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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Safe Reinforcement Learning-Based Transient Stability Control for Islanded Microgrids With Topology Reconfiguration

This paper proposes a safe reinforcement learning (RL)-based transient stability emergency control (TSEC) method for islanded microgrids. RL requires extensive interaction with the environment to learn control strategies, hence, a data-driven approach is used as a substitute for time-consuming time-domain simulation calculations. Deep sigma point processes (DSPP), which is a Gaussian process model, is utilized to predict the normal distribution of transient stability of microgrids and to construct a transient stability chance constraint. Reward-constrained policy optimization (RCPO) can simultaneously achieve objective prediction, policy learning, and constraint cost coefficient update across multiple timescales. RCPO interacts with the DSPP-based microgrid environment through a multi-process parallel manner, greatly increasing the training speed. Case studies on a real islanded microgrid demonstrate that the proposed method can efficiently and quickly obtain the optimal emergency control strategy while adhering to all hard constraints.

14 SOLAR ENERGY↗
DOE OSTI2022

Watershed zonation through hillslope clustering for tractably quantifying above- and below-ground watershed heterogeneity and functions

Abstract. In this study, we develop a watershed zonation approach for characterizing watershed organization and functions in a tractable manner by integrating multiple spatial data layers. We hypothesize that (1) a hillslope is an appropriate unit for capturing the watershed-scale heterogeneity of key bedrock-through-canopy properties and for quantifying the co-variability of these properties representing coupled ecohydrological and biogeochemical interactions, (2) remote sensing data layers and clustering methods can be used to identify watershed hillslope zones having the unique distributions of these properties relative to neighboring parcels, and (3) property suites associated with the identified zones can be used to understand zone-based functions, such as response to early snowmelt or drought and solute exports to the river. We demonstrate this concept using unsupervised clustering methods that synthesize airborne remote sensing data (lidar, hyperspectral, and electromagnetic surveys) along with satellite and streamflow data collected in the East River Watershed, Crested Butte, Colorado, USA. Results show that (1) we can define the scale of hillslopes at which the hillslope-averaged metrics can capture the majority of the overall variability in key properties (such as elevation, net potential annual radiation, and peak snow-water equivalent – SWE), (2) elevation and aspect are independent controls on plant and snow signatures, (3) near-surface bedrock electrical resistivity (top 20 m) and geological structures are significantly correlated with surface topography and plan species distribution, and (4) K-means, hierarchical clustering, and Gaussian mixture clustering methods generate similar zonation patterns across the watershed. Using independently collected data, we show that the identified zones provide information about zone-based watershed functions, including foresummer drought sensitivity and river nitrogen exports. The approach is expected to be applicable to other sites and generally useful for guiding the selection of hillslope-experiment locations and informing model parameterization.

58 GEOSCIENCES↗
DOE OSTI2025

The Langdon effect in laser plasmas: Absorption and conduction

A plasma heated by inverse bremsstrahlung absorption of laser light develops a non-Maxwellian electron distribution function, called the Langdon effect [A. B. Langdon, Phys. Rev. Lett. 44, 575 (1980)]. These non-Maxwellian distributions are sufficiently long-lived to impact the absorption processes itself as well as the transport of heat by electrons. The theory of the Langdon effect in a homogeneous plasma is reviewed to clarify some aspects of Langdon's derivation as well as to confirm that the widely used super-Gaussian approximation works fairly well to describe the shape of the distribution function and reduction of the absorption rate. The Langdon effect on thermal conduction in an inhomogeneous plasma is developed by considering perturbations in a homogeneous absorbing plasma, which develops a heat flux due to both temperature and density gradients. A practical theory of the heat flux is developed by fitting the results of Vlasov–Fokker–Planck simulations, which avoids several approximations that compromised the usefulness of past theoretical predictions, most critically, the effect of electron–electron collisions on the fluxes. The present fits parameterize the coefficients of the temperature gradient (thermal conductivity) and the density gradient for a plasma of any ionization state and for any laser intensity where the theory of the Langdon effect remains locally valid. It is expected that this generalized theory of heat flow in an absorbing plasma will improve the predictive capability of radiation-hydrodynamics simulations of laser-produced plasmas, especially those formed in inertial confinement fusion experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Disaggregating Customer-Level Behind-the-Meter PV Generation Using Smart Meter Data and Solar Exemplars

Customer-level rooftop photovoltaic (PV) has been widely integrated into distribution systems. In most cases, PVs are installed behind-the-meter (BTM), and only the net demand is recorded. Therefore, the native demand and PV generation are unknown to utilities. Separating native demand and solar generation from net demand is critical for improving grid-edge observability. In this paper, a novel approach is proposed for disaggregating customer-level BTM PV generation using low-resolution but widely available hourly smart meter data. The proposed approach exploits the strong correlation between monthly nocturnal and diurnal native demands and the high similarity among PV generation profiles. First, a joint probability density function (PDF) of monthly nocturnal and diurnal native demands is constructed for customers without PVs, using Gaussian mixture modeling (GMM). Deviation from the constructed PDF is utilized to probabilistically assess the monthly solar generation of customers with PVs. Then, to identify hourly BTM solar generation for these customers, their estimated monthly solar generation is decomposed into an hourly timescale; to do this, we have proposed a maximum likelihood estimation (MLE)-based technique that utilizes hourly typical solar exemplars. Leveraging the strong monthly native demand correlation and high PV generation similarity enhances our approach's robustness against the volatility of customers’ hourly load and enables highly-accurate disaggregation. Furthermore, the proposed approach has been verified using real native demand and PV generation data.

14 SOLAR ENERGY↗
DOE OSTI2024

An Entropy-Based Test and Development Framework for Uncertainty Modeling in Level-Set Visualizations

We present a simple comparative framework for testing and developing uncertainty modeling in uncertain marching cubes implementations. The selection of a model to represent the probability distribution of uncertain values directly influences the memory use, run time, and accuracy of an uncertainty visualization algorithm. We use an entropy calculation directly on ensemble data to establish an expected result and then compare the entropy from various probability models, including uniform, Gaussian, histogram, and quantile models. Our results verify that models matching the distribution of the ensemble indeed match the entropy. We further show that fewer bins in nonparametric histogram models are more effective whereas large numbers of bins in quantile models approach data accuracy.

Sisneros, Robert↗
DOE OSTI2021

Data-Driven Global Sensitivity Analysis of Three-Phase Distribution System with PVs

Global sensitivity analysis (GSA) of distribution system with respect to stochastic PV variations plays an important role in designing optimal voltage control schemes. This paper proposes a Kriging, i.e., Gaussian process modeling enabled data-driven GSA method. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol index can be conveniently calculated to assess the global sensitivity of voltage to various power injection variations. Comparison results with other model-based GSA methods on the IEEE 37-bus feeder, such as the polynomial chaos expansion and the Monte Carlo approaches demonstrate that the proposed method can achieve accurate GSA outcomes while maintaining high computational efficiency.

distribution system analysis↗
DOE OSTI2022

Quantifying radial migration in the Milky Way: inefficient over short time-scales but essential to the very outer disc beyond ∼15 kpc

ABSTRACT Stellar radial migration plays an important role in reshaping a galaxy’s structure and the radial distribution of stellar population properties. In this work, we revisit reported observational evidence for radial migration and quantify its strength using the age–[Fe/H] distribution of stars across the Milky Way with APOGEE data. We find a broken age–[Fe/H] relation in the Galactic disc at r > 6 kpc, with a more pronounced break at larger radii. To quantify the strength of radial migration, we assume stars born at each radius have a unique age and metallicity, and then decompose the metallicity distribution function (MDF) of mono-age young populations into different Gaussian components that originated from various birth radii at rbirth < 13 kpc. We find that, at ages of 2 and 3 Gyr, roughly half the stars were formed within 1 kpc of their present radius, and very few stars (<5 per cent) were formed more than 4 kpc away from their present radius. These results suggest limited short-distance radial migration and inefficient long-distance migration in the Milky Way during the last 3 Gyr. In the very outer disc beyond 15 kpc, the observed age–[Fe/H] distribution is consistent with the prediction of pure radial migration from smaller radii, suggesting a migration origin of the very outer disc. We also estimate intrinsic metallicity gradients at ages of 2 and 3 Gyr of −0.061 and −0.063 dex kpc−1, respectively.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Heavy-ion fusion cross section formula and barrier height distribution

Methods for obtaining fusion cross section formulas are discussed, especially for those that take on an empirical form. A new expression starting with σ( E) = $\frac{1}{E}$ $\int ^{E}_{E_0}$ $(\int^{E'}_{E_0} B (E^\shortparallel) dE^\shortparallel)dE'$ has been explored. Here, B(E) is a reasonable, assumed function of the barrier height distribution, $\frac{d^2 (\sigma E)}{dE^2}$. Further, the resulting analytic cross section formula reproduces very well the excitation functions for many light and heavy fusion systems across wide energy ranges, when B(E) is assumed to be a multi-Gaussian function. This study offers an improved determination of the fusion barrier height distribution over other numerical techniques.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

A Data-Driven Global Sensitivity Analysis Framework for Three-Phase Distribution System with PVs

Global sensitivity analysis (GSA) of distribution systems with respect to stochastic PV and load variations plays an important role in designing optimal voltage control schemes. This paper proposes a data-driven framework for GSA of distribution systems. In particular, two representative surrogate modeling-based approaches are developed, including the traditional Gaussian process-based and the analysis of variance (ANOVA) kernel ones. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol indices can be conveniently calculated through either the sampling-based method or the analytical method to assess the global sensitivity of voltage to variations of PV and load power injections. The sampling-based method approximates the Sobol indices using Monte Carlo simulations while the analytical method calculates them by resorting to the ANOVA expansion framework. Comparison results with other model-based GSA methods on the unbalanced three-phase IEEE 37-bus and 123-bus distribution systems show that the proposed framework can achieve much higher computational efficiency with negligible loss of accuracy. The results on a real 240-node distribution system using actual smart meter data further validate the feasibility and scalability of the proposed framework.

14 SOLAR ENERGY↗
DOE OSTI2020

A discrete integral transform for rapid spectral synthesis

Accurate synthetic spectra that rely on large Line-By-Line (LBL)-databases are used in a wide range of applications such as high temperature combustion, atmospheric re-entry, planetary surveillance and laboratory plasmas. Conventionally synthetic spectra are calculated by computing a lineshape for every spectral line in the database and adding those together, which may take multiple hours for large databases. In this paper we propose a new approach for spectral synthesis based on an integral transform: the synthetic spectrum is calculated as the integral over the product of a Voigt profile and a newly proposed three-dimensional “lineshape distribution function”, which is a function of spectral position and Gaussian- & Lorentzian width coordinates. A fast discrete version of this transform based on the Fast Fourier Transform (FFT) is proposed, which improves performance compared to the conventional approach by several orders of magnitude while maintaining accuracy. Strategies that minimize the discretization error are discussed. A Python implementation of the method is compared against state-of-the-art spectral code RADIS, and is since adopted as RADIS's default synthesis method. The synthesis of a benchmark CO2 spectrum consisting of 1.8 M spectral lines and 200k spectral points took only 3.1 s using the proposed method (1011 lines × spectral points/s), a factor ~300 improvement over the state-of-the-art, with the relative improvement generally increasing for higher number of lines and/or number of spectral points. Finally, an experimental GPU-implementation of the method was also benchmarked, which demonstrated another 2~3 orders performance increase, achieving up to 5 ∙ 10 14 lines × spectral points/s.

42 ENGINEERING↗
DOE OSTI2022

Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning

Scalable electronic predictions are critical for soft materials design. Recently, the Electronic Coarse-Graining (ECG) method was introduced to renormalize all-atom quantum chemical (QC) predictions to coarse-grained (CG) resolutions using deep neural networks (DNNs). While DNNs can learn complex representations that prove challenging for kernel-based methods, they are susceptible to overfitting and the overconfidence of uncertainty estimations. Here, we develop ECG within a GPU-accelerated Deep Kernel Learning (DKL) framework to enable CG QC predictions using range-separated hybrid density functional theory (DFT), obtaining a 107 speedup relative to naive all-atom QC. By treating the predicted electronic properties as random Gaussian Processes, DKL incorporates CG mapping degeneracy by learning the distribution of electronic energies as a function of CG configuration. DKL-ECG accurately reproduces molecular orbital energies from range-separated DFT while facilitating efficient training via active learning using the uncertainties provided by DKL. Further, we show that while active learning algorithms enable efficient sampling of a more diverse configurational space relative to random sampling, all explored query methods exhibit comparable performance for the examined system. We attribute this result to the significant overlap of the feature space and output property distributions across multiple temperatures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Nonlinear models for coupling the effects of stimulated Raman scattering to inertial confinement fusion codes

Laser plasma instabilities (LPI) reduce driver-target coupling, alter implosion symmetry, and therefore can fundamentally limit fusion performance in inertial confinement fusion (ICF). Developing a predictive modeling capability for LPI effects can critically advance the success of the field. We perform vector particle-in-cell simulations of multi-speckled laser beams undergoing stimulated Raman scattering (SRS) at various densities and intensities relevant to mainly indirectly driven and a subset of parameter space for directly driven ICF systems, focusing on the regimes with intensities above the onset of electron trapping. Based on the wavenumber of the SRS daughter electron plasma wave, we identify several regions with underpinning SRS saturation physics: the electron-trapping dominated region with intermediate kλD values, the strong Landau damping region at larger kλD values, and the region where the Langmuir decay instability arises at lower kλD values. We develop a nonlinear SRS reflectivity model that features the base trapping-dominated scaling of (kλD)−4 and its modifications. Electron trapping deforms the initialized electron distribution functions, and we have developed a new δf-Gaussian-mixture algorithm for an accurate characterization of the trapped hot electron population. With this SRS hot electron description, we construct a nonlinear energy deposition model and a hot electron source model—based on a modified Manley–Rowe relation—suitable for including SRS effects as a sub-grid module in a high-fidelity ICF design code.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Spot size measurement of a deuterium–tritium dense plasma focus using neutron radiography

Neutron radiography is a technique uniquely suited to applications in nuclear diagnostics, non-destructive testing, and subcritical experiments. The spatial resolution of neutron radiographs is degraded by optical blur in the imaging system and the neutron source size, where the ideal source is point-like to optimize the point-spread function. A potential neutron source for radiography is the dense plasma focus (DPF), a coaxial Z-pinch that produces thermonuclear and beam-target neutrons. To assess if the source size is suitable for radiography, a neutron imaging system was used to measure the source size of the 4 MA Sodium DPF at the Nevada National Security Site operating with deuterium–tritium gas-fill. The source size was measured using the edge-spread function of tungsten objects, each having a rolled (convex) edge. The spot size was found to be 7–12 mm full-width at half-max (FWHM) assuming a Gaussian source, though comparison is presented for Lorentzian and Bennett distributions. The average FWHM was found to be 8.6 ± 1.2 mm vertically and 10.8 ± 1.2 mm horizontally with respect to the image plane, averaging over varied edges and alignments. The results were sensitive to source alignment and edge metrology, which introduced notable uncertainties. These results are consistent with separate experimental measurements as well as magnetohydrodynamics simulations of this DPF, which suggest that neutron production can originate from pinches ∼5–7 mm off-axis. These results suggest that the DPF should be used for radiography at low magnification (M < 1) where spot size does not dominate spatial blur.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

QSO photometric redshifts from SDSS, WISE, and GALEX colours

ABSTRACT Machine learning techniques, specifically the k-nearest neighbour algorithm applied to optical band colours, have had some success in predicting photometric redshifts of quasi-stellar objects (QSOs): Although the mean of differences between the spectroscopic and photometric redshifts, Δ$z$, is close to zero, the distribution of these differences remains wide and distinctly non-Gaussian. As per our previous empirical estimate of photometric redshifts, we find that the predictions can be significantly improved by adding colours from other wavebands, namely the near-infrared and ultraviolet. Self-testing this, by using half of the 33 643 strong QSO sample to train the algorithm, results in a significantly narrower spread in Δ$z$ for the remaining half of the sample. Using the whole QSO sample to train the algorithm, the same set of magnitudes return a similar spread in Δ$z$ for a sample of radio sources (quasars). Although the matching coincidence is relatively low (739 of the 3663 sources having photometry in the relevant bands), this is still significantly larger than from the empirical method (2 per cent) and thus may provide a method with which to obtain redshifts for the vast number of continuum radio sources expected to be detected with the next generation of large radio telescopes.

Curran, S. J.↗
DOE OSTI2022

Fiber Uncertainty Visualization for Bivariate Data With Parametric and Nonparametric Noise Models

Visualization and analysis of multivariate data and their uncertainty are top research challenges in data visualization. Constructing fiber surfaces is a popular technique for multivariate data visualization that generalizes the idea of level-set visualization for univariate data to multivariate data. Here, in this paper, we present a statistical framework to quantify positional probabilities of fibers extracted from uncertain bivariate fields. Specifically, we extend the state-of-the-art Gaussian models of uncertainty for bivariate data to other parametric distributions (e.g., uniform and Epanechnikov) and more general nonparametric probability distributions (e.g., histograms and kernel density estimation) and derive corresponding spatial probabilities of fibers. In our proposed framework, we leverage Green's theorem for closed-form computation of fiber probabilities when bivariate data are assumed to have independent parametric and nonparametric noise. Additionally, we present a nonparametric approach combined with numerical integration to study the positional probability of fibers when bivariate data are assumed to have correlated noise. For uncertainty analysis, we visualize the derived probability volumes for fibers via volume rendering and extracting level sets based on probability thresholds. We present the utility of our proposed techniques via experiments on synthetic and simulation datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Measures of illuminant-induced metameric mismatch: theory, comparative analysis, and implications for application

Illuminant-induced metameric mismatch is an important consideration in the specification of light sources for some architectural environments, yet there is currently no standardized performance measure. The goal of this work was to evaluate two recent research proposals: the metameric uncertainty index ( R t ) and the metamer mismatching color rendering index (MMCRI). To compare the relative performance of these two measures, 100,000 spectral power distributions were generated with 3, 4, 5, 6, and 7 Gaussian spectral components and spectral widths varying from 1 nm (monochromatic) to 100 nm. Both measures generally agree with the theory that broadband radiation should cause less metameric mismatch than narrowband radiation. The two measures have relatively better agreement for broadband SPDs and relatively worse agreement for narrower spectra. Despite some similarities, non-parametric statistical tests suggest that R t and MMCRI are significantly different quantifications of illuminant-induced metameric mismatch ( p < 0.0001 for all comparisons). Characteristics of the MMCRI computation that are potentially problematic for applied lighting were observed.

42 ENGINEERING↗
DOE OSTI2020

We Know Where We Don't Know: 3D Bayesian CNNs for Credible Geometric Uncertainty

Deep learning has been successfully applied to the segmentation of 3D Computed Tomography (CT) scans. Establishing the credibility of these segmentations requires uncertainty quantification (UQ) to identify untrustworthy predictions. Recent UQ architectures include Monte Carlo dropout networks (MCDNs), which approximate deep Gaussian processes, and Bayesian neural networks (BNNs), which learn the distribution of the weight space. BNNs are advantageous over MCDNs for UQ but are thought to be computationally infeasible in high dimension, and neither architecture has produced interpretable geometric uncertainty maps. We propose a novel 3D Bayesian convolutional neural network (BCNN), the first deep learning method which generates statistically credible geometric uncertainty maps and scales for application to 3D data. We present experimental results on CT scans of graphite electrodes and laser-welded metals and show that our BCNN outperforms an MCDN in recent uncertainty metrics. The geometric uncertainty maps generated by our BCNN capture distributions of sigmoid values that are interpretable as confidence intervals, critical for applications that rely on deep learning for high-consequence decisions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A New Probability Table Methodology with Covariances on Probability Tables

Through this work, we test a new methodology of evaluating probability tables informed by a combination of physics models and experimental data. This methodology uses generalized least squares to produce a joint mean and covariance for average cross sections and probability tables. This methodology has been tested and validated using total Monte Carlo. Attempts were made to parameterize the probability table using a formulation provided in. Although the normal inverse Gaussian fit was successful for the total cross section probability distribution, the rational-function fit to the capture, inelastic, and fission cross sections was unsuccessful due to time constraints.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗