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At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Model predictive control for matrix converter operating in current control mode with load current estimation

A matrix converter system operating in current control mode is provided. The matrix converter includes a switching matrix coupled between a low voltage side and a high voltage side, wherein the matrix converter is coupled at its low voltage side to a generator for receiving an input power including an input current and transforming the input power into an output power at its high voltage side, wherein a load is coupled to the high voltage side. The control system includes a load observer and a model predictive controller (MPC). The load observer is configured to estimate a load current that flows to the load from the high voltage side of the matrix converter as a function of a switching state of the switching matrix, an output voltage output at the high voltage side of the matrix converter, and the input current. The MPC is configured to select the switching state of the switching matrix to meet one or more control objectives defined by minimization of a multi-objective function that tracks the output voltage and the input current using the estimated load current.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

ESTIMATING BLACK HOLE MASSES IN HUNDREDS OF QUASARS

We explore the practical feasibility of active galactic nucleus (AGN) broadband reverberation mapping and present first results. We lay out and apply a rigorous approach for the stochastic reverberation mapping of unevenly sampled multi-broadband flux measurements, assuming that the broad-line region (BLR) line flux is contributing up to 15% in some bands, and is directly constrained by one spectroscopical epoch. The approach describes variations of the observed flux as the continuum, modeled as a stochastic Gaussian process, and emission line contribution, modeled as a scaled, smoothed, and delayed version of the continuum. This approach can be used not only to interpolate in time between measurements, but also to determine confidence limits on continuum—line emission delays. This approach is applied to Sloan Digital Sky Survey observations in Stripe 82 (S82), providing flux measurements that are precise to 2% at ∼60 epochs over ∼10 yr. The strong annual variations in the epoch sampling prove a serious limitation in practice. In addition, suitable redshift ranges must be identified where strong, broad emission lines contribute to one filter, but not to another. By generating and evaluating problem-specific mock data, we verify that S82-like data can constrain τ{sub delay} for a simple transfer function model. In application to real data, we estimate τ{sub delay} for 323 AGNs with 0.225 < z < 0.846, combining information for different objects through the ensemble-scaling relationships for BLR size and black hole mass. Our analysis tentatively indicates a 1.7 times larger BLR size of Hα and Mg II compared to Kaspi et al. and Vestergaard, but the seasonal data sampling casts doubt on the robustness of the inference.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Automating Component-Level Stress Measurements for Inverter Reliability Estimation

In the near future, grid operators are expected to regularly use advanced distributed energy resource (DER) functions, defined in IEEE 1547-2018, to perform a range of grid-support operations. Many of these functions adjust the active and reactive power of the device through commanded or autonomous operating modes which induce new stresses on the power electronics components. In this work, an experimental and theoretical framework is introduced which couples laboratory-measured component stress with advanced inverter functionality and derives a reduction in useful lifetime based on an applicable reliability model. Multiple DER devices were instrumented to calculate the additional component stress under multiple reactive power setpoints to estimate associated DER lifetime reductions. A clear increase in switch loss was demonstrated as a function of irradiance level and power factor. This is replicated in the system-level efficiency measurements, although magnitudes were different—suggesting other loss mechanisms exist. Using an approximate Arrhenius thermal model for the switches, the experimental data indicate a lifetime reduction of 1.5% when operating the inverter at 0.85 PF—compared to unity PF—assuming the DER failure mechanism thermally driven within the H-bridge. If other failure mechanisms are discovered for a set of power electronics devices, this testing and calculation framework can easily be tailored to those failure mechanisms.

Flicker, Jack (ORCID:0000000212232154)↗
DOE OSTI2017

Reducing Uncertainties in the Production of the Gamma-emitting Nuclei {sup 26}Al, {sup 44}Ti, and {sup 60}Fe in Core-collapse Supernovae by Using Effective Helium Burning Rates

We have used effective reaction rates (ERRs) for the helium burning reactions to predict the yield of the gamma-emitting nuclei {sup 26}Al, {sup 44}Ti, and {sup 60}Fe in core-collapse supernovae (SNe). The variations in the predicted yields for values of the reaction rates allowed by the ERR are much smaller than obtained previously, and smaller than other uncertainties. A “filter” for SN nucleosynthesis yields based on pre-SN structure was used to estimate the effect of failed SNe on the initial mass function averaged yields; this substantially reduced the yields of all these isotopes, but the predicted yield ratio {sup 60}Fe/{sup 26}Al was little affected. The robustness of this ratio is promising for comparison with data, but it is larger than observed in nature; possible causes for this discrepancy are discussed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Critical role of morphology on the dielectric constant of semicrystalline polyolefins

A particularly attractive method to predict the dielectric properties of materials is density functional theory (DFT). While this method is very popular, its large computational requirements allow practical treatments of unit cells with just a small number of atoms in an ordered array, i.e., in a crystalline morphology. By comparing DFT and Molecular Dynamics (MD) simulations on the same ordered arrays of functional polyolefins, we confirm that both methodologies yield identical estimates for the dipole moments and hence the ionic component of the dielectric storage modulus. Additionally, MD simulations of more realistic semi-crystalline morphologies yield estimates for this polar contribution that are in good agreement with the limited experiments in this field. However, these predictions are up to 10 times larger than those for pure crystalline simulations. Here, we show that the constraints provided by the surrounding chains significantly impede dipolar relaxations in the crystalline regions, whereas amorphous chains must sample all configurations to attain their fully isotropic spatial distributions. These results, which suggest that the amorphous phase is the dominant player in the context, argue strongly that the proper polymer morphology needs to be modeled to ensure accurate estimates of the ionic component of the dielectric constant.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

A methodology to identify and quantify mobility-reducing defects in 4H-silicon carbide power metal-oxide-semiconductor field-effect transistors

In this paper, we present a methodology for the identification and quantification of defects responsible for low channel mobility in 4H-Silicon Carbide (SiC) power metal-oxide-semiconductor field-effect transistors (MOSFETs). To achieve this, we use an algorithm based on 2D-device simulations of a power MOSFET, density functional simulations, and measurement data. Using physical modeling of carrier mobility and interface traps, we reproduce the experimental I-V characteristics of a 4H-SiC doubly implanted MOSFET through drift-diffusion simulation. We extract the position of Fermi level and the occupied trap density as a function of applied bias and temperature. Using these inputs, our algorithm estimates the number of possible trap types, their energy levels, and concentrations at 4H-SiC/SiO{sub 2} interface. Subsequently, we use density functional theory (DFT)-based ab initio simulations to identify the atomic make-up of defects causing these trap levels. We study silicon vacancy and carbon di-interstitial defects in the SiC side of the interface. Our algorithm indicates that the D{sub it} spectrum near the conduction band edge (3.25 eV) is composed of three trap types located at 2.8–2.85 eV, 3.05 eV, and 3.1–3.2 eV, and also calculates their densities. Based on DFT simulations, this work attributes the trap levels very close to the conduction band edge to the C di-interstitial defect.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2012

Simple Limits on Achieving A Quasi-Linear Magnetic Compression for an FEL Driver

Free electron lasers (FEL) need a very bright electron beam in three dimensions and a high peak charge density. In order to compress an initially longer electron bunch generated from the photoinjector, magnetic bunch compression systems are widely employed. In this paper, first harmonic RF linearization and its associated requirements are reviewed. Meanwhile it is also briefly discussed what is the relation between a proper initial bunch length and main RF frequency, when a harmonic RF linearization is included. Then given a reasonable bunch compression ratio, a proper initial bunch length as a function of the main RF frequency and RF phase is estimated analytically by several approaches, assuming that no harmonic RF section is needed to linearize the energy modulation introduced during main RF acceleration, and at the same time still linearly compress the bunch length. Next the upper limit of the bunch compression ratio in a single stage is evaluated analytically. The analytical relations derived on choosing a proper initial bunch length as a function of main RF frequency are confirmed by numerical simulation. These simple limit provide rough estimations and may be beneficial for choosing bunch compression ratios in different stages of an FEL driver, especially in a first stage bunch compression where there is usually a harmonic RF linearization applied. It may also be useful in evaluating the possibility of low charge operation mode without any harmonic RF linearization, where a shorter initial bunch length can be achieved from the photoinjector.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

A three-point velocity estimation method for two-dimensional coarse-grained imaging data

Time delay and velocity estimation methods have been widely studied subjects in the context of signal processing, with applications in many different fields of physics. The velocity of waves or coherent fluctuation structures is commonly estimated as the distance between two measurement points divided by the time lag that maximizes the cross correlation function between the measured signals, but this is demonstrated to result in erroneous estimates for two spatial dimensions. We present an improved method to accurately estimate both components of the velocity vector, relying on three non-aligned measurement points. We introduce a stochastic process describing the fluctuations as a superposition of uncorrelated pulses moving in two dimensions. Using this model, we show that the three-point velocity estimation method, using time delays calculated through cross correlations, yields the exact velocity components when all pulses have the same velocity. The two- and three-point methods are tested on synthetic data generated from realizations of such processes for which the underlying velocity components are known. The results reveal the superiority of the three-point technique. Finally, we demonstrate the applicability of the velocity estimation on gas puff imaging data of strongly intermittent plasma fluctuations due to the radial motion of coherent, blob-like structures at the boundary of the Alcator C-Mod tokamak.

Materials Science↗
DOE OSTI2025

Validation of the DESI 2024 Lyα forest BAO analysis using synthetic datasets

The first year of data from the Dark Energy Spectroscopic Instrument (DESI) contains the largest set of Lyman-α (Lyα) forest spectra ever observed. This data, collected in the DESI Data Release 1 (DR1) sample, has been used to measure the Baryon Acoustic Oscillation (BAO) feature at redshift z = 2.33. In this work, we use a set of 150 synthetic realizations of DESI DR1 to validate the DESI 2024 Lyα forest BAO measurement presented in [1]. The synthetic data sets are based on Gaussian random fields using the log-normal approximation. We produce realistic synthetic DESI spectra that include all major contaminants affecting the Lyα forest. The synthetic data sets span a redshift range 1.8 < z < 3.8, and are analyzed using the same framework and pipeline used for the DESI 2024 Lyα forest BAO measurement. To measure BAO, we use both the Lyα auto-correlation and its cross-correlation with quasar positions. We use the mean of correlation functions from the set of DESI DR1 realizations to show that our model is able to recover unbiased measurements of the BAO position. We also fit each mock individually and study the population of BAO fits in order to validate BAO uncertainties and test our method for estimating the covariance matrix of the Lyα forest correlation functions. Finally, we discuss the implications of our results and identify the needs for the next generation of Lyα forest synthetic data sets, with the top priority being to simulate the effect of BAO broadening due to non-linear evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Active learning with multifidelity modeling for efficient rare event simulation

Here, while multifidelity modeling provides a cost-effective way to conduct uncertainty quantification with computationally expensive models, much greater efficiency can be achieved by adaptively deciding the number of required high-fidelity (HF) simulations, depending on the type and complexity of the problem and the desired accuracy in the results. We propose a framework for active learning with multifidelity modeling emphasizing the efficient estimation of rare events. Our framework works by fusing a low-fidelity (LF) prediction with an HF-inferred correction, filtering the corrected LF prediction to decide whether to call the high-fidelity model, and for enhanced subsequent accuracy, adapting the correction for the LF prediction after every HF model call. The framework does not make any assumptions as to the LF model type or its correlations with the HF model. In addition, for improved robustness when estimating smaller failure probabilities, we propose using dynamic active learning functions that decide when to call the HF model. We demonstrate our framework using several academic case studies (including some high-dimensional problems) and two finite element model case studies: estimating Navier-Stokes velocities using the Stokes approximation and estimating stresses in a transversely isotropic model subjected to displacements via a coarsely meshed isotropic model. Across these case studies, not only did the proposed framework estimate the failure probabilities accurately, but compared with either Monte Carlo or a standard variance reduction method, it also required only a small fraction of the calls to the HF model.

42 ENGINEERING↗
DOE OSTI2015

TOWARD UNDERSTANDING STELLAR RADIAL VELOCITY JITTER AS A FUNCTION OF WAVELENGTH: THE SUN AS A PROXY

Using solar spectral irradiance measurements from the SORCE spacecraft and the F/F' technique, we have estimated the radial velocity (RV) scatter induced on the Sun by stellar activity as a function of wavelength. Our goal was to evaluate the potential advantages of using new near-infrared (NIR) spectrographs to search for low-mass planets around bright F, G, and K stars by beating down activity effects. Unlike M dwarfs, which have higher fluxes and therefore greater RV information content in the NIR, solar-type stars are brightest at visible wavelengths, and, based solely on information content, are better suited to traditional optical RV surveys. However, we find that the F/F' estimated RV noise induced by stellar activity is diminished by up to a factor of four in the NIR versus the visible. Observations with the upcoming future generation of NIR instruments can be a valuable addition to the search for low-mass planets around bright FGK stars in reducing the amount of stellar noise affecting RV measurements.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Numerical methods for high-dimensional probability density function equations

In this paper we address the problem of computing the numerical solution to kinetic partial differential equations involving many phase variables. These types of equations arise naturally in many different areas of mathematical physics, e.g., in particle systems (Liouville and Boltzmann equations), stochastic dynamical systems (Fokker–Planck and Dostupov–Pugachev equations), random wave theory (Malakhov–Saichev equations) and coarse-grained stochastic systems (Mori–Zwanzig equations). We propose three different classes of new algorithms addressing high-dimensionality: The first one is based on separated series expansions resulting in a sequence of low-dimensional problems that can be solved recursively and in parallel by using alternating direction methods. The second class of algorithms relies on truncation of interaction in low-orders that resembles the Bogoliubov–Born–Green–Kirkwood–Yvon (BBGKY) framework of kinetic gas theory and it yields a hierarchy of coupled probability density function equations. The third class of algorithms is based on high-dimensional model representations, e.g., the ANOVA method and probabilistic collocation methods. A common feature of all these approaches is that they are reducible to the problem of computing the solution to high-dimensional equations via a sequence of low-dimensional problems. The effectiveness of the new algorithms is demonstrated in numerical examples involving nonlinear stochastic dynamical systems and partial differential equations, with up to 120 variables.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Mechanistic insights into heterogeneous methane activation

While natural gas is an abundant chemical fuel, its low volumetric energy density has prompted a search for catalysts able to transform methane into more useful chemicals. This search has often been aided through the use of transition state (TS) scaling relationships, which estimate methane activation TS energies as a linear function of a more easily calculated descriptor, such as final state energy, thus avoiding tedious TS energy calculations. It has been shown that methane can be activated via a radical or surface-stabilized pathway, both of which possess a unique TS scaling relationship. Herein, we present a simple model to aid in the prediction of methane activation barriers on heterogeneous catalysts. Analogous to the universal radical TS scaling relationship introduced in a previous publication, we show that a universal TS scaling relationship that transcends catalysts classes also seems to exist for surface-stabilized methane activation if the relevant final state energy is used. We demonstrate that this scaling relationship holds for several reducible and irreducible oxides, promoted metals, and sulfides. By combining the universal scaling relationships for both radical and surface-stabilized methane activation pathways, we show that catalyst reactivity must be considered in addition to catalyst geometry to obtain an accurate estimation for the TS energy. Here, this model can yield fast and accurate predictions of methane activation barriers on a wide range of catalysts, thus accelerating the discovery of more active catalysts for methane conversion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Performance of Bolted Closure Joint Elastomers Under Cask Aging Conditions

The bolted closure joint of a bare spent fuel cask is susceptible to age-related degradation and potential loss of confinement function under long-term storage conditions. Elastomeric seals, a component of the joint typically used to facilitate leak testing of the primary seal that includes the metallic seal and bolting, is susceptible to degradation over time by several mechanisms, principally via thermo-oxidation, stress-relaxation, and radiolytic degradation under time and temperature condition. Irradiation and thermal exposure testing and evaluation of an ethylene-propylene diene monomer (EPDM) elastomeric seal material similar to that used in the CASTOR® V/21 cask for a matrix of temperature and radiation exposure conditions relevant to the cask extended storage conditions, and development of semiempirical predictive models for loss of sealing force is in progress. A special insert was developed to allow Compressive Stress Relaxation (CSR) measurements before and after the irradiation and/or thermal exposure without unloading the elastomer. A condition of the loss of sealing force for the onset of leakage was suggested. The experimentation and modeling being performed could enable acquisition of extensive coupled aging data as well as an estimation of the timeframe when loss of sealing function under aging (temperature/radiation) conditions may occur.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

A Probabilistic Data-Driven Method for Photovoltaic Pseudo-Measurement Generation in Distribution Systems

This paper presents a probabilistic data-driven method to model the uncertainty of distributed photovoltaic (PV) generation to enhance the observability of distribution grids with a high penetration of renewable resources. The proposed method employs a kernel density estimation (KDE) technique to develop the conditional probability density function (PDF) of PV outputs given the time of the day, using the measurement data from observed PV systems. Then time series of output power of unmeasured PV generators, located at the same grid, are generated according to the corresponding PDF and used as pseudo-measurements in distribution system state estimation (DSSE). The proposed model has been tested and verified using real PV data and utility networks.

Yuan, Yuxuan↗
DOE OSTI2024

Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset

Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for >700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R 2 (0.12–0.49, 0.15–0.42, and 0.25–0.56) and increased NRMSE (3.58–18.24%, 6.27–11.55%, and 7.0–33.12%) compared with nonspatial random cross-validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Probabilistic Photometric Redshifts in the Era of Petascale Astronomy

With the growth of large photometric surveys, accurately estimating photometric redshifts, preferably as a probability density function (PDF), and fully understanding the implicit systematic uncertainties in this process has become increasingly important. These surveys are expected to obtain images of billions of distinct galaxies. As a result, storing and analyzing all of these photometric redshift PDFs will be non-trivial, and this challenge becomes even more severe if a survey plans to compute and store multiple different PDFs. In this thesis, we have developed an end-to-end framework that will compute accurate and robust photometric redshift PDFs for massive data sets by using two new, state-of-the-art machine learning techniques that are based on a random forest and a random atlas, respectively. By using data from several photometric surveys, we demonstrate the applicability of these new techniques, and we demonstrate that our new approach is among the best techniques currently available. We also show how different techniques can be combined by using novel Bayesian techniques to improve the photometric redshift precision to unprecedented levels while also presenting new approaches to better identify outliers. In addition, our framework provides supplementary information regarding the data being analyzed, including unbiased estimates of the accuracy of the technique without resorting to a validation data set, identification of poor photometric redshift areas within the parameter space occupied by the spectroscopic training data, and a quantification of the relative importance of the variables used during the estimation process. Furthermore, we present a new approach to represent and store photometric redshift PDFs by using a sparse representation with outstanding compression and reconstruction capabilities. We also demonstrate how this framework can also be directly incorporated into cosmological analyses. The new techniques presented in this thesis are crucial to enable the development of precision cosmology in the era of petascale astronomical surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Investigations on the electronic properties and effect of chitosan capping on the structural and optical properties of zinc aluminate quantum dots

Quantum confined uncapped and chitosan capped nanoparticles of ZnAl 2 O 4 synthesized by microwave-assisted sol–gel method were investigated by structural analysis and optical techniques. X-ray diffraction studies confirmed the formation of cubic spinels with crystallite size 4.5 nm and 3.4 nm, respectively for uncapped and chitosan capped ZnAl 2 O 4 nanoparticles. Chitosan capping produced a blue shift in bandgap energy (3.84 to 3.94 eV) agreeing with size effects according to the Brus equation. A blue shift in emission peak and enhancement in photoluminescence intensity was also observed upon chitosan capping. The electronic band structure and the density of states of the bulk spinel were also calculated using density functional theory. The effective masses of electrons and holes estimated based on the band structure were used to extract the excitonic Bohr radius.

36 MATERIALS SCIENCE↗