicates that anomalous Sun sensor readings could be caused by Earth albedo interference. A previous Sun sensor study presented a detailed mathematical model of this effect. The model can be used to study the effect of both diffusive and specular reflections and to improve Sun angle determination based on perturbed Sun sensor measurements, satellite position, and an approximate knowledge of attitude. The model predicts that diffuse reflected light can cause errors of up to 10 degrees in Coarse Sun Sensor (CSS) measurements and 5 to 10 arc sec in Fine Sun Sensor (FSS) measurements, depending on spacecraft orbit and attitude. The accuracy of these sensors is affected as long as part of the illuminated Earth surface is present in the sensor field of view. Digital Sun Sensors (DSS) respond in a different manner to the Earth albedo interference. Most of the time DSS measurements are not affected, but for brief periods of time the Earth albedo can cause errors which are a multiple of the sensor least significant bit and may exceed one degree. This paper compares model predictions with Tropical Rainfall Measuring Mission (TRMM) CSS measurements in order to validate and refine the model. Methods of reducing and mitigating the impact of Earth albedo are discussed. ne CSS sensor errors are roughly proportional to the Earth albedo coefficient. Photocells that are sensitive only to ultraviolet emissions would reduce the effective Earth albedo by up to a thousand times, virtually eliminating all errors caused by Earth albedo interference.

Brasoveanu, Dan↗

NASA NTRS2014

Preliminary Structural Sensitivity Study of Hypersonic Inflatable Aerodynamic Decelerator Using Probabilistic Methods

Acceptance of new spacecraft structural architectures and concepts requires validated design methods to minimize the expense involved with technology validation via flighttesting. This paper explores the implementation of probabilistic methods in the sensitivity analysis of the structural response of a Hypersonic Inflatable Aerodynamic Decelerator (HIAD). HIAD architectures are attractive for spacecraft deceleration because they are lightweight, store compactly, and utilize the atmosphere to decelerate a spacecraft during re-entry. However, designers are hesitant to include these inflatable approaches for large payloads or spacecraft because of the lack of flight validation. In the example presented here, the structural parameters of an existing HIAD model have been varied to illustrate the design approach utilizing uncertainty-based methods. Surrogate models have been used to reduce computational expense several orders of magnitude. The suitability of the design is based on assessing variation in the resulting cone angle. The acceptable cone angle variation would rely on the aerodynamic requirements.

Lyle, Karen H.↗

NASA NTRS2016

The Lick AGN Monitoring Project 2011: Spectroscopic Campaign and Emission-line Light Curves

In the Spring of 2011 we carried out a 2.5 month reverberation mapping campaign using the 3 m Shane telescope at Lick Observatory, monitoring 15 low-redshift Seyfert 1 galaxies. This paper describes the observations, reductions and measurements, and data products from the spectroscopic campaign. The reduced spectra were fitted with a multicomponent model in order to isolate the contributions of various continuum and emission-line components. We present light curves of broad emission lines and the active galactic nucleus (AGN) continuum, and measurements of the broad Hβ line widths in mean and rms spectra. For the most highly variable AGNs we also measured broad H beta line widths and velocity centroids from the nightly spectra. In four AGNs exhibiting the highest variability amplitudes, we detect anticorrelations between broad H beta width and luminosity, demonstrating that the broad-line region "breathes" on short timescales of days to weeks in response to continuum variations. We also find that broad H beta velocity centroids can undergo substantial changes in response to continuum variations; in NGC 4593, the broad H beta velocity shifted by approximately 250 km s(exp -1) over a 1 month period. This reverberation-induced velocity shift effect is likely to contribute a significant source of confusion noise to binary black hole searches that use multi-epoch quasar spectroscopy to detect binary orbital motion. We also present results from simulations that examine biases that can occur in measurement of broad-line widths from rms spectra due to the contributions of continuum variations and photon-counting noise.

galaxies: Seyfert↗

DOE OSTI2016

An Iterative Optimization Strategy for Nuclear Reactor Design

Optimization of a complex system involves the determination of optimum values for a set of design parameters to meet a specific set of objectives on the quantities of interest (QOI) wherein, the design parameters are a subset of the input parameters, and the QOIs are determined from the output parameters. Particularly, when the parameter space is large, optimization necessitates a significant number of executions of the simulator to obtain a desired solution in tolerance limits. When the simulations are expensive in terms of computation time, an emulator, based on regression methods is useful for predictions. This work presents a novel methodology that uses an iterative hybrid global optimization method (GOM) using genetic algorithms (GA), and simulated annealing (SA) model coupled (HYBGASA) with a Gaussian process regression method based emulator (GPMEM) to optimize a set of input parameters based on a set of defined objectives in a nuclear reactor power system. Hereafter this iterative hybrid method comprising of HYBGASA, and GPMEM would be called as 'IHGOM'. In addition to optimization, IHGOM iteratively updates the trial data, obtained from the neighborhood of the near optimal solution, used to train the GPMEM in order to reduce regression errors. The objective is to develop, model and analyze IHGOM and apply it to an optimization problem in the design of a nuclear reactor. Development and analysis of IHGOM and its implementation in a nuclear reactor power system problem is a significant contribution to the optimization and the nuclear engineering communities. The objective of this work is to develop IHGOM and apply it to an optimization problem in the design of a nuclear reactor. The research involves the development and analysis of a novel optimization strategy, IHGOM. The feasibility analysis involves the optimization of a set of defined parameters in the design and development of a complex system. A GCFBR reactor power system with a set of defined objectives and parameters based on criticality and depletion studies have been characterized as the complex system. Development and analysis of IHGOM and its implementation in an optimization problem in the design of a GCFBR is a significant contribution to the optimization and the nuclear engineering communities. In particular based on the literature review, a coupled GOM and GPMEM method for optimization and regression error reduction in nuclear engineering domain has never been explored. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1987

A scattering model for perfectly conducting random surfaces. I - Model development. II - Range of validity

The surface current on a perfectly conducting randomly rough surface is estimated by solving iteratively a standard integral equation, and the estimate is then used to compute the far-zone scattered fields and the backscattering coefficients for vertical, horizontal and cross polarizations. The model developed here yields a simple backscattering coefficient expression in terms of the surface parameters. The expression reduces analytically to the Kirchhoff and the first-order small-perturbation model in the high- and low-frequency regions, respectively. The range of validity of the model is determined.

Fung, A. K.↗

NASA NTRS1978

Computerized power supply analysis: State equation generation and terminal models

To aid engineers that design power supply systems two analysis tools that can be used with the state equation analysis package were developed. These tools include integration routines that start with the description of a power supply in state equation form and yield analytical results. The first tool uses a computer program that works with the SUPER SCEPTRE circuit analysis program and prints the state equation for an electrical network. The state equations developed automatically by the computer program are used to develop an algorithm for reducing the number of state variables required to describe an electrical network. In this way a second tool is obtained in which the order of the network is reduced and a simpler terminal model is obtained.

Garrett, S. J.↗

DOE OSTI2016

Experimental verification of an energy consumption signal tool for operational decision support in an office building

Not Available

Pavlak, Gregory S. (ORCID:0000000347340750)↗

NASA NTRS2009

Thermal Transport Model for Heat Sink Design

A document discusses the development of a finite element model for describing thermal transport through microcalorimeter arrays in order to assist in heat-sinking design. A fabricated multi-absorber transition edge sensor (PoST) was designed in order to reduce device wiring density by a factor of four. The finite element model consists of breaking the microcalorimeter array into separate elements, including the transition edge sensor (TES) and the silicon substrate on which the sensor is deposited. Each element is then broken up into subelements, whose surface area subtends 10 10 microns. The heat capacity per unit temperature, thermal conductance, and thermal diffusivity of each subelement are the model inputs, as are the temperatures of each subelement. Numerical integration using the Finite in Time Centered in Space algorithm of the thermal diffusion equation is then performed in order to obtain a temporal evolution of the subelement temperature. Thermal transport across interfaces is modeled using a thermal boundary resistance obtained using the acoustic mismatch model. The document concludes with a discussion of the PoST fabrication. PoSTs are novel because they enable incident x-ray position sensitivity with good energy resolution and low wiring density.

Chervenak, James A.↗

DOE OSTI2013

Performance assessment for the geological disposal of Deep Burn spent fuel using TTBX

The behavior of Deep Burn Modular High Temperature Reactor Spent Fuel (DBSF) is investigated in the Yucca Mountain geological repository (YMR) with respect to the annual dose (Sv/yr) delivered to the Reasonably Maximally Exposed Individual (RMEI) from the transport of radionuclides released from the graphite waste matrix. Transport calculations are performed with a novel computer code, TTBX which is capable of modeling transport pathways that pass through heterogeneous geological formations. TTBX is a multi-region extension of the existing single region TTB transport code. Overall the peak annual dose received by the RMEI is seen to be four orders of magnitude lower than the regulatory threshold for exposure, even under pessimistic scenarios. A number of factors contribute to the favorable performance of DBSF. A reduction of one order of magnitude in the peak annual dose received by the RMEI is observed for every order of magnitude increase in the waste matrix lifetime, highlighting the importance of the waste matrix durability and suggesting graphite's utility as a potential waste matrix for the disposal of high-level waste. Furthermore, we see that by incorporating a higher fidelity far-field model the peak annual dose calculated to be received by the RMEI is reduced by two orders of magnitude. By accounting for the heterogeneities of the far field we have simultaneously removed unnecessary conservatisms and improved the fidelity of the transport model. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2017

Model's sparse representation based on reduced mixed GMsFE basis methods

In this paper, we propose a model's sparse representation based on reduced mixed generalized multiscale finite element (GMsFE) basis methods for elliptic PDEs with random inputs. A typical application for the elliptic PDEs is the flow in heterogeneous random porous media. Mixed generalized multiscale finite element method (GMsFEM) is one of the accurate and efficient approaches to solve the flow problem in a coarse grid and obtain the velocity with local mass conservation. When the inputs of the PDEs are parameterized by the random variables, the GMsFE basis functions usually depend on the random parameters. This leads to a large number degree of freedoms for the mixed GMsFEM and substantially impacts on the computation efficiency. In order to overcome the difficulty, we develop reduced mixed GMsFE basis methods such that the multiscale basis functions are independent of the random parameters and span a low-dimensional space. To this end, a greedy algorithm is used to find a set of optimal samples from a training set scattered in the parameter space. Reduced mixed GMsFE basis functions are constructed based on the optimal samples using two optimal sampling strategies: basis-oriented cross-validation and proper orthogonal decomposition. Although the dimension of the space spanned by the reduced mixed GMsFE basis functions is much smaller than the dimension of the original full order model, the online computation still depends on the number of coarse degree of freedoms. To significantly improve the online computation, we integrate the reduced mixed GMsFE basis methods with sparse tensor approximation and obtain a sparse representation for the model's outputs. The sparse representation is very efficient for evaluating the model's outputs for many instances of parameters. To illustrate the efficacy of the proposed methods, we present a few numerical examples for elliptic PDEs with multiscale and random inputs. In particular, a two-phase flow model in random porous media is simulated by the proposed sparse representation method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2016

A robust real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system

Purpose: To develop a robust and real-time surface reconstruction method on point clouds captured from a 3D surface photogrammetry system. Methods: The authors have developed a robust and fast surface reconstruction method on point clouds acquired by the photogrammetry system, without explicitly solving the partial differential equation required by a typical variational approach. Taking advantage of the overcomplete nature of the acquired point clouds, their method solves and propagates a sparse linear relationship from the point cloud manifold to the surface manifold, assuming both manifolds share similar local geometry. With relatively consistent point cloud acquisitions, the authors propose a sparse regression (SR) model to directly approximate the target point cloud as a sparse linear combination from the training set, assuming that the point correspondences built by the iterative closest point (ICP) is reasonably accurate and have residual errors following a Gaussian distribution. To accommodate changing noise levels and/or presence of inconsistent occlusions during the acquisition, the authors further propose a modified sparse regression (MSR) model to model the potentially large and sparse error built by ICP with a Laplacian prior. The authors evaluated the proposed method on both clinical point clouds acquired under consistent acquisition conditions and on point clouds with inconsistent occlusions. The authors quantitatively evaluated the reconstruction performance with respect to root-mean-squared-error, by comparing its reconstruction results against that from the variational method. Results: On clinical point clouds, both the SR and MSR models have achieved sub-millimeter reconstruction accuracy and reduced the reconstruction time by two orders of magnitude to a subsecond reconstruction time. On point clouds with inconsistent occlusions, the MSR model has demonstrated its advantage in achieving consistent and robust performance despite the introduced occlusions. Conclusions: The authors have developed a fast and robust surface reconstruction method on point clouds captured from a 3D surface photogrammetry system, with demonstrated sub-millimeter reconstruction accuracy and subsecond reconstruction time. It is suitable for real-time motion tracking in radiotherapy, with clear surface structures for better quantifications.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Data-driven predictive probability density function control of fiber length stochastic distribution shaping in refining process

Pulp is the most important raw material in paper in paper industries, whose Fiber Length Stochastic Distribution (FLSD) shaping directly determines the energy consumption and paper quality of the subsequent papermaking processes. However, the mean and variance are insufficient to describe the output FLSD shaping, which display non-Gaussian distribution properties. Therefore, the traditional control, method based on the mean and variance of fiber length is difficult to control the FLSD shaping effectively. In this paper, a novel data-driven predictive probability density function (PDF) control method is proposed for output FLSD shaping in refining process. Primarily, in order to improve the approximation accuracy of the PDF employing RBF neural network, the parameters (center value and width) of each RBF basis functions are tuned via utilizing iterative learning control (ILC) law, and the corresponding estimations of weights law can be obtained. Secondly, considering that the conventional linear model of weights vector has drawbacks of low accuracy and weak generalization ability, random vector functional link networks (RVFLNs) based data driven nonlinear modelling method is employed to characterize the prediction model between the input variables and weights vector. Finally, in order to reduce the randomness of the output FLSD, a minimum entropy control method under mean constraint is employed to design the predictive PDF controller for the output FLSD shaping with the help of the established nonlinear stochastic distribution model. Both simulations and experiments are given to illustrate the effectiveness and practicability of the proposed method.

Probability debsity function, Fiber length distrib↗

DOE OSTI2023

Mean field model parameterization to recover coverage-dependent kinetics

Lateral interactions between adsorbates introduce coverage dependence into adsorption energies and activation barriers of surface reactions. Lattice-based kinetic Monte Carlo (kMC) simulations can capture these interactions quantitatively but are laborious to parameterize and solve. Mean field models are more tractable, but protocols to construct and parameterize them are unclear. Here we explore the ability of a coverage-aware mean-field model to map to a lattice-kMC model of a generic two-step reaction network, including quasi-equilibrated adsorption and rate-limiting dissociation steps. We derive expressions for mean-field and coverage-dependent adsorption energies and dissociation barriers and parameterize against lattice-kMC predictions. We show that the parameterized mean-field rates correlate with ground truth lattice-kMC results across a wide range of reaction conditions and identify regions where the mean field fails. The mean field model similarly captures kMC-derived rate-order, Arrhenius and Sabatier plots at a greatly reduced computational cost. Further, the results provide guidance for parameterizing mean-field models, benchmarked against explicit lattice-based approaches for incorporating the influence of coverage effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Measurement of the inclusive and fiducial t t ¯ production cross-sections in the lepton+jets channel in pp collisions at s = 8   TeV with the ATLAS detector

The inclusive and fiducial t t ¯ production cross-sections are measured in the lepton+jets channel using 20.2   fb − 1 of proton–proton collision data at a centre-of-mass energy of 8 TeV recorded with the ATLAS detector at the LHC. Major systematic uncertainties due to the modelling of the jet energy scale and b-tagging efficiency are constrained by separating selected events into three disjoint regions. In order to reduce systematic uncertainties in the most important background, the W +\,jets process is modelled using Z+ jets events in a data-driven approach. The inclusive t t ¯ cross-section is measured with a precision of 5.7% to be σ inc ( t t ¯ ) = 248.3 ± 0.7 ( s t a t . ) ± 13.4 ( s y s t . ) ± 4.7 ( l u m i . )   pb , assuming a top-quark mass of 172.5 GeV. The result is in agreement with the Standard Model prediction. The cross-section is also measured in a phase space close to that of the selected data. The fiducial cross-section is σ fid ( t t ¯ ) = 48.8 ± 0.1 ( s t a t . ) ± 2.0 ( s y s t . ) ± 0.9 ( l u m i . )   pb with a precision of 4.5%.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗