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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Learning viscoelasticity models from indirect data using deep neural networks

In this study, we propose a novel approach to model viscoelasticity materials, where rate-dependent and non-linear constitutive relationships are approximated with deep neural networks. We assume that inputs and outputs of the neural networks are not directly observable, and therefore common training techniques with input–output pairs for the neural networks are inapplicable. To that end, we develop a novel computational approach to both calibrate parametric and learn neural-network-based constitutive relations of viscoelasticity materials from indirect displacement data in the context of multiple-physics systems. We show that limited displacement data holds sufficient information to quantify the viscoelasticity behavior. We formulate the inverse computation – modeling viscoelasticity properties from observed displacement data – as a PDE-constrained optimization problem and minimize the error functional using a gradient-based optimization method. The gradients are computed by a combination of automatic differentiation and implicit function differentiation rules. The effectiveness of our method is demonstrated through numerous benchmark problems in geomechanics and porous media transport.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Deep-Learning based Reconstruction of the Shower Maximum $X_{\mathrm{max}}$ using the Water-Cherenkov Detectors of the Pierre Auger Observatory

The atmospheric depth of the air shower maximum X max is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of X max are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of X max from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of X max . The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long short-term memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed X max using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than 25 g/cm 2 at energies above 2 × 10 19 eV.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Variations in the Width, Density, and Direction of the Palomar 5 Tidal Tails

Stars that escape globular clusters form tidal tails that are predominantly shaped by the global distribution of mass in the Galaxy, but also preserve a historical record of small-scale perturbations. Using deep grz photometry from DECaLS, we present highly probable members of the tidal tails associated with the evaporating globular cluster Palomar 5. These data yield the cleanest view of a stellar stream beyond ≈20 kpc and reveal: (1) a wide, low surface-brightness extension of the leading tail; (2) significant density variations along the stream; and (3) sharp changes in the direction of both the leading and the trailing tail. In the fiducial Milky Way model, a rotating bar perturbs the Palomar 5 tails and can produce streams with similar width and density profiles to those observed. However, the deviations of the stream track in this simple model do not match those observed in the Palomar 5 trailing tail, indicating the need for an additional source of perturbation. These discoveries open up the possibility of measuring the population of perturbers in the Milky Way, including dark-matter subhalos, with an ensemble of stellar streams and deep photometry alone.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

DeepDist: real-value inter-residue distance prediction with deep residual convolutional network

Abstract Background Driven by deep learning, inter-residue contact/distance prediction has been significantly improved and substantially enhanced ab initio protein structure prediction. Currently, most of the distance prediction methods classify inter-residue distances into multiple distance intervals instead of directly predicting real-value distances. The output of the former has to be converted into real-value distances to be used in tertiary structure prediction. Results To explore the potentials of predicting real-value inter-residue distances, we develop a multi-task deep learning distance predictor (DeepDist) based on new residual convolutional network architectures to simultaneously predict real-value inter-residue distances and classify them into multiple distance intervals. Tested on 43 CASP13 hard domains, DeepDist achieves comparable performance in real-value distance prediction and multi-class distance prediction. The average mean square error (MSE) of DeepDist’s real-value distance prediction is 0.896 Å 2 when filtering out the predicted distance ≥ 16 Å, which is lower than 1.003 Å 2 of DeepDist’s multi-class distance prediction. When distance predictions are converted into contact predictions at 8 Å threshold (the standard threshold in the field), the precision of top L/5 and L/2 contact predictions of DeepDist’s multi-class distance prediction is 79.3% and 66.1%, respectively, higher than 78.6% and 64.5% of its real-value distance prediction and the best results in the CASP13 experiment. Conclusions DeepDist can predict inter-residue distances well and improve binary contact prediction over the existing state-of-the-art methods. Moreover, the predicted real-value distances can be directly used to reconstruct protein tertiary structures better than multi-class distance predictions due to the lower MSE. Finally, we demonstrate that predicting the real-value distance map and multi-class distance map at the same time performs better than predicting real-value distances alone.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction

Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries. While it is known that protein structure directly impacts protein function, many functional prediction tasks use only protein sequence. In this work, we isolate protein structure to make functional annotations for proteins in the Protein Data Bank in order to study the expressiveness of different structure-based prediction schemes. We present PersGNN - an end-to-end trainable deep learning model that combines graph representation learning with topological data analysis to capture a complex set of both local and global structural features. While variations of these techniques have been successfully applied to proteins before, we demonstrate that our hybridized approach, PersGNN, outperforms either method on its own as well as a baseline neural network that learns from the same information. PersGNN achieves a 9.3% boost in area under the precision recall curve (AUPR) compared to the best individual model, as well as high F1 scores across different gene ontology categories, indicating the transferability of this approach.

Swenson, Nicolas↗
DOE OSTI2023

Emulating aerosol optics with randomly generated neural networks

Abstract. Atmospheric aerosols have a substantial impact on climate and remain one of the largest sources of uncertainty in climate prediction. Accurate representation of their direct radiative effects is a crucial component of modern climate models. However, direct computation of the radiative properties of aerosol populations is far too computationally expensive to perform in a climate model, so optical properties are typically approximated using a parameterization. This work develops artificial neural networks (ANNs) capable of replacing the current aerosol optics parameterization used in the Energy Exascale Earth System Model (E3SM). A large training dataset is generated by using Mie code to directly compute the optical properties of a range of atmospheric aerosol populations given a large variety of particle sizes, wavelengths, and refractive indices. Optimal neural architectures for shortwave and longwave bands are identified by evaluating ANNs with randomly generated wirings. Randomly generated deep ANNs are able to outperform conventional multilayer-perceptron-style architectures with comparable parameter counts. Finally, the ANN-based parameterization produces significantly more accurate bulk aerosol optical properties than the current parameterization when compared with direct Mie calculations using mean absolute error. The success of this approach makes possible the future inclusion of much more sophisticated representations of aerosol optics in climate models that cannot be captured by extension of the existing parameterization scheme and also demonstrates the potential of random-wiring-based neural architecture search in future applications in the Earth sciences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Lagrangian Study of the Transition from Shallow to Deep Convection Using ASR Observations and Les Simulations (Final Report)

With the support from this grant, we have made the following accomplishments. We have found that a formula for fractional entrainment rate, a key aspect for parameterization moist convection, that works for both shallow and deep convection, which provide a promising direction for unified treatment of entrainment for both shallow and deep convection. We also examined the formulation of vertical velocity and showed that the effect of non-hydrostatic pressure and entrainment on vertical velocity are not constants, as assumed in past studies, but can be computed using a simple model for the cloudy updrafts. These results also apply to both deep and shallow convection, which opens the door for a more physically based parameterization of the updraft vertical velocity. We have also conducted extensive analysis of the RACORO aircraft data and have published the results in Wei et al. (2020). We have made extensive effort scrutinizing the data in detail and examining the various hypotheses with the aid of the LES simulations. This was the first application of the modified Paluch diagram on modern aircraft data and the first comprehensive analysis of mixing in clouds using real data (instead of only LES output) in a very long time. These analyses enhanced the science return of the ASR aircraft measurements. This grant also supported a study of the collisions among cold pools and an effort to build parameterizations of moist convection using tools developed in control theory.

42 ENGINEERING↗
DOE OSTI2021

Phase Equilibrium of Water with Hexagonal and Cubic Ice Using the SCAN Functional

Machine learning models are rapidly becoming widely used to simulate complex physicochemical phenomena with ab initio accuracy. Here, we use one such model as well as direct density functional theory (DFT) calculations to investigate the phase equilibrium of water, hexagonal ice (Ih), and cubic ice (Ic), with an eye toward studying ice nucleation. The machine learning model is based on deep neural networks and has been trained on DFT data obtained using the SCAN exchange and correlation functional. We use this model to drive enhanced sampling simulations aimed at calculating a number of complex properties that are out of reach of DFT-driven simulations and then employ an appropriate reweighting procedure to compute the corresponding properties for the SCAN functional. This approach allows us to calculate the melting temperature of both ice polymorphs, the driving force for nucleation, the heat of fusion, the densities at the melting temperature, the relative stability of ices Ih and Ic, and other properties. We find a correct qualitative prediction of all properties of interest. In some cases, quantitative agreement with experiment is better than for state-of-the-art semiempirical potentials for water. Furthermore, our results also show that SCAN correctly predicts that ice Ih is more stable than ice Ic.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Optimal Control of SOEC-Based Hydrogen Production Systems for Demand Response Using Deep Reinforcement Learning in Smart Grids

Solid oxide electrolysis cell (SOEC) hydrogen production technology can range in size from small, appliance-size equipment to large-scale, central production facilities that can be tied directly to renewable or non-greenhouse-gas-emitting forms of electricity production, making it an ideal resource for demand response (DR). The SOEC hydrogen production system is a complex integrated system that encompasses fluid dynamics, electrical dynamics, and electrochemical and thermal dynamics, all of which involve non-linearity and non-convexity. Proper control of the SOEC hydrogen production system is crucial to enable its participation in the DR program. Here, to overcome the difficulty of designing an explicit control law for such nonlinear systems with nonconvex optimization features in DR applications, deep reinforcement learning (DRL) is explored to achieve the optimal control of the SOEC system for DR participation. Specifically, a twin delayed deterministic policy gradient (TD3) control framework is applied to achieve optimal response performance during DR events by considering power tracking error and hydrogen production efficiency with a suitable reward function. Two case studies with grid connections for tracking different DR commands were investigated. The first case study involved operating conditions reaching the boundaries, while the second involved operating conditions within the boundaries. The results showed that the proposed DRL-based control for SOEC can track the DR signal in a timely manner while maintaining high energy efficiency.

08 HYDROGEN↗
DOE OSTI2020

DPM: A deep learning PDE augmentation method with application to large-eddy simulation

A framework is introduced that leverages known physics to reduce overfitting in machine learning for scientific applications. The partial differential equation (PDE) that expresses the physics is augmented with a neural network that uses available data to learn a description of the corresponding unknown or unrepresented physics. Training within this combined system corrects for missing, unknown, or erroneously represented physics, including discretization errors associated with the PDE's numerical solution. For optimization of the network within the PDE, an adjoint PDE is solved to provide high-dimensional gradients, and a stochastic adjoint method (SAM) further accelerates training. Additionally, the approach is demonstrated for large-eddy simulation (LES) of turbulence. High-fidelity direct numerical simulations (DNS) of decaying isotropic turbulence provide the training data used to learn sub-filter-scale closures for the filtered Navier–Stokes equations. Out-of-sample comparisons show that the deep learning PDE method outperforms widely-used models, even for filter sizes so large that they become qualitatively incorrect. It also significantly outperforms the same neural network when a priori trained based on simple data mismatch, not accounting for the full PDE. Measures of discretization errors, which are well-known to be consequential in LES, point to the importance of the unified training formulation's design, which without modification corrects for them. For comparable accuracy, simulation runtime is significantly reduced. A relaxation of the typical discrete enforcement of the divergence-free constraint in the solver is also successful, instead allowing the DPM to approximately enforce incompressibility physics. Since the training loss function is not restricted to correspond directly to the closure to be learned, training can incorporate diverse data, including experimental data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Quantitative Particle Analysis of Neptunium-237 Oxides: Optimization of MAMA Analysis for Modified Direct Denitration Products

The production of plutonium-238 through irradiation of neptunium-237 ( 237 Np) target materials for the use in radioisotope thermoelectric generators is paramount for continued deep space exploration. This work employs scanning electron microscopy to analyze 237 Np materials coupled with a well-developed image analysis framework (Morphological Analysis for Material Attribution, or MAMA) to determine the degree of micron-scale homogeneity in the materials. This work demonstrated how the quantification of particle characteristics can validate production materials and affirm the qualitative similarities observed in micrographs. The 237 Np oxide particle analysis determined that the materials from five production runs were quantitatively homogenous (significant at α = 0.05) in particle area, circularity, equivalent circular diameter, and ellipse aspect ratio, with two of the sampling dates having statistically significant different means for one of the four characteristics. Furthermore, these metrics not only confirm general homogeneity of the material but also expand the application of MAMA workflows to 237 Np materials, demonstrating the utility of MAMA analysis for a wider breadth of nuclear materials than previously reported. In the open literature, this study is the first time that these microanalytical techniques were applied to 237 Np materials to this degree.

MAMA↗
DOE OSTI2024

High-fidelity dimer excitations using quantum hardware

The quantum simulation of entangled spin systems can play a central role in quantum magnetic materials discovery. Additionally, the simulation of spectroscopic signatures, such as the dynamical structure factor accessed in inelastic neutron scattering (INS), necessitates a long timescale for circuit evolution. This is because the energy resolution is directly related to the time over which the circuit could be meaningfully evolved. However, canonical Trotterization requires deep circuits precluding such long-time evolution—even for a small number of qubits. Here, in this study, we demonstrate “direct” resource efficient fast-forwarding (REFF) measurements with short-depth circuits that can be used to capture longer time dynamics of spin Hamiltonians. We showcase the results of the dynamics of a quantum spin dimer, the basic quantum unit of emergent many-body spin systems, whose density of states we simulate accurately. The long temporal evolution and measurement of the two-spin correlation functions enable the calculation of the dynamical structure factor S⁡(Q = 0, ω) measured in the neutron scattering cross-section. We exhibit the clarity of the triplet gap and the triplet splitting of the quantum dimer with class-leading fidelity that enables comparison to experimental neutron data. Our results on current circuit hardware outline an important workflow to predict and benchmark against the outputs of INS experiments of quantum magnets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Time-Resolved Line Shapes of Single Quantum Emitters via Machine Learned Photon Correlations

Solid-state single-photon emitters (SPEs) are quantum light sources that combine atomlike optical properties with solid-state integration and fabrication capabilities. SPEs are hindered by spectral diffusion, where the emitter’s surrounding environment induces random energy fluctuations. Timescales of spectral diffusion span nanoseconds to minutes and require probing single emitters to remove ensemble averaging. Photon correlation Fourier spectroscopy (PCFS) can be used to measure time-resolved single emitter line shapes, but is hindered by poor signal-to-noise ratio in the measured correlation functions at early times due to low photon counts. Here, we develop a framework to simulate PCFS correlation functions directly from diffusing spectra that match well with experimental data for single colloidal quantum dots. We use these simulated datasets to train a deep ensemble autoencoder machine learning model that outputs accurate, noiseless, and probabilistic reconstructions of the noisy correlations. Using this model, we obtain reconstructed time-resolved single dot emission line shapes at timescales as low as 10 ns, which are otherwise completely obscured by noise. This enables PCFS to extract optical coherence times on the same timescales as Hong-Ou-Mandel two-photon interference, but with the advantage of providing spectral information in addition to estimates of photon indistinguishability. Further, our machine learning approach is broadly applicable to different photon correlation spectroscopy techniques and SPE systems, offering an enhanced tool for probing single emitter line shapes on previously inaccessible timescales.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Factors Impacting Nuclear Energy Share in U.S. Energy Markets

The purpose of this report is to collate information and findings from recent studies conducted by national and international bodies/institutes to identify approaches for maintaining/enhancing the role of nuclear energy in the current and future energy mix of the United States. This report shows how nuclear generation grew quickly to provide 20% of U.S. electricity, sustaining that level for three decades without the benefit of new construction, but is now projected to decline going forward. Nuclear construction costs in the U.S. spiraled out of control, ending construction for two decades and the recent resumption of construction has continued the pattern of schedule delays and cost overruns. However, nuclear operations exhibited strong learning, achieving and sustaining the highest capacity factor of any electricity generation technology and license extensions and uprates have sustained nuclear market share. The share of nuclear energy in the U.S. electricity market is projected to decline by ~1/3rd over the next 30 years The report provides an overview of how the markets work in theory and in practice. It indicates how market deregulation and clean energy policies have created conditions where nuclear plants are being retired for economic rather than technical reasons. The report also shows how many of the markets do not in practice have free competition but instead have outcomes that are being determined more and more by policy instead of market forces. While electricity costs from existing nuclear plants are low, electricity from new builds is projected to be too expensive to be competitive head-to-head with natural gas, even for nth-of-a-kind costs. Wind and solar energy have enjoyed an extended period of sustained subsidy. This protected environment has resulted in a sustained reduction in plant level costs to the point that some of these Variable Renewable Energy (VRE) technologies are becoming competitive even if their direct subsidies are removed. But plant level costs underestimate total VRE costs which include a number of system-level externalities. The incremental system value of additional VRE capacity was shown to decline as market share increases, with solar value declining more quickly than wind. The report closes with examination of a possible future for nuclear generation as part of deep decarbonization of the electricity sector. This approach avoids direct competition with natural gas. The two options for achieving 100% decarbonization are to use only renewables or to use all zero emissions technologies, and the report shows the second approach is much less expensive than the first.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

MIGHTEE: total intensity radio continuum imaging and the COSMOS/XMM-LSS Early Science fields

ABSTRACT MIGHTEE is a galaxy evolution survey using simultaneous radio continuum, spectropolarimetry, and spectral line observations from the South African MeerKAT telescope. When complete, the survey will image ∼20 deg2 over the COSMOS, E-CDFS, ELAIS-S1, and XMM-Newton Large Scale Structure field (XMM-LSS) extragalactic deep fields with a central frequency of 1284 MHz. These were selected based on the extensive multiwavelength data sets from numerous existing and forthcoming observational campaigns. Here, we describe and validate the data processing strategy for the total intensity continuum aspect of MIGHTEE, using a single deep pointing in COSMOS (1.6 deg2) and a three-pointing mosaic in XMM-LSS (3.5 deg2). The processing includes the correction of direction-dependent effects, and results in thermal noise levels below 2 $\mathrm{\mu }$Jy beam−1 in both fields, limited in the central regions by classical confusion at ∼8 arcsec angular resolution, and meeting the survey specifications. We also produce images at ∼5 arcsec resolution that are ∼3 times shallower. The resulting image products form the basis of the Early Science continuum data release for MIGHTEE. From these images we extract catalogues containing 9896 and 20 274 radio components in COSMOS and XMM-LSS, respectively. We also process a close-packed mosaic of 14 additional pointings in COSMOS and use these in conjunction with the Early Science pointing to investigate methods for primary beam correction of broad-band radio images, an analysis that is of relevance to all full-band MeerKAT continuum observations, and wide-field interferometric imaging in general. A public release of the MIGHTEE Early Science continuum data products accompanies this article.

Heywood, I. (ORCID:0000000168645057)↗
DOE OSTI2022

Shape Evolution of C75 Large-Grain Niobium Half-Cells During Cavity Fabrication

The largely anisotropic deformation of large-grain Nb discs during deep drawing into half-cells poses a challenge for achieving a desired shape accuracy. Two 5-cell cavities for the C75 CEBAF cryomodule rework program have been fabricated at Jefferson Lab from large-grain Nb discs directly sliced from an ingot. The shape of the inner surface of eight half-cells has been inspected using a FARO Edge laser scanner during the fabrication process and compared to the reference shape. On average, approximately 63% of the half-cell inner surface was found to be within 0.1 mm of the reference shape and ~90% to be within 0.2 mm, after the final equator machining. Several 5-cell C75 cavities have also been fabricated at Research Instruments, Germany, and measurements of the shape accuracy using a Zeiss 3D coordinate measuring machine gave similar results. One half-cell was measured both at Research Instruments and Jefferson Lab for comparison.

Ciovati, G.↗
DOE OSTI2025

The role of the droplet interface in controlling the multiphase oxidation of thiosulfate by ozone

Predicting reaction kinetics in aqueous microdroplets, including aerosols and cloud droplets, is challenging due to the probability that the underlying reaction mechanism can occur both at the surface and in the interior of the droplet. Additionally, few studies directly measure the surface activities of doubly charged anions, despite their prevalence in the atmosphere. Here, deep-UV second harmonic generation spectroscopy is used to probe surface affinities of the doubly charged anions thiosulfate, sulfate, and sulfite, key species in the thiosulfate ozonation reaction mechanism. Thiosulfate has an appreciable surface affinity with a measured Gibbs free energy of adsorption of -7.3 ± 2.5 kJ mol -1 in neutral solution, while sulfate and sulfite exhibit negligible surface propensity. The Gibbs free energy is combined with data from liquid flat jet ambient pressure X-ray photoelectron spectroscopy to constrain the concentration of thiosulfate at the surface in our model. Stochastic kinetic simulations leveraging these novel measurements show that the primary reaction between thiosulfate and ozone occurs at the interface and in the bulk, with the contribution of the interface decreasing from ~65% at pH 5 to ~45% at pH 13. Additionally, sulfate, the major product of thiosulfate ozonation and an important species in atmospheric processes, can be produced by two different pathways at pH 5, one with a contribution from the interface of >70% and the other occurring predominantly in the bulk (>98%). The observations in this work have implications for mining wastewater remediation, atmospheric chemistry, and understanding other complex reaction mechanisms in multiphase environments. Future interfacial or microdroplet/aerosol chemistry studies should carefully consider the role of both surface and bulk chemistry.

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

Neutrino-induced charged-current coherent pion production for constraining the muon neutrino flux at DUNE

We study neutrino-induced charged-current coherent pion production ( ν μ CC - Coh π ) as a tool for constraining the neutrino flux at the Deep Underground Neutrino Experiment (DUNE). The neutrino energy and flavor in the process can be directly reconstructed from the outgoing particles, making it especially useful to specifically constrain the muon neutrino component of the total flux. The cross section of this process can be obtained using the Adler relation with the π -Ar elastic scattering cross section taken either from external data or, as we explore, from a simultaneous measurement in the DUNE near detector. We develop a procedure that leverages ν μ CC - Coh π events to fit for the neutrino flux while simultaneously accounting for relevant effects in the cross section. We project that this method has the statistical power to constrain the uncertainty on the normalization of the flux at its peak to a few percent. This study demonstrates the potential utility of a ν μ CC - Coh π flux constraint, though further work will be needed to determine the range of validity and precision of the Adler relation upon which it relies, as well as to measure the π -Ar elastic scattering cross section to the requisite precision. We discuss the experimental and phenomenological developments necessary to unlock the ν μ CC - Coh π process as a “standard candle” for neutrino experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗