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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Methods for determining optical power, for power-normalizing laser measurements, and for stabilizing power of lasers via compliance voltage sensing

A method is disclosed for power normalization of spectroscopic signatures obtained from laser based chemical sensors that employs the compliance voltage across a quantum cascade laser device within an external cavity laser. The method obviates the need for a dedicated optical detector used specifically for power normalization purposes. A method is also disclosed that employs the compliance voltage developed across the laser device within an external cavity semiconductor laser to power-stabilize the laser mode of the semiconductor laser by adjusting drive current to the laser such that the output optical power from the external cavity semiconductor laser remains constant.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

ANOMALOUS SKIN EFFECT STUDY OF NORMAL CONDUCTING FILM

For the radiofrequency (RF) applications of normal conducting film with large mean free path at high frequency and low temperature, the anomalous skin effect differs considerably from the normal skin effect with field decaying exponentially in the film. Starting from the relationship be-tween the current and the electric field (E field) in the film, the amplitude of E field along the film depth is calculated and is found to be non-monotonic. The surface impedance is found to have a minimum value at certain film thickness.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series

Anomaly detection is a widely studied task for a broad variety of data types; among them, multiple time series appear frequently in applications, including for example, power grids and traffic networks. Detecting anomalies for multiple time series, however, is a challenging subject, owing to the intricate interdependencies among the constituent series. We hypothesize that anomalies occur in low density regions of a distribution and explore the use of normalizing flows for unsupervised anomaly detection, because of their superior quality in density estimation. Moreover, we propose a novel flow model by imposing a Bayesian network among constituent series. A Bayesian network is a directed acyclic graph (DAG) that models causal relationships; it factorizes the joint probability of the series into the product of easy-to-evaluate conditional probabilities. We call such a graph-augmented normalizing flow approach GANF and propose joint estimation of the DAG with flow parameters. We conduct extensive experiments on real-world datasets and demonstrate the effectiveness of GANF for density estimation, anomaly detection, and identification of time series distribution drift.

Dai, Enyan↗
DOE OSTI2026

Wasserstein Normalized Autoencoder for Anomaly Detection in ProtoDUNE Vertical-Drift Detector

ProtoDUNE Vertical Drift needs a selective triggering algorithm. The detector sits on Earth's surface, so cosmic activity dominates its data. Our goal in this paper is to trigger on neutrino events more robustly than the current deployed Analog-to-Digital Converter Simple Window (ADCSW) model and, eventually, search for signals of Beyond Standard Model (BSM) physics at DUNE as our ultimate North Star objective. As a step towards this goal, we evaluate a Wasserstein Normalized Autoencoder (WNAE) on simulated collection-plane only windows of shape $1\times10\times10$ where Neutrinos act as our BSM-proxy and Cosmic-ray Muons serve as our learned background. The network parameters are fitted using only cosmic-ray muon events as background in order to maintain an unsupervised pipeline. Training uses finite-step Langevin $x^-$ samples, positive-sample reconstruction energy, and an empirical sliced $2$-Wasserstein objective to learn a normalized Boltzmann energy model. We then calibrate on a nominal $5\,\mathrm{Hz}$ operating threshold calculated from cosmic validation data. Both WNAE and ADCSW accept 311 of 194,083 held-out cosmic background events at this $5\,\mathrm{Hz}$ threshold. We found that WNAE accepts 9,677 of 34,634 neutrino-proxy events $(27.9\pm0.24)\%$, compared with 10,076 $(29.1\pm0.24)\%$ for ADCSW, an observed WNAE-minus-ADCSW difference of $-1.15\%$. At another nominal $2\,\mathrm{Hz}$ target threshold, the corresponding efficiencies are $(20.5\pm0.22)\%$ and $(22.6\pm0.22)\%$, respectively. Of the WNAE-selected neutrino proxies at $5\,\mathrm{Hz}$, $(20.8\pm0.4)\%$ of the classified neutrino-proxy events are unique to WNAE, where the uncertainty is an absolute binomial standard error of $0.4\%$.

Zheng, Jake [Chicago U.] (ORCID:0009000218901379)↗
DOE OSTI2025

Normalized Solar-Induced Fluorescence Responds Earlier Than Vegetation Indices to the 2019 North China Plain Drought

Recently, solar-induced chlorophyll fluorescence (SIF) from satellites has shown potential for evaluating vegetation status and stress responses. Fluorescence quantum yield ($Φ_F$) is essentially linked to vegetation stress. However, the complex physiological and structural responses of SIF and $Φ_F$ to drought need further study. This study normalized SIF as SIFn to account for angular variations and fluctuations in photosynthetically active radiation (PAR), aiming for more accurate drought monitoring. SIFn anomalies were compared to historical baselines (2019–2021 averages) of vegetation indices (VIs), raw SIF, and $Φ_F$ during a 2019 drought in the North China Plain (NCP). Here, the results show SIFn provides an effective method for drought monitoring, showing the earliest decline compared to raw SIF, VIs, and $Φ_F$. In the first two weeks of drought, SIFn decreased by 8.2%, 7.0%, 12.5%, and 8.2% across the four NCP subdivisions. SIFn outperformed other indicators, proving sensitive to early drought detection. SIFn was also examined for tracking drought alleviation by rainfall. The uncertainty under different viewing geometries was quantified. SIFn anomalies showed a strong correlation with rainfall anomalies (R: 0.45 ~ 0.52) and meteorological factors like PAR (R: 0.80 ~ 0.84) and relative humidity (R:0.52 ~ 0.54). The correlation of near-infrared reflectance (NIRv) and $Φ_F$ anomalies with SIF was weak during drought onset (R: 0.16 ~ 0.32) but strong at the end (R: 0.83 ~ 0.87). These suggest both canopy structure (mainly characterized by NIRv) and vegetation chlorophyll ($Φ_F$) are impacted by drought and influence SIF at different stages.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Chapter 1: Direct Normal Radiation

This chapter addresses the quantitative and qualitative aspects of the solar resource, the direct solar radiation. It discusses the total or integrated broadband direct beam extraterrestrial radiation (ETR). This total integrated irradiance is comprised of photons of electromagnetic radiation. The chapter also discusses the impact of the atmosphere and its effect upon the direct normal irradiance (DNI) beam radiation. The gases and particulates present in the atmosphere traversed by the direct beam reflect, absorb, and scatter differing spectral regions and proportions of the direct beam, and act as a variable filter. Knowledge of the available broadband DNI beam radiation resource data is essential in designing a concentrating photovoltaic (CPV) system. Spectral variations in the DNI beam radiation affect the performance of a CPV system depending on the solar cell technology used. The chapter describes propagation and scattering processes of circumsolar radiation (CSR), which includes the Mie scattering from large particles.

14 SOLAR ENERGY↗
DOE OSTI2022

A Non-perturbative Approach to Computing Seismic Normal Modes in Rotating Planets

In this work, a continuous Galerkin method based approach is presented to compute the seismic normal modes of rotating planets. Special care is taken to separate out the essential spectrum in the presence of a fluid outer core using a polynomial filtering eigensolver. The relevant elastic-gravitational system of equations, including the Coriolis force, is subjected to a mixed finite-element method, while self-gravitation is accounted for with the fast multipole method. Our discretization utilizes fully unstructured tetrahedral meshes for both solid and fluid regions. The relevant eigenvalue problem is solved by a combination of several highly parallel and computationally efficient methods. We validate our three-dimensional results in the non-rotating case using analytical results for constant elastic balls, as well as numerical results for an isotropic Earth model from standard “radial” algorithms. We also validate the computations in the rotating case, but only in the slowly-rotating regime where perturbation theory applies, because no other independent algorithms are available in the general case. The algorithm and code are used to compute the point spectra of eigenfrequencies in several Earth and Mars models studying the effects of heterogeneity on a large range of scales.

58 GEOSCIENCES↗
DOE OSTI2024

Improved multifidelity Monte Carlo estimators based on normalizing flows and dimensionality reduction techniques

Here, we study the problem of multifidelity uncertainty propagation for computationally expensive models. In particular, we consider the general setting where the high-fidelity and low-fidelity models have a dissimilar parameterization both in terms of number of random inputs and their probability distributions, which can be either known in closed form or provided through samples. We derive novel multifidelity Monte Carlo estimators which rely on a shared subspace between the high-fidelity and low-fidelity models where the parameters follow the same probability distribution, i.e., a standard Gaussian. We build the shared space employing normalizing flows to map different probability distributions into a common one, together with linear and nonlinear dimensionality reduction techniques, active subspaces and autoencoders, respectively, which capture the subspaces where the models vary the most. We then compose the existing low-fidelity model with these transformations and construct modified models with an increased correlation with the high-fidelity model, which therefore yield multifidelity estimators with reduced variance. A series of numerical experiments illustrate the properties and advantages of our approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach

The escalating energy demand and the adverse environmental impacts of fossil-fuel use necessitate a shift towards cleaner and renewable alternatives. Concentrated Solar Power (CSP) technology emerges as a promising solution, offering a carbon-free alternative for power generation. The efficiency and profitability of CSP depend on the Direct Normal Irradiance (DNI) component of solar radiation; hence, accurate DNI forecasting can help optimize CSP plants’ operations and performance. The unpredictable nature of weather phenomena, particularly cloud cover, introduces uncertainty into DNI projections. Existing DNI forecasting models use meteorological factors, which are both challenging to estimate numerically over short prediction windows and expensive to model through data at a sufficiently high spatial and temporal resolution. This research addresses the challenge by presenting a novel approach that formulates DNI prediction as a multi-class classification problem, departing from conventional regression-based methods. The primary objective of this classification framework is to identify optimal periods aligning with specific operational thresholds for CSP plants, contributing to enhanced dispatch optimization strategies. We model the DNI classification problem using four advanced deep neural networks – rectified linear unit (ReLU) networks, 1D residual networks (ResNets), bidirectional long short-term memory (BiLSTM) networks, and transformers – achieving accuracies up to 93.5% without requiring meteorological parameters.

14 SOLAR ENERGY↗
DOE OSTI2023

Uniform-in-phase-space data selection with iterative normalizing flows

Improvements in computational and experimental capabilities are rapidly increasing the amount of scientific data that are routinely generated. In applications that are constrained by memory and computational intensity, excessively large datasets may hinder scientific discovery, making data reduction a critical component of data-driven methods. Datasets are growing in two directions: the number of data points and their dimensionality. Whereas dimension reduction typically aims at describing each data sample on lower-dimensional space, the focus here is on reducing the number of data points. A strategy is proposed to select data points such that they uniformly span the phase-space of the data. The algorithm proposed relies on estimating the probability map of the data and using it to construct an acceptance probability. An iterative method is used to accurately estimate the probability of the rare data points when only a small subset of the dataset is used to construct the probability map. Instead of binning the phase-space to estimate the probability map, its functional form is approximated with a normalizing flow. Therefore, the method naturally extends to high-dimensional datasets. The proposed framework is demonstrated as a viable pathway to enable data-efficient machine learning when abundant data are available.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Prediction of the Wave Normal Angle of Proton‐Band EMIC Waves Near Geosynchronous Orbit

We investigate how the wave normal angle (WNA) and polarization of proton-band electromagnetic ion cyclotron (EMIC) waves change as they travel from their source to Earth. This paper marks a significant milestone as the first full-wave simulation of proton-band EMIC waves reflecting from the ionosphere. Our findings show that the WNA can change rapidly during propagation, primarily due to plasma inhomogeneities, such as variations in the Alfvén speed. The wave polarization is strongly related to the WNA, consistent with theory. Newly generated EMIC waves near the equator propagate with a WNA of 0° , then the WNA gradually shifts to 90° as they move toward Earth. In contrast, reflecting waves having 90° of WNA at Earth maintain a relatively larger WNA even near the magnetic equator. As a result, only the newly generated waves close to the source, where the magnetic latitude is less than approximately 20° , show left-handed polarization, while linear polarization remains dominant throughout the rest of the propagation.

EMIC wave↗
DOE OSTI2013

Instantaneous normal mode analysis for intermolecular and intramolecular vibrations of water from atomic point of view

By exploiting the instantaneous normal mode (INM) analysis for models of flexible molecules, we investigate intermolecular and intramolecular vibrations of water from the atomic point of view. With two flexible SPC/E models, our investigations include three aspects about their INM spectra, which are separated into the unstable, intermolecular, bending, and stretching bands. First, the O- and H-atom contributions in the four INM bands are calculated and their stable INM spectra are compared with the power spectra of the atomic velocity autocorrelation functions. The unstable and intermolecular bands of the flexible models are also compared with those of the SPC/E model of rigid molecules. Second, we formulate the inverse participation ratio (IPR) of the INMs, respectively, for the O- and H-atom and molecule. With the IPRs, the numbers of the three species participated in the INMs are estimated so that the localization characters of the INMs in each band are studied. Further, by the ratio of the IPR of the H atom to that of the O atom, we explore the number of involved OH bond per molecule participated in the INMs. Third, by classifying simulated molecules into subensembles according to the geometry of their local environments or their H-bond configurations, we examine the local-structure effects on the bending and stretching INM bands. All of our results are verified to be insensible to the definition of H-bond. Our conclusions about the intermolecular and intramolecular vibrations in water are given.

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

Normal mode analysis of macromolecular systems with the mobile block Hessian method

Until recently, normal mode analysis (NMA) was limited to small proteins, not only because the required energy minimization is a computationally exhausting task, but also because NMA requires the expensive diagonalization of a 3N{sub a}×3N{sub a} matrix with N{sub a} the number of atoms. A series of simplified models has been proposed, in particular the Rotation-Translation Blocks (RTB) method by Tama et al. for the simulation of proteins. It makes use of the concept that a peptide chain or protein can be seen as a subsequent set of rigid components, i.e. the peptide units. A peptide chain is thus divided into rigid blocks with six degrees of freedom each. Recently we developed the Mobile Block Hessian (MBH) method, which in a sense has similar features as the RTB method. The main difference is that MBH was developed to deal with partially optimized systems. The position/orientation of each block is optimized while the internal geometry is kept fixed at a plausible - but not necessarily optimized - geometry. This reduces the computational cost of the energy minimization. Applying the standard NMA on a partially optimized structure however results in spurious imaginary frequencies and unwanted coordinate dependence. The MBH avoids these unphysical effects by taking into account energy gradient corrections. Moreover the number of variables is reduced, which facilitates the diagonalization of the Hessian. In the original implementation of MBH, atoms could only be part of one rigid block. The MBH is now extended to the case where atoms can be part of two or more blocks. Two basic linkages can be realized: (1) blocks connected by one link atom, or (2) by two link atoms, where the latter is referred to as the hinge type connection. In this work we present the MBH concept and illustrate its performance with the crambin protein as an example.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Anharmonicity and necessity of phonon eigenvectors in the phonon normal mode analysis

It is well known that phonon frequencies can shift from their harmonic values when elevated to a finite temperature due to the anharmonicity of interatomic potential. Here, we show that phonon eigenvectors also have shifts, but only for compound materials in which each atom has at least two types of anharmonic interactions with other atoms. Using PbTe as the model material, we show that the shifts in some phonon modes may reach as much as 50% at 800 K. Phonon eigenvectors are used in normal mode analysis (NMA) to predict phonon relaxation times and thermal conductivity. We show, from both analytical derivations and numerical simulations, that the eigenvectors are unnecessary in frequency-domain NMA, which gives a critical revision of previous knowledge. This simplification makes the calculation in frequency-domain NMA more convenient since no separate lattice dynamics calculations are needed. On the other hand, we expect our finding of anharmonic eigenvectors may make difference in time-domain NMA and other areas, like wave-packet analysis.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Multi-scaled normal mode analysis method for dynamics simulation of protein-membrane complexes: A case study of potassium channel gating motion correlations

Membrane proteins play critically important roles in many cellular activities such as ions and small molecule transportation, signal recognition, and transduction. In order to fulfill their functions, these proteins must be placed in different membrane environments and a variety of protein-lipid interactions may affect the behavior of these proteins. One of the key effects of protein-lipid interactions is their ability to change the dynamics status of membrane proteins, thus adjusting their functions. Here, we present a multi-scaled normal mode analysis (mNMA) method to study the dynamics perturbation to the membrane proteins imposed by lipid bi-layer membrane fluctuations. In mNMA, channel proteins are simulated at all-atom level while the membrane is described with a coarse-grained model. mNMA calculations clearly show that channel gating motion can tightly couple with a variety of membrane deformations, including bending and twisting. We then examined bi-channel systems where two channels were separated with different distances. From mNMA calculations, we observed both positive and negative gating correlations between two neighboring channels, and the correlation has a maximum as the channel center-to-center distance is close to 2.5 times of their diameter. This distance is larger than recently found maximum attraction distance between two proteins embedded in membrane which is 1.5 times of the protein size, indicating that membrane fluctuation might impose collective motions among proteins within a larger area. The hybrid resolution feature in mNMA provides atomic dynamics information for key components in the system without costing much computer resource. We expect it to be a conventional simulation tool for ordinary laboratories to study the dynamics of very complicated biological assemblies. The source code is available upon request to the authors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Normalizing flows for likelihood-free inference with fusion simulations

Fluid-based scrape-off layer transport codes, such as UEDGE, are heavily utilized in tokamak analysis and design, but typically require user-specified anomalous transport coefficients to match experiments. Determining the uniqueness of these parameters and the uncertainties in them to match experiments can provide valuable insights to fusion scientists. Here, we leverage recent work in the area of likelihood-free inference (‘simulation-based inference’) to train a neural network, which enables accurate statistical inference of the anomalous transport coefficients given experimental plasma profile input. UEDGE is treated as a black-box simulator and runs multiple times with anomalous transport coefficients sampled from priors, and the neural network is trained on these simulations to emulate the posterior. The neural network is trained as a normalizing flow model for density estimation, allowing it to accurately represent complicated, high-dimensional distribution functions. With a fixed simulation budget, we compare a single-round procedure to a multi-round approach that guides the training simulations toward a specific target observation. Finally, we discuss the future possibilities for use of amortized models, which train on a wide range of simulations and enable fast statistical inference for results during experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Aspects of the normal state resistivity of cuprate superconductors

Planar normal state resistivity data taken from three families of cuprate superconductors are compared here with theoretical calculations from the recent extremely correlated Fermi liquid theory (ECFL). The two hole doped cuprate materials LSCO and BSLCO and the electron doped material LCCO have yielded rich data sets at several densities δ and temperatures T, thereby enabling a systematic comparison with theory. The recent ECFL resistivity calculations for the highly correlated t-t'-J model by us give the resistivity for a wide set of model parameters. After using X-ray diffraction and angle resolved photoemission data to fix parameters appearing in the theoretical resistivity, only one parameter, the magnitude of the hopping t, remains undetermined. For each data set, the slope of the experimental resistivity at a single temperature-density point is sufficient to determine t, and hence the resistivity on absolute scale at all remaining densities and temperatures. This procedure is demonstrated to give a fair account of the entire data.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Gap-filled water quality, Normalized Differenced Vegetation Index, total nitrogen (nitrate and ammonia) deposition, and land cover data trends for the Continental United States

The purpose of this work was to quantify trends in water quality, total nitrogen deposition, vegetation, and land-use trends for the Continental United States (CONUS). This dataset contains gap-filled daily and yearly in-stream carbon (C) and nitrogen (N) concentrations, discharge, and calculated exports from United States Geological Survey (USGS) National Water Information System (NWIS) stations across the United States of six different nitrogen parameters (Supplementary Table A1), one carbon parameter, and temperature (USGS, 2016, 2018) (https://waterdata.usgs.gov/nwis). The final water quality dataset is merged with the normalized differenced vegetation index (NDVI), total deposition (TDEP), landcover, and elevation datasets. All data are available at the CONUS scale as well as at the scale of hydrologic unit codes: (HUC) HUC8, HUC6, HUC4, and HUC2 watersheds. There are two zip files associated with this data package. I split the data into two equal sized zip files to comply with the data size limits, however the datasets are the same within each zip file. All data files can be read in directly into the R software.

54 ENVIRONMENTAL SCIENCES↗