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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Combining High-Throughput Experiments and Active Learning to Characterize Deep Eutectic Solvents

The high tunability of deep eutectic solvents (DESs) stems from the ease of changing their precursors and relative compositions. However, measuring the physicochemical properties across large composition and temperature ranges, necessary to properly design target-specific DESs, is tedious and error-prone and represents a bottleneck in the advancement and scalability of DES-based applications. As such, active learning (AL) methodologies based on Gaussian processes (GPs) were developed in this work to minimize the experimental effort necessary to characterize DESs. Owing to its importance for large-scale applications, the reduction of DES viscosity through the addition of a low-molecular-weight solvent was explored as a case study. A high-throughput experimental screening was initially performed on nine different ternary DESs. Then, GPs were successfully trained to predict DES viscosity from its composition and temperature, showcasing the ability of these stochastic, nonparametric models to accurately describe the physicochemical properties of complex mixtures. Finally, the ability of GPs to provide estimates of their own uncertainty was leveraged through an AL framework to minimize the number of data points necessary to obtain accurate viscosity modes. This led to a significant reduction in data requirements, with many systems requiring only five independent viscosity data points to be properly described.

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

Automatic parameter selection for electron ptychography via Bayesian optimization

Abstract Electron ptychography provides new opportunities to resolve atomic structures with deep sub-angstrom spatial resolution and to study electron-beam sensitive materials with high dose efficiency. In practice, obtaining accurate ptychography images requires simultaneously optimizing multiple parameters that are often selected based on trial-and-error, resulting in low-throughput experiments and preventing wider adoption. Here, we develop an automatic parameter selection framework to circumvent this problem using Bayesian optimization with Gaussian processes. With minimal prior knowledge, the workflow efficiently produces ptychographic reconstructions that are superior to those processed by experienced experts. The method also facilitates better experimental designs by exploring optimized experimental parameters from simulated data.

42 ENGINEERING↗
DOE OSTI2024

Exploration with Scalable Gaussian Process Reinforcement Learning

Exploration is a challenging problem in reinforcement learning (RL), especially in environments with sparse rewards. Quantifying and utilizing the parametric uncertainty has been shown to be paramount for successful exploration [Osband et al., 2018]. Bayesian, or approximately Bayesian, methods present a principled means of estimating the parametric uncertainty in RL problems. Gaussian processes, nonparametric Bayesian models, are often impractical due to poor scalability and computational bottlenecks. We introduce a scalable Gaussian process RL (GPRL) method which directly induces sparsity in the covariance matrix to facilitate faster computation. This is a departure from previous GPRL methods which instead rely on data reduction and subsampling. We compare various covariance-based exploration techniques (Thompson sampling, upper confidence bound, and probabilistic maximum variance) which leverage our scalable GP framework in sparse reward environments. Finally, we show favorable comparison against the bootstrapped deep Q-Network.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Strong localization induced anomalous temperature dependence exciton emission above 300 K from SnO{sub 2} quantum dots

SnO{sub 2} quantum dots (QDs) are potential materials for deep ultraviolet (DUV) light emitting devices. In this study, we report the temperature and excitation power-dependent exciton luminescence from SnO{sub 2} QDs. The exciton emission exhibits anomalous blue shift, accompanied with band width reduction with increasing temperature and excitation power above 300 K. The anomalous temperature dependences of the peak energy and band width are well interpreted by the strongly localized carrier thermal hopping process and Gaussian shape of band tails states, respectively. The localized wells and band tails at conduction minimum are considered to be induced by the surface oxygen defects and local potential fluctuation in SnO{sub 2} QDs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms

A recent expert elicitation showed that model validation remains one of the largest barriers for commercial wind farm control deployment. The Gaussian-shaped wake deficit model has grown in popularity in wind farm field experiments, yet its validation for larger farms and throughout annual operation remains limited. This article addresses this scientific gap, providing a model comparison of the Gaussian wind farm model with historical data of three offshore wind farms. The energy ratio is used to quantify the model’s accuracy. We assume a fixed turbulence intensity of $I_∞$ = 6% and a standard deviation on the inflow wind direction of $σ_{wd}$ = 3° in our Gaussian model. First, we demonstrate the non-uniqueness issue of $I_∞$ and $σ_{wd}$, which display a waterbed effect when considering the energy ratios. Second, we show excellent agreement between the Gaussian model and historical data for most wind directions in the Offshore Windpark Egmond aan Zee (OWEZ) and Westermost Rough wind farms (36 and 35 wind turbines, respectively) and wind turbines on the outer edges of the Anholt wind farm (110 turbines). Turbines centrally positioned in the Anholt wind farm show larger model discrepancies, likely due to deep-array effects that are not captured in the model. A second source of discrepancy is hypothesized to be inflow heterogeneity. In future work, the Gaussian wind farm model will be adapted to address those weaknesses.

17 WIND ENERGY↗
DOE OSTI2015

SELECTION OF BURST-LIKE TRANSIENTS AND STOCHASTIC VARIABLES USING MULTI-BAND IMAGE DIFFERENCING IN THE PAN-STARRS1 MEDIUM-DEEP SURVEY

We present a novel method for the light-curve characterization of Pan-STARRS1 Medium Deep Survey (PS1 MDS) extragalactic sources into stochastic variables (SVs) and burst-like (BL) transients, using multi-band image-differencing time-series data. We select detections in difference images associated with galaxy hosts using a star/galaxy catalog extracted from the deep PS1 MDS stacked images, and adopt a maximum a posteriori formulation to model their difference-flux time-series in four Pan-STARRS1 photometric bands g {sub P1}, r {sub P1}, i {sub P1}, and z {sub P1}. We use three deterministic light-curve models to fit BL transients; a Gaussian, a Gamma distribution, and an analytic supernova (SN) model, and one stochastic light-curve model, the Ornstein-Uhlenbeck process, in order to fit variability that is characteristic of active galactic nuclei (AGNs). We assess the quality of fit of the models band-wise and source-wise, using their estimated leave-out-one cross-validation likelihoods and corrected Akaike information criteria. We then apply a K-means clustering algorithm on these statistics, to determine the source classification in each band. The final source classification is derived as a combination of the individual filter classifications, resulting in two measures of classification quality, from the averages across the photometric filters of (1) the classifications determined from the closest K-means cluster centers, and (2) the square distances from the clustering centers in the K-means clustering spaces. For a verification set of AGNs and SNe, we show that SV and BL occupy distinct regions in the plane constituted by these measures. We use our clustering method to characterize 4361 extragalactic image difference detected sources, in the first 2.5 yr of the PS1 MDS, into 1529 BL, and 2262 SV, with a purity of 95.00% for AGNs, and 90.97% for SN based on our verification sets. We combine our light-curve classifications with their nuclear or off-nuclear host galaxy offsets, to define a robust photometric sample of 1233 AGNs and 812 SNe. With these two samples, we characterize their variability and host galaxy properties, and identify simple photometric priors that would enable their real-time identification in future wide-field synoptic surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A robust estimator of mutual information for deep learning interpretability

Abstract We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning (DL) models. To accurately estimate MI from a finite number of samples, we present GMM-MI (pronounced ‘Jimmie’), an algorithm based on Gaussian mixture models that can be applied to both discrete and continuous settings. GMM-MI is computationally efficient, robust to the choice of hyperparameters and provides the uncertainty on the MI estimate due to the finite sample size. We extensively validate GMM-MI on toy data for which the ground truth MI is known, comparing its performance against established MI estimators. We then demonstrate the use of our MI estimator in the context of representation learning, working with synthetic data and physical datasets describing highly non-linear processes. We train DL models to encode high-dimensional data within a meaningful compressed (latent) representation, and use GMM-MI to quantify both the level of disentanglement between the latent variables, and their association with relevant physical quantities, thus unlocking the interpretability of the latent representation. We make GMM-MI publicly available in this GitHub repository.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Automated Bayesian high-throughput estimation of plasma temperature and density from emission spectroscopy

Here, this paper introduces a novel approach for automated high-throughput estimation of plasma temperature and density using atomic emission spectroscopy, integrating Bayesian inference with sophisticated physical models. We provide an in-depth examination of Bayesian methods applied to the complexities of plasma diagnostics, supported by a robust framework of physical and measurement models. Our methodology is demonstrated using experimental observations in the field of magneto-inertial fusion, focusing on individual and sequential shot analyses of the Plasma Liner Experiment at LANL. The results demonstrate the effectiveness of our approach in enhancing the accuracy and reliability of plasma parameter estimation and in using the analysis to reveal the deep hidden structure in the data. This study not only offers a new perspective of plasma analysis but also paves the way for further research and applications in nuclear instrumentation and related domains.

Bayesian inference↗
NASA NTRS1990

Rough surface scattering results based on bandpass autocorrelation forms

Surface-height autocorrelation forms such as Gaussian and exponential are often used in studies of near-normal incidence rough-surface scattering. Such models require the existence of a constant, or DC, value in the spectrum. The consequences of autocorrelation forms that correspond to spectral processes that are essentially bandpass in nature are examined. One such process is that of ocean wind waves. In this case, the spectral components do not extend down to zero frequency. The physical optics backscatter theory is reexamined relative to such autocorrelation functions. Experimental results obtained from a wavetank are compared to the autocorrelation model used in the analysis. The analysis indicates that Gaussian correlation length or mean-square slope is not an appropriate parameter for narrowband conditions and that significant slope is a more relevant parameter. Inherent in the deep-phase assumption is some form of slope dependency. The analysis given (and variants thereof) can be used to provide insight into the physical effects of separate spectral components and of spectral directionality.

Miller, Lee S.↗
DOE OSTI2022

A Decision-Making Machine Learning Approach in Hermite Spectral Approximations of Partial Differential Equations

The accuracy and effectiveness of Hermite spectral methods for the numerical discretization of partial differential equations on unbounded domains are strongly affected by the amplitude of the Gaussian weight function employed to describe the approximation space. This is particularly true if the problem is under-resolved, i.e., there are no enough degrees of freedom. The issue becomes even more crucial when the equation under study is time-dependent, forcing in this way the choice of Hermite functions where the corresponding weight depends on time. In order to adapt dynamically the approximation space, it is here proposed an automatic decision-making process that relies on machine learning techniques, such as deep neural networks and support vector machines. The algorithm is numerically tested with success on a simple 1D problem, but the main goal is its exportability in the context of more serious applications. Here we also show at the end an application in the framework of plasma physics.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1995

Effects of correlated noise on the full-spectrum combining and complex-symbol combining arraying techniques

The process of combining telemetry signals received at multiple antennas, commonly referred to as arraying, can be used to improve communication link performance in the Deep Space Network (DSN). By coherently adding telemetry from multiple receiving sites, arraying produces an enhancement in signal-to-noise ratio (SNR) over that achievable with any single antenna in the array. A number of different techniques for arraying have been proposed and their performances analyzed in past literature. These analyses have compared different arraying schemes under the assumption that the signals contain additive white Gaussian noise (AWGN) and that the noise observed at distinct antennas is independent. In situations where an unwanted background body is visible to multiple antennas in the array, however, the assumption of independent noises is no longer applicable. A planet with significant radiation emissions in the frequency band of interest can be one such source of correlated noise. For example, during much of Galileo's tour of Jupiter, the planet will contribute significantly to the total system noise at various ground stations. This article analyzes the effects of correlated noise on two arraying schemes currently being considered for DSN applications: full-spectrum combining (FSC) and complex-symbol combining (CSC). A framework is presented for characterizing the correlated noise based on physical parameters, and the impact of the noise correlation on the array performance is assessed for each scheme.

Vazirani, P.↗
DOE OSTI2019

DeepCMB: Lensing reconstruction of the cosmic microwave background with deep neural networks

Next-generation cosmic microwave background (CMB) experiments will have lower noise and therefore increased sensitivity, enabling improved constraints on fundamental physics parameters such as the sum of neutrino masses and the tensor-to-scalar ratio r. Achieving competitive constraints on these parameters requires high signal-to-noise extraction of the projected gravitational potential from the CMB maps. Standard methods for reconstructing the lensing potential employ the quadratic estimator (QE). However, the QE performs suboptimally at the low noise levels expected in upcoming experiments. Other methods, like maximum likelihood estimators (MLE), are under active development. In this work, we demonstrate reconstruction of the CMB lensing potential with deep convolutional neural networks (CNN) - ie, a ResUNet. The network is trained and tested on simulated data, and otherwise has no physical parametrization related to the physical processes of the CMB and gravitational lensing. We show that, over a wide range of angular scales, ResUNets recover the input gravitational potential with a higher signal-to-noise ratio than the QE method, reaching levels comparable to analytic approximations of MLE methods. We demonstrate that the network outputs quantifiably different lensing maps when given input CMB maps generated with different cosmologies. We also show we can use the reconstructed lensing map for cosmological parameter estimation. This application of CNN provides a few innovations at the intersection of cosmology and machine learning. First, while training and regressing on images, we predict a continuous-variable field rather than discrete classes. Second, we are able to establish uncertainty measures for the network output that are analogous to standard methods. We expect this approach to excel in capturing hard-to-model non-Gaussian astrophysical foreground and noise contributions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Quantum Simulation of Molecular Dynamics Processes─A Benchmark Study Using a Classical Simulator and Present-Day Quantum Hardware

Here, we explore how the fundamental problems in quantum molecular dynamics can be modeled using classical simulators (emulators) of quantum computers and the actual quantum hardware available to us today. The list of problems we tackle includes propagation of a free wave packet, vibration of a harmonic oscillator, and tunneling through a barrier. Each of these problems starts with the initial wave packet setup. Although Qiskit provides a general method for initializing wave functions, in most cases it generates deep quantum circuits. While these circuits perform well on noiseless simulators, they suffer from excessive noise on quantum hardware. To overcome this issue, we designed a shallower quantum circuit for preparing a Gaussian-like initial wave packet, which improves the performance of real hardware. Next, quantum circuits are implemented to apply the kinetic and potential energy operators for the evolution of a wave function over time. The results of our modeling on classical emulators of quantum hardware agree perfectly with the results obtained using the traditional (classical) methods. This serves as a benchmark and demonstrates that the quantum algorithms and Qiskit codes we developed are accurate. However, the results obtained on the actual quantum hardware available today, such as IBM’s superconducting qubits and IonQ’s trapped ions, indicate large discrepancies due to hardware limitations. This work highlights both the potential and challenges of using quantum computers to solve fundamental quantum molecular dynamics problems.

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

Enhanced ultraviolet electroluminescence and spectral narrowing from ZnO quantum dots/GaN heterojunction diodes by using high-k HfO{sub 2} electron blocking layer

We demonstrated the capability of realizing enhanced ZnO-related UV emissions by using the low-cost and solution-processable ZnO quantum dots (QDs) with the help of a high-k HfO{sub 2} electron blocking layer (EBL) for the ZnO QDs/p-GaN light-emitting diodes (LEDs). Full-width at half maximum of the LED devices was greatly decreased from ∼110 to ∼54 nm, and recombinations related to nonradiative centers were significantly suppressed with inserting HfO{sub 2} EBL. The electroluminescence of the ZnO QDs/HfO{sub 2}/p-GaN LEDs demonstrated an interesting spectral narrowing effect with increasing HfO{sub 2} thickness. The Gaussian fitting revealed that the great enhancement of the Zn{sub i}-related emission at ∼414 nm whereas the deep suppression of the interfacial recombination at ∼477 nm should be the main reason for the spectral narrowing effect.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Preliminary Preclosure Accident Consequence Analysis for the APM Conceptual Design - 18090

The Nuclear Waste Management Organization (NWMO) is implementing Adaptive Phased Management (APM), Canada's plan for the long-term management of used nuclear fuel. The APM approach encompasses centralized containment and isolation of the used fuel in a Deep Geological Repository (DGR) in a suitable rock formation, such as crystalline rock or sedimentary rock, in an informed and willing host. This paper describes a preliminary analysis of the potential dose consequences to the public under accident conditions associated with the operation of a DGR and related surface processing facilities. These results are for a generic site and considers exposure to a person standing at a potential fenceline location under conservative atmospheric conditions. Radiological dose consequences to the public were calculated for accidents identified in the preliminary hazard identification. The presence or absence of ventilation system HEPA filters was also considered in combination with specific accident scenarios. A Gaussian dispersion model considering the most stable atmospheric condition (Pasquill stability category F) was used to estimate the exposure of a member of the public in the direct plume path at the location of maximum airborne radionuclide concentrations. The public dose was calculated via the inhalation, air immersion and ground exposure pathways. For all accidents considered, the calculated public dose was low, up to about 0.8 mSv for a fall of an UFTP with container breach. The total dose to the public was also calculated at various distances from the Used Fuel Packaging Plant, Ventilation Shaft, and Main Shaft. For all accidents considered, the calculated dose to the public was low, below 1 mSv even at the minimum 100 m distance evaluated. Sensitivity cases were also carried out to determine the effect of stack release height, the effluent exit velocity, and the release orientation on the calculated minimum site boundary distance. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Improving Dark Energy Constraints Using Low-Redshift Large-Scale Structures

The primary goal of this project was to improve constraints on dark energy measurements by improving our ability to extract cosmological information from low redshift large-scale structures. PI Clowe's project's primary aim was to reduce the bias in measurements of the masses of clusters of galaxies to a level where the evolution of the cluster mass function can be used in the Vera Rubin Observatory's Legacy Survey of Space and Time Dark Energy Science Collaboration survey to improve the accuracy of the measurement of dark energy and other cosmological parameters. Co-PI Seo's project studied observational systematics affecting large-scale clustering of galaxies, which will be used to improve dark energy constraints from the Dark Energy Spectroscopic Instrument (DESI). The cluster lensing project employed a series of simulations and observations of clusters of galaxies to test numerous potential systematic errors in cluster mass measurements using weak gravitational lensing as the accuracy of current weak lensing measurements are more than order of magnitude worse than what is required to use clusters of galaxies for accurate determination of dark energy parameters. PI Clowe and group developed and analyzed simulations to test for, and correct biases introduced in, the weak lensing measurement process. Finally, PI Clowe and group developed a method of detecting clusters using galaxy overdensities and applied the method to the BLISS and DES surveys. The success of spectroscopic dark energy mission such as the extended Baryon Oscillation Spectroscopic Survey (eBOSS) and the Dark Energy Spectroscopic Instrument (DESI) will depend on a thorough understanding of various observational systematics in the target density fluctuations that would give rise to spurious, non-cosmological signals. PI Seo and group developed a deep learning, artificial neural network (ANN) technique that modeled and mitigated such effects, aimed at deriving more robust galaxy clustering signals not only for the baryon acoustic oscillation feature and redshift-space distortions but also for primordial non-Gaussianity constraint.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Impact of Three-Phase Relative Permeability and Hysteresis Models on Forecasts of Storage Associated With CO 2 -EOR

Geological CO 2 sequestration in conjunction with enhanced oil recovery (CO 2 -EOR) includes complex multiphase flow processes compared to CO 2 storage in deep saline aquifers. Two of the most critical factors affecting multiphase flow in CO 2 -EOR are three-phase relative permeability and associated hysteresis, both of which are difficult to measure and are usually represented by numerical interpolation models. The purpose of this research is to improve understanding of (1) the relative impacts of different three-phase relative permeability models and hysteresis models on CO 2 trapping mechanisms, and (2) uncertainty associated with these two factors. Four different three-phase relative permeability models and three hysteresis models were applied to simulations of an active CO 2 -EOR site, the SACROC unit located in western Texas. To eliminate potential bias of deterministic parameters, we utilized a sequential Gaussian simulation technique to generate 50 realizations to describe heterogeneity of porosity and permeability, based on data obtained from well logs and seismic survey. Simulation results of forecasted CO 2 storage suggested that (1) the choice of three-phase relative permeability model and hysteresis model led to noticeable impacts on forecasted CO 2 sequestration capacity; (2) impacts of three-phase relative permeability models and hysteresis models on CO 2 trapping are small during the CO 2 -EOR injection period, and increase during the post-EOR CO 2 injection period; (3) the specific choice of hysteresis model is more important relative to the choice of three-phase relative permeability model; and (4) using the recommended three-phase WAG (Water-Alternating-Gas) hysteresis model may increase the impact of three-phase relative permeability models and uncertainty due to heterogeneity.

42 ENGINEERING↗
DOE OSTI2022

Discriminative Dimensionality Reduction using Deep Neural Networks for Clustering of LIGO Data

In this paper, leveraging the capabilities of neural networks for modeling the non-linearities that exist in the data, we propose several models that can project data into a low dimensional, discriminative, and smooth manifold. The proposed models can transfer knowledge from the domain of known classes to a new domain where the classes are unknown. A clustering algorithm is further applied in the new domain to find potentially new classes from the pool of unlabeled data. The research problem and data for this paper originated from the Gravity Spy project which is a side project of Advanced Laser Interferometer Gravitational-wave Observatory (LIGO). The LIGO project aims at detecting cosmic gravitational waves using huge detectors. However non-cosmic, non-Gaussian disturbances known as "glitches", show up in gravitational-wave data of LIGO. This is undesirable as it creates problems for the gravitational wave detection process. Gravity Spy aids in glitch identification with the purpose of understanding their origin. Since new types of glitches appear over time, one of the objective of Gravity Spy is to create new glitch classes. Towards this task, we offer a methodology in this paper to accomplish this.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗