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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Simulation studies of R 2 (Δη,Δφ) and P 2 (Δη,Δφ) correlation functions in pp collisions with the PYTHIA and HERWIG models

We report studies of charge-independent (CI) and charge-dependent (CD) two-particle differential-number correlation functions, R 2 (Δη,Δφ), and transverse momentum (p T ) correlation functions, P 2 (Δη,Δφ), of charged particles in √s = 2.76 TeV pp collisions with the PYTHIA and HERWIG models. Model predictions are presented for inclusive charged hadrons (h ± ), as well as pions (π ± ), kaons (K ± ), and (anti-)protons (p¯/p) in the ranges 0.2 < p T ≤ 2.0 GeV/c, 2.0 < p T ≤ 5.0 GeV/c, and 5.0 < p T ≤ 30.0 GeV/c, with full azimuthal coverage in the range |η| < 1.0. We compare the model predictions for the strength and shape of the R 2 and P 2 correlators as these pertain to recent measurements by the ALICE collaboration. The R 2 and P 2 correlation functions estimated with PYTHIA and HERWIG exhibit qualitatively similar near-side and away-side correlation structures but feature important differences. Here, our analysis indicates that comparative studies of R 2 and P 2 correlation functions would provide valuable insight towards the understanding of particle production in pp collisions, and by extension, should also be useful in studies of heavy-ion collisions. Comparison of the Δη dependence of R 2 and P 2 could contribute, in particular, to a better understanding and modeling of the angular ordering of particles produced by hadronization in jets, as well as a better description of jet fragmentation functions of identified species at low momentum fraction (z).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Snake River Plain Geothermal Play Fairway Analysis Project MT Data

MT is measured in the field by using induction coils to measure the time-varying magnetic source for frequencies between 1000-0.001~Hz, and electric dipoles to measure the Earth's electrical response. Because the magnetic source field is polarized, orthogonal directions of the fields need to be measured to get a complete description of the fields. In all measurements collected for this project, induction coils and electric dipoles were aligned with geomagnetic north and east. MT data were collected at 22 stations with a ZEN 32-bit data logger developed by Zonge International, magnetic fields were measured with ANT-4 induction coils, and electric fields where measured with Ag-AgCl reference electrodes from Borin on 50~m dipoles. The data was collected on a repeating schedule of 10~min at 4096~samples/s and 7 hours and 50 minutes at 256 samples/s over a 20-24 hour period. To convert time series data into the frequency domain and get estimations of the impedance tensor, the processing code BIRRP was used (Chave & Thompson 2004). Simultaneous measurements were used as remote references to reduce noise and bias in the data. Chave, A. D., & Thomson, D. J. 2004. Bounded inuence magnetotelluric response function estimation. Geophys. J. Int., 157, 988-1006.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

GIWAXS-SIIRkit: scattering intensity, indexing and refraction calculation toolkit for grazing-incidence wide-angle X-ray scattering of organic materials

Grazing-incidence wide-angle X-ray scattering (GIWAXS) has become an increasingly popular technique for quantitative structural characterization and comparison of thin films. For this purpose, accurate intensity normalization and peak position determination are crucial. At present, few tools exist to estimate the uncertainties of these measurements. Here, a simulation package is introduced called GIWAXS-SIIRkit , where SIIR stands for scattering intensity, indexing and refraction. Furthermore, the package contains several tools that are freely available for download and can be executed in MATLAB. The package includes three functionalities: estimation of the relative scattering intensity and the corresponding uncertainty based on experimental setup and sample dimensions; extraction and indexing of peak positions to approximate the crystal structure of organic materials starting from calibrated GIWAXS patterns; and analysis of the effects of refraction on peak positions. Additionally, each tool is based on a graphical user interface and designed to have a short learning curve. A user guide is provided with detailed usage instruction, tips for adding functionality and customization, and exemplary files.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Anticipating global terrestrial ecosystem state change using FLUXNET

Ecosystems can be characterized as complex systems that traverse a variety of functional and structural states in response to changing bioclimatic forcings. A central challenge of global change biology is the robust empirical description of these states and state transitions. An ecosystem's functional state can be empirically described using Process Networks (PN) that use timeseries observations to determine the strength of process-level functional couplings between ecosystem components. A globally extensive source of in-situ observations of terrestrial ecosystem dynamics is the FLUXNET eddy-covariance network that provides standardized observations of micrometeorology and carbon, water, and energy flux dynamics. We employ the LaThuile FLUXNET synthesis dataset to delineate each month's functional state for 204 sites, yielding the LaThuile PN version 1.0 database that describes the strength of an ecosystem's functional couplings from air temperature and precipitation to carbon fluxes during each site-month. Then we calculate the elasticity of these couplings to seasonal scale forcings: air temperature, precipitation, solar radiation, and phenophase. Finally, we train artificial neural networks to extrapolate these elasticities from 204 sites to the globe, yielding maps of the estimated functional elasticity of every terrestrial ecosystem's functional states to changing seasonal bioclimatic forcings. These maps provide theoretically novel resource that can be used to anticipate ecological state transitions in response to climate change and to validate process-based models of ecological change. These elasticity maps show that each ecosystem can be expected to respond uniquely to changing forcings. Tropical forests, hot deserts, savannas, and high elevations are most elastic to climate change, and elasticity of ecosystems to seasonal air temperature is on average an order of magnitude higher than elasticity to other bioclimatic forcings. We also observe a reasonable amount of moderate relationships between functional elasticity and structural state change across different ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Sparse Bayesian Regression with Integrated Feature Selection for Nuclear Reactor Analysis

High-dimensional, nonlinear function estimation using large datasets is a current area of interest in the machine learning community, and applications may be found throughout the analytical sciences, where ever-growing datasets are making more information available to the analyst. In this paper, we leverage the existing relevance vector machine, a sparse Bayesian version of the well-studied support vector machine, and expand the method to include integrated feature selection and automatic function shaping. These innovations produce an algorithm that is able to distinguish variables that are useful for making predictions of a response from variables that are unrelated or confusing. We test the technology using synthetic data, conduct initial performance studies, and develop a model capable of making position-independent predictions of the coreaveraged burnup using a single specimen drawn randomly from a nuclear reactor core.

Dayman, Ken J↗
DOE OSTI2019

Computing three-point correlation function randoms counts without the randoms catalogue

ABSTRACT As we move towards future galaxy surveys, the three-point statistics will be increasingly leveraged to enhance the constraining power of the data on cosmological parameters. An essential part of the three-point function estimation is performing triplet counts of synthetic data points in random catalogues. Since triplet counting algorithms scale at best as $\mathcal {O}(N^2\log N)$ with the number of particles and the random catalogues are typically at least 50 times denser than the data; this tends to be by far the most time-consuming part of the measurements. Here, we present a simple method of computing the necessary triplet counts involving uniform random distributions through simple one-dimensional integrals. The method speeds up the computation of the three-point function by orders of magnitude, eliminating the need for random catalogues, with the simultaneous pair and triplet counting of the data points alone being sufficient.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Position-dependent power spectrum of the large-scale structure: a novel method to measure the squeezed-limit bispectrum

The influence of large-scale density fluctuations on structure formation on small scales is described by the three-point correlation function (bispectrum) in the so-called ''squeezed configurations,'' in which one wavenumber, say k{sub 3}, is much smaller than the other two, i.e., k{sub 3} << k{sub 1} ≈ k{sub 2}. This bispectrum is generated by non-linear gravitational evolution and possibly also by inflationary physics. In this paper, we use this fact to show that the bispectrum in the squeezed configurations can be measured without employing three-point function estimators. Specifically, we use the ''position-dependent power spectrum,'' i.e., the power spectrum measured in smaller subvolumes of the survey (or simulation box), and correlate it with the mean overdensity of the corresponding subvolume. This correlation directly measures an integral of the bispectrum dominated by the squeezed configurations. Measuring this correlation is only slightly more complex than measuring the power spectrum itself, and sidesteps the considerable complexity of the full bispectrum estimation. We use cosmological N-body simulations of collisionless particles with Gaussian initial conditions to show that the measured correlation between the position-dependent power spectrum and the long-wavelength overdensity agrees with the theoretical expectation. The position-dependent power spectrum thus provides a new, efficient, and promising way to measure the squeezed-limit bispectrum from large-scale structure observations such as galaxy redshift surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Enhanced dynamic equivalent identification method of large-scale power systems using multiple events

The increasing complexity of the interconnected power system makes high-fidelity dynamic simulation models computationally more intensive. To improve computation efficiency, model reduction techniques have been investigated to only preserve the dynamics in a limited area of interest (study area), while deriving equivalent representation for the external area. For this purpose, a measurement-based reduction approach using system identification techniques has been previously proposed. Accordingly, external areas are represented by dynamic equivalent loads using transfer function estimation. In this paper, in order to enhance the accuracy of the reduced model in preserving dynamics of the study area, multiple “grid” events of different types and at different locations are considered for identifying the parameters of the equivalent loads. Additionally, case studies are carried out in the NPCC 140-bus system. Comparisons are made between multiple events training and single event training, highlighting the advantages of the proposed method in providing a better representation of the grid dynamics under different operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

An improved multilevel Monte Carlo method for estimating probability distribution functions in stochastic oil reservoir simulations

In this paper, we develop an improved multilevel Monte Carlo (MLMC) method for estimating cumulative distribution functions (CDFs) of a quantity of interest, coming from numerical approximation of large-scale stochastic subsurface simulations. Compared with Monte Carlo (MC) methods, that require a significantly large number of high-fidelity model executions to achieve a prescribed accuracy when computing statistical expectations, MLMC methods were originally proposed to significantly reduce the computational cost with the use of multifidelity approximations. The improved performance of the MLMC methods depends strongly on the decay of the variance of the integrand as the level increases. However, the main challenge in estimating CDFs is that the integrand is a discontinuous indicator function whose variance decays slowly. To address this difficult task, we approximate the integrand using a smoothing function that accelerates the decay of the variance. In addition, we design a novel a posteriori optimization strategy to calibrate the smoothing function, so as to balance the computational gain and the approximation error. The combined proposed techniques are integrated into a very general and practical algorithm that can be applied to a wide range of subsurface problems for high-dimensional uncertainty quantification, such as a fine-grid oil reservoir model considered in this effort. The numerical results reveal that with the use of the calibrated smoothing function, the improved MLMC technique significantly reduces the computational complexity compared to the standard MC approach. Finally, we discuss several factors that affect the performance of the MLMC method and provide guidance for effective and efficient usage in practice.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Adaptive Sampling-Based Bi-Fidelity Stochastic Trust Region Method for Stochastic Derivative-Free Optimization

Bi-fidelity stochastic optimization has gained increasing attention as an efficient approach to reduce computational costs by leveraging a low-fidelity (LF) model to optimize an expensive high-fidelity (HF) objective. In this paper, we propose ASTRO-BFDF, an adaptive sampling trust-region method specifically designed for unconstrained bi-fidelity stochastic derivative-free optimization problems. In ASTRO-BFDF, the LF function serves two purposes: (i) to identify better iterates for the HF function when the optimization process indicates a high correlation between them and (ii) to reduce the variance of the HF function estimates using bi-fidelity Monte Carlo (BFMC). The algorithm dynamically determines sample sizes while adaptively choosing between crude Monte Carlo and BFMC to balance the trade-off between optimization and sampling errors. We prove that the iterates generated by ASTRO-BFDF converge to a first-order stationary point almost surely. Additionally, we demonstrate the effectiveness of the proposed algorithm through numerical experiments on synthetic benchmarks and simulation optimization problems involving discrete event systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Microgrid energy scheduling under uncertain extreme weather: Adaptation from parallelized reinforcement learning agents

Microgrids are useful solutions for integrating renewable energy resources and providing seamless green electricity to minimize carbon footprint. In recent years, extreme weather events happened often worldwide and caused significant economic and societal losses. Such events bring uncertainties to the microgrid energy scheduling problems and increase the challenges of microgrid operation. Traditional optimization approaches suffer from the inaccuracy of the uncertain microgrid model and the unseen events. Existing reinforcement learning (RL) - based approaches are also hampered by the limited generalization and the increasing computational burden when stochastic formulations are required to accommodate the uncertainties. This paper proposes a new parallelized reinforcement learning (PRL) method based on the probabilistic events to handle the microgrid energy uncertainties. Specifically, several local learning agents are employed to interact with pertinent microgrid environments in a distributed manner and report outcomes to the global agent, which will optimize microgrid energy resources online during extreme events. The stochastic microgrid energy optimization problem is reformulated to include all possible scenarios with probabilities. The advantage estimate functions of learning agents are designed with a backward sweep to transfer the outcomes to the value function updating process. Two simulation studies, stochastic optimization and online testing, are performed to compare with several existing RL approaches. Results substantiate that the proposed PRL method can achieve up to 20% optimization performance improvement with 4 and 28 times less computation cost than Q-learning with experience replay and multi-agent Q-learning approaches, respectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Structures of Vanadium-Containing Silicate and Borosilicate Glasses: Vanadium Potential Development and MD Simulations

Vanadium-containing multi-component glasses have been found with widely interests in various applications. However, the complex compositions, thus structures, lead to the challenges in experiments to understand the structural origin of the property change of these glasses. In this work, we developed compatible vanadium-related parameters for a widely used pair wise potential set to enable the simulations of the vanadium-containing multi-component glasses. Various crystal structures and glass compositions (with vanadium in different oxidation states) have been tested using the newly developed parameters, and structural information of cell parameters, pair distribution function, estimated bond distance, and coordination number have been obtained. The results are in good agreement with available experimental data, which indicates the new vanadium parameters can be used to simulate the vanadium-containing multi-component glasses and thus help study those applications with wide interests.

Deng, Lu↗
DOE OSTI2022

Accurate predictions from small boxes: variance suppression via the Zel'dovich approximation

Simulations have become an indispensable tool for accurate modelling of observables measured in galaxy surveys, but can be expensive if very large dynamic range in scale is required. We describe how to combine Lagrangian perturbation theory models with N-body simulations to reduce the effects of finite computational volume in the prediction of ensemble average properties in the simulations within the context of control variates. In particular we use the fact that Zel'dovich displacements, computed during initial condition generation for any simulation, correlate strongly with the final density field. Since all the correlators of biased tracers can be computed with arbitrary precision for these displacements, pairing the Zel'dovich `simulation' with the N-body realization allows hundredfold reductions in sample variance for power spectrum or correlation function estimation. Additionally, Zel'dovich control variates can accurately extend matter or tracer field emulators to larger scales than previously possible, as well as improving measurements of statistics in simulations which are inherently limited to small volumes, such as hydrodynamical simulations of galaxy formation and reionization.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Investigation of fast particle redistribution induced by sawtooth instability in NSTX-U

The effects of sawtooth on fast ion transport have been examined in reproducible, 2s long sawtoothing L-mode discharges during the 2016 experimental campaign on National Spherical Torus Experiment Upgrade (NSTX-U) [J.E. Menard et al. Nucl. Fusion (2012) 083015]. Analysis of the discharges showed that standard sawtooth models (full/partial reconnection models) in the TRANSP code were not capable to fully reproduce the fast ion redistribution induced by sawtooth crashes. Some global parameters such as neutron rate can be recovered while detailed features, e.g. distribution functions, estimated using the models were different from the experimental observation. The standard sawtooth models in TRANSP do not take into account the different effect of sawtooth crashes depending on fast ion energy and orbit type and that may cause the disagreement between experiments and simulations. In this work, the newly developed kick model has been applied to replace the standard sawtooth models for the fast ion transport. TRANSP simulation results using the kick model, taking into account the characteristics of fast ion such as energy and pitch angle, can reproduce experimental neutron rates within 10\% difference. The qualitative comparison of the measurements and synthetic diagnostics of FIDA and SSNPA using the TRANSP simulation results with kick model demonstrates good agreements.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Computation of pseudospectral abscissa for large-scale nonlinear eigenvalue problems

We present an algorithm to compute the pseudospectral abscissa for a nonlinear eigenvalue problem. The algorithm relies on global under-estimator and over-estimator functions for the eigenvalue and singular value functions involved. These global models follow from eigenvalue perturbation theory. The algorithm has three particular features. First, it converges to the globally rightmost point of the pseudospectrum, and it is immune to nonsmoothness. The global convergence assertion is under the assumption that a global lower bound is available for the second derivative of a singular value function depending on one parameter. It may not be easy to deduce such a lower bound analytically, but assigning large negative values works robustly in practice. Second, it is applicable to large-scale problems since the dominant cost per iteration stems from computing the smallest singular value and associated singular vectors, for which efficient iterative solvers can be used. Furthermore, a significant increase in computational efficiency can be obtained by subspace acceleration, that is, by restricting the domains of the linear maps associated with the matrices involved to small but suitable subspaces, and solving the resulting reduced problems. Occasional restarts of these subspaces further enhance the efficiency for large-scale problems. Finally, in contrast to existing iterative approaches based on constructing low-rank perturbations and rightmost eigenvalue computations, the algorithm relies on computing only singular values of complex matrices. Hence, the algorithm does not require solutions of nonlinear eigenvalue problems, thereby further increasing efficiency and reliability. This work is accompanied by a robust implementation of the algorithm that is publicly available.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

The DES Science Verification weak lensing shear catalogues

Here, we present weak lensing shear catalogues for 139 square degrees of data taken during the Science Verification (SV) time for the new Dark Energy Camera (DECam) being used for the Dark Energy Survey (DES). We also describe our object selection, point spread function estimation and shear measurement procedures using two independent shear pipelines, IM3SHAPE and NGMIX, which produce catalogues of 2.12 million and 3.44 million galaxies, respectively. Furthermore, we detail a set of null tests for the shear measurements and find that they pass the requirements for systematic errors at the level necessary for weak lensing science applications using the SV data. Finally, we discuss some of the planned algorithmic improvements that will be necessary to produce sufficiently accurate shear catalogues for the full 5-yr DES, which is expected to cover 5000 square degrees.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Aviation Transportation, Cyber Threats, and Network-of-Networks: Modeling Perspectives for Translating Theory to Practice

Understanding aviation transportation infrastructure system behavior and coupling with communication networks is essential for securing and restoring functionality against cyber-enabled threats. While significant progress has been made in the past decade on developing infrastructure resilience theories based on network structure and operations, translating and generalizing them to real-world practice has often been challenging due to imperfect data and inapplicability of modeling assumptions. These typically include: 1) stylized network structures without uncertainty, 2) node homogeneity, 3) static criticality measures, and 4) unrealistic cascade models originating from single points of failure. This paper presents modeling perspectives that aim to address these theory-to-practice challenges using a well-grounded network-of-networks (NoN) construct. Real-world modeling challenges are identified and a network theory-guided conceptual NoN model is developed that may be operationalized with the U.S. national airspace system airport network (NASAN) and Federal Aviation Administration (FAA) communication network as application domain. Improving accuracy in loss of functionality estimates due to cyber events in communication networks and cascading air travel delay impacts is key for informing consequence-based decision support and risk/contingency analyses.

Bhatia, Udit↗
DOE OSTI2023

Bayesian GAN-Based False Data Injection Attack Detection in Active Distribution Grids With DERs

Advancements in information and communication technologies have revolutionized monitoring and control capabilities within smart grids. However, it also brings new vulnerabilities to data acquisition systems and state estimation functions, which attackers can subtly tamper with the measurement data through compromising the communication network. Moreover, the high penetration of renewable energy sources with the inherited characteristics of uncertainty and variability further complicates the design of effective intrusion detection systems. In this paper, a Bayesian deep learning-based approach is developed to detect cyber attacks and maintain the security of smart grids. Our method specifically addresses the prevalent issue of imbalanced data in real power systems, which arises from the predominance of normal system operations over compromised or attacked states. Employing a novel Bayesian GAN-based technique, our approach successfully discriminates between secure and compromised measurement data, even in scenarios with significant data imbalance. Furthermore, the proposed method accommodates various practical application factors, ensuring accurate intrusion detection despite the presence of measurement noise. The feasibility and effectiveness of the proposed detection mechanism are validated by testing on IEEE 13-node and 123-node test systems. Simulation results and comparisons with literature methods demonstrate the superiority of proposed cybersecurity solutions.

Bayesian GAN↗