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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Design and implementation of a portable diagnostic system for Thomson scattering and optical emission spectroscopy measurements

A diagnostic system, which has a design goal of high-portability, has been designed at Oak Ridge National Laboratory (ORNL). This project aims at providing measurements of key plasma parameters (ne, Te, ni, Ti) for fusion-relevant devices, utilizing Thomson scattering (TS) and optical emission spectroscopy (OES). The innovative design employs mostly commercial off-the-shelf instrumentation and a traveling team of researchers to conduct measurements at various magnetic-confinement plasma devices. The TS diagnostic uses a Quantel Q-smart 1500 Nd:YAG laser with a 2ω harmonic generator to produce up to 850 mJ of 532 nm laser pulses at 10 Hz. Collection optics placed at the detection port consists of an 11 × 3 optical fiber bundle, where the TS diagnostic uses an 11 × 1 subset array of the fibers, the OES diagnostic uses another 11 fibers, and the remaining fibers are available to the host institution. The detection system is comprised of two separate IsoPlane-320 spectrometers with triple-grating turrets of various line spacing and two PI-MAX 4 intensified CCD detectors, used simultaneously to measure a broad range of ion, impurity, and electron parameters. The self-contained diagnostic package also includes a data processing and storage system. The design and initial implementation of the TS-OES diagnostic system are described. The experiments from the proof-of-principle operation of the portable package on a high density (∼2.5 × 1022 m−3) and low-temperature (∼5 eV) electrothermal arc source at ORNL are also discussed.

Kafle, N. (ORCID:000000028144787X)↗
DOE OSTI2021

Guiding the design of oxidation-resistant Fe-based single atom alloy catalysts with insights from configurational space

The high activity and selectivity of Fe-based heterogeneous catalysts toward a variety of reactions that require the breaking of strong bonds are offset in large part by their considerable instability with respect to oxidative deactivation. While it has been shown that the stability of Fe catalysts is considerably enhanced by alloying them with precious metals (even at the single-atom limit), rational design criteria for choosing such secondary metals are still missing. Since oxidative deactivation occurs due to the strong binding of oxygen to Fe and reduction by adsorbed hydrogen mitigates the deactivation, we propose here to use the binding affinity of oxygen and hydrogen adatoms as the basis for rational design. As it would also be beneficial to use cheaper secondary metals, we have scanned over a large subset of 3d–5d mid-to late transition metal single atoms and computationally determined their effect on the oxygen and hydrogen adlayer binding as a function of chemical potential and adsorbate coverage. We further determine the underlying chemical origins that are responsible for these effects and connect them to experimentally tunable quantities. Our results reveal a reliable periodic trend wherein oxygen binding is weakened greatest as one moves right and down the periodic table. Hydrogen binding shows the same trend only at high (but relevant) coverages and otherwise tends to have its binding slightly increased in all systems. Trends with secondary metal coverage are also uncovered and connected to experimentally tunable parameters.

Hensley, Alyssa↗
DOE OSTI2023

Validation of IMEP on Alcator C-Mod and JET-ILW ELMy H-mode plasmas

Abstract The recently developed integrated model based on engineering parameters (IMEP) (Luda et al 2020 Nucl. Fusion 61 126048; Luda et al 2021 Nucl. Fusion 60 036023), so far validated on ASDEX Upgrade, has been tested on a database of 3 Alcator C-Mod and 55 JET-ILW ELMy (type I) H-mode stationary phases. The empirical pedestal transport model included in IMEP, consisting now of imposing a fixed value of R < ∇ T e > / T e , t o p = − 82.5 , allows an accurate prediction of the pedestal top temperature (when the pedestal top density is fixed to the experimental measurements) across these three machines with different sizes, when the pedestal is peeling–ballooning (PB) limited. Cases far from the ideal PB boundary, corresponding to high edge Spitzer resistivity, are instead strongly overpredicted by IMEP. A comparison between the predictions of Europed and IMEP for a subset of JET-ILW cases shows that IMEP can more accurately reproduce the experimental pedestal width. This allows IMEP to better capture profile effects on the pedestal stability, and therefore to correctly describe the negative effect of fueling on the pedestal pressure for PB limited cases. A strong correlation between the separatrix density and the fueling rate has been identified for a subset of JET-ILW cases, when taking into account different divertor configurations. Overall, these promising results encourage further developments of integrated models to obtain reliable predictions of pedestal and global confinement using only engineering parameters for present and future machines.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Using active learning to improve quasar identification for the DESI spectra processing pipeline

The Dark Energy Spectroscopic Instrument (DESI) survey uses an automatic spectral classification pipeline to classify spectra. QuasarNET is a convolutional neural network used as part of this pipeline originally trained using data from the Baryon Oscillation Spectroscopic Survey (BOSS). In this paper we implement an active learning algorithm to optimally select spectra to use for training a new version of the QuasarNET weights file using only DESI data, with the goal of improving classification accuracy. This active learning algorithm includes a novel outlier rejection step using a Self-Organizing Map to ensure we label spectra representative of the larger quasar sample observed in DESI. We perform two iterations of the active learning pipeline, assembling a final dataset of 5600 labeled spectra, a small subset of the approximately 1.3 million quasar targets in DESI's Data Release 1. When splitting the spectra into training and validation subsets we achieve similar performance to the previously trained weights file in completeness and purity calculated on the validation dataset but do so with less than one tenth of the amount of training data. The new weights also more consistently classify objects in the same way when used on unlabeled data compared to the old weights file. In the process of improving QuasarNET's classification accuracy we discovered a systemic error in QuasarNET's redshift estimation and used our findings to improve our understanding of QuasarNET's redshifts.

Machine learning↗
DOE OSTI2022

Identifying Transient Candidates in the Dark Energy Survey Using Convolutional Neural Networks

The ability to discover new transient candidates via image differencing without direct human intervention is an important task in observational astronomy. For these kind of image classification problems, machine learning techniques such as Convolutional Neural Networks (CNNs) have shown remarkable success. In this work, we present the results of an automated transient candidate identification on images with CNNs for an extant data set from the Dark Energy Survey Supernova program, whose main focus was on using Type Ia supernovae for cosmology. By performing an architecture search of CNNs, we identify networks that efficiently select non-artifacts (e.g., supernovae, variable stars, AGN, etc.) from artifacts (image defects, mis-subtractions, etc.), achieving the efficiency of previous work performed with random Forests, without the need to expend any effort in feature identification. The CNNs also help us identify a subset of mislabeled images. Performing a relabeling of the images in this subset, the resulting classification with CNNs is significantly better than previous results, lowering the false positive rate by 27% at a fixed missed detection rate of 0.05.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Plasma confinement state classification in fusion power plants: Profile reflectometer and ensemble diagnostics

As Fusion Pilot Plants (FPPs) are increasingly viewed as within reach, many engineering challenges remain. Not many diagnostics are expected to be available in a reactor environment. Survivability, maintainability, and limited port space substantially restrict the number of FPP-relevant diagnostics. One remaining challenge is developing tools and devices to extract plasma state information necessary for controlling an FPP from a limited subset of diagnostics. This work is part of an overarching project to address this challenge. The specific diagnostic subset to be used in FPPs is still under debate. We take the approach of developing machine-learning-based tools for different significant plasma state parameters, using already known FPP-viable diagnostics. Previously we developed a plasma confinement mode classifier utilizing the Electron Cyclotron Emission (ECE) diagnostic. Here, we expand on this by developing a Profile Reflectometer (PR) based classifier with 97% test accuracy, and an ensemble model that combines the ECE and PR models into a single model, achieving 99% test accuracy.

Clark, Randall [Univ. of California, San Diego, CA↗
DOE OSTI2020

A Topology Optimization Framework to Facilitate Adoption of Advanced District Thermal Energy Systems

Advanced district thermal energy systems, which circulate water at temperatures near ambient conditions, and facilitate the utilization of waste heat and renewable thermal sources, can lower the carbon-intensity of urban districts, advancing the U.N. Sustainable Development Goals. Optimization of the network topology — the selection of the best subset of buildings and the best network to connect them, to minimize life cycle cost — can increase adoption of these system in appropriate applications. The potential "solution space" of the topology optimization problem grows factorially with the number of buildings in the district, motivating the consideration of a design heuristic. In this study, a heuristic for the network selection was evaluated with an exhaustive search, for a prototypical four-building district. For the prototypical district considered, the heuristic was effective in selecting an optimal network topology. Additionally, it was found that, in this case, the selection of the subset of buildings was more influential on the life cycle cost than the selection of the network topology. This work is part of a larger effort to develop a topology optimization framework for district thermal energy systems, which is anticipated to address barriers to adoption of ambient-temperature systems.

28 EE - Advanced Manufacturing Office (EE-5A)↗
DOE OSTI2021

Adaptive pruning-based optimization of parameterized quantum circuits

Abstract Variational hybrid quantum–classical algorithms are powerful tools to maximize the use of noisy intermediate-scale quantum devices. While past studies have developed powerful and expressive ansatze, their near-term applications have been limited by the difficulty of optimizing in the vast parameter space. In this work, we propose a heuristic optimization strategy for such ansatze used in variational quantum algorithms, which we call ‘parameter-efficient circuit training (PECT)’. Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms, in which each iteration of the algorithm activates and optimizes a subset of the total parameter set. To update the parameter subset between iterations, we adapt the Dynamic Sparse Reparameterization scheme which was originally proposed for training deep convolutional neural networks. We demonstrate PECT for the Variational Quantum Eigensolver, in which we benchmark unitary coupled-cluster ansatze including UCCSD and k -UpCCGSD, as well as the Low-Depth Circuit Ansatz (LDCA), to estimate ground state energies of molecular systems. We additionally use a layerwise variant of PECT to optimize a hardware-efficient circuit for the Sycamore processor to estimate the ground state energy densities of the one-dimensional Fermi-Hubbard model. From our numerical data, we find that PECT can enable optimizations of certain ansatze that were previously difficult to converge and more generally can improve the performance of variational algorithms by reducing the optimization runtime and/or the depth of circuits that encode the solution candidate(s).

Physics↗
DOE OSTI2020

Prediction of histone post-translational modifications using deep learning

Abstract Motivation Histone post-translational modifications (PTMs) are involved in a variety of essential regulatory processes in the cell, including transcription control. Recent studies have shown that histone PTMs can be accurately predicted from the knowledge of transcription factor binding or DNase hypersensitivity data. Similarly, it has been shown that one can predict PTMs from the underlying DNA primary sequence. Results In this study, we introduce a deep learning architecture called DeepPTM for predicting histone PTMs from transcription factor binding data and the primary DNA sequence. Extensive experimental results show that our deep learning model outperforms the prediction accuracy of the model proposed in Benveniste et al. (PNAS 2014) and DeepHistone (BMC Genomics 2019). The competitive advantage of our framework lies in the synergistic use of deep learning combined with an effective pre-processing step. Our classification framework has also enabled the discovery that the knowledge of a small subset of transcription factors (which are histone-PTM and cell-type-specific) can provide almost the same prediction accuracy that can be obtained using all the transcription factors data. Availabilityand implementation https://github.com/dDipankar/DeepPTM. Supplementary information Supplementary data are available at Bioinformatics online.

Baisya, Dipankar Ranjan (ORCID:0000000267847359)↗
DOE OSTI2024

The Interplay of Binary and Quantitative Structure on the Stability of Mutualistic Networks

Synopsis Understanding how the structure of biological systems impacts their resilience (broadly defined) is a recurring question across multiple levels of biological organization. In ecology, considerable effort has been devoted to understanding how the structure of interactions between species in ecological networks is linked to different broad resilience outcomes, especially local stability. Still, nearly all of that work has focused on interaction structure in presence-absence terms and has not investigated quantitative structure, i.e., the arrangement of interaction strengths in ecological networks. We investigated how the interplay between binary and quantitative structure impacts stability in mutualistic interaction networks (those in which species interactions are mutually beneficial), using community matrix approaches. We additionally examined the effects of network complexity and within-guild competition for context. In terms of structure, we focused on understanding the stability impacts of nestedness, a structure in which more-specialized species interact with smaller subsets of the same species that more-generalized species interact with. Most mutualistic networks in nature display binary nestedness, which is puzzling because both binary and quantitative nestedness are known to be destabilizing on their own. We found that quantitative network structure has important consequences for local stability. In more-complex networks, binary-nested structures were the most stable configurations, depending on the quantitative structures, but which quantitative structure was stabilizing depended on network complexity and competitive context. As complexity increases and in the absence of within-guild competition, the most stable configurations have a nested binary structure with a complementary (i.e., anti-nested) quantitative structure. In the presence of within-guild competition, however, the most stable networks are those with a nested binary structure and a nested quantitative structure. In other words, the impact of interaction overlap on community persistence is dependent on the competitive context. These results help to explain the prevalence of binary-nested structures in nature and underscore the need for future empirical work on quantitative structure.

Zoology↗
DOE OSTI2024

Phylogenetic distribution and experimental characterization of corrinoid production and dependence in soil bacterial isolates

Abstract Soil microbial communities impact carbon sequestration and release, biogeochemical cycling, and agricultural yields. These global effects rely on metabolic interactions that modulate community composition and function. However, the physicochemical and taxonomic complexity of soil and the scarcity of available isolates for phenotypic testing are significant barriers to studying soil microbial interactions. Corrinoids—the vitamin B12 family of cofactors—are critical for microbial metabolism, yet they are synthesized by only a subset of microbiome members. Here, we evaluated corrinoid production and dependence in soil bacteria as a model to investigate the ecological roles of microorganisms involved in metabolic interactions. We isolated and characterized a taxonomically diverse collection of 161 soil bacteria from a single study site. Most corrinoid-dependent bacteria in the collection prefer B12 over other corrinoids, while all tested producers synthesize B12, indicating metabolic compatibility between producers and dependents in the collection. Furthermore, a subset of producers release B12 at levels sufficient to support dependent isolates in laboratory culture at estimated ratios of up to 1000 dependents per producer. Within our isolate collection, we did not find strong phylogenetic patterns in corrinoid production or dependence. Upon investigating trends in the phylogenetic dispersion of corrinoid metabolism categories across sequenced bacteria from various environments, we found that these traits are conserved in 47 out of 85 genera. Together, these phenotypic and genomic results provide evidence for corrinoid-based metabolic interactions among bacteria and provide a framework for the study of nutrient-sharing ecological interactions in microbial communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Regional epithelial cell diversity in the small intestine of pigs

Abstract Understanding regional distribution and specialization of small intestinal epithelial cells is crucial for developing methods to control appetite, stress, and nutrient uptake in swine. To establish a better understanding of specific epithelial cells found across different regions of the small intestine in pigs, we utilized single-cell RNA sequencing (scRNA-seq) to recover and analyze epithelial cells from duodenum, jejunum, and ileum. Cells identified included crypt cells, enterocytes, BEST4 enterocytes, goblet cells, and enteroendocrine (EE) cells. EE cells were divided into two subsets based on the level of expression of the EE lineage commitment gene, NEUROD1. NEUROD1hi EE cells had minimal expression of hormone-encoding genes and were dissimilar to EE cells in humans and mice, indicating a subset of EE cells unique to pigs. Recently discovered BEST4 enterocytes were detected in both crypts and villi throughout the small intestine via in situ staining, unlike in humans, where BEST4 enterocytes are found only in small intestinal villi. Proximal-to-distal gradients of expression were noted for hormone-encoding genes in EE cells and nutrient transport genes in enterocytes via scRNA-seq, demonstrating regional specialization. Regional gene expression in EE cells and enterocytes was validated via quantitative PCR (qPCR) analysis of RNA isolated from epithelial cells of different small intestinal locations. Though many genes had similar patterns of regional expression when assessed by qPCR of total epithelial cells, some regional expression was only detected via scRNA-seq, highlighting advantages of scRNA-seq to deconvolute cell type-specific regional gene expression when compared to analysis of bulk samples. Overall, results provide new information on regional localization and transcriptional profiles of epithelial cells in the pig small intestine.

Agriculture↗
DOE OSTI2020

μ⋆ masses: weak-lensing calibration of the Dark Energy Survey Year 1 redMaPPer clusters using stellar masses

ABSTRACT We present the weak-lensing mass calibration of the stellar-mass-based μ⋆ mass proxy for redMaPPer galaxy clusters in the Dark Energy Survey Year 1. For the first time, we are able to perform a calibration of μ⋆ at high redshifts, z > 0.33. In a blinded analysis, we use ∼6000 clusters split into 12 subsets spanning the ranges 0.1 ≤ z < 0.65 and μ⋆ up to ${\sim} 5.5 \times 10^{13} \, \mathrm{M}_{\odot }$, and infer the average masses of these subsets through modelling of their stacked weak-lensing signal. In our model, we account for the following sources of systematic uncertainty: shear measurement and photometric redshift errors, miscentring, cluster-member contamination of the source sample, deviations from the Navarro–Frenk–White halo profile, halo triaxiality, and projection effects. We use the inferred masses to estimate the joint mass–μ⋆–z scaling relation given by $\langle M_{200c} | \mu _{\star },z \rangle = M_0 (\mu _{\star }/5.16\times 10^{12} \, \mathrm{M_{\odot }})^{F_{\mu _{\star }}} ((1+z)/1.35)^{G_z}$. We find $M_0= (1.14 \pm 0.07) \times 10^{14} \, \mathrm{M_{\odot }}$ with $F_{\mu _{\star }}= 0.76 \pm 0.06$ and Gz = −1.14 ± 0.37. We discuss the use of μ⋆ as a complementary mass proxy to the well-studied richness λ for: (i) exploring the regimes of low z, λ < 20 and high λ, z ∼ 1; and (ii) testing systematics such as projection effects for applications in cluster cosmology.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Determining the range of validity of quasar X-ray and UV flux measurements for constraining cosmological model parameters

ABSTRACT We use six different cosmological models to study the recently released compilation of X-ray and UV flux measurements of 2038 quasars (QSOs) which span the redshift range 0.009 ≤ z ≤ 7.5413. We find, for the full QSO data set, that the parameters of the X-ray and UV luminosities LX−LUV relation used to standardize these QSOs depend on the cosmological model used to determine these parameters, i.e. it appears that the full QSO data set includes QSOs that are not standardized and so cannot be used for the purpose of constraining cosmological parameters. Subsets of the QSO data, restricted to redshifts z ≲ 1.5–1.7 obey the LX−LUV relation in a cosmological-model-independent manner, and so can be used to constrain cosmological parameters. The cosmological constraints from these lower z, smaller QSO data subsets are mostly consistent with, but significantly weaker than, those that follow from baryon acoustic oscillation and Hubble parameter measurements.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Consistency study of high- and low-accreting Mg ii quasars: no significant effect of the Fe ii to Mg ii flux ratio on the radius–luminosity relation dispersion

ABSTRACT We use observations of 66 reverberation-measured Mg ii quasars (QSOs) in the redshift range 0.36 ≤ z ≤ 1.686 – a subset of the 78 QSOs we previously studied that also have ${\cal R}_{\rm {Fe\, {\small II}}}$ (flux ratio parameter of UV Fe ii to Mg ii that is used as an accretion-rate proxy) measurements – to simultaneously constrain cosmological model parameters and QSO two-parameter and three-parameter radius–luminosity (R–L) relation parameters in six different cosmological models. We find that these QSO R–L relation parameters are independent of the assumed cosmological model and so these QSOs are standardizable through the R–L relations. Also: (1) With the two-parameter R–L relation, we find that the low-${\cal R}_{\rm {Fe\, {\small II}}}$ and high-${\cal R}_{\rm {Fe\, {\small II}}}$ data subsets obey the same R–L relation within the error bars. (2) Extending the two-parameter R–L relation to a three-parameter one does not result in the hoped-for reduction in the intrinsic dispersion of the R–L relation. (3) Neither of the three-parameter R–L relations provide a significantly better fit to the measurements than does the two-parameter R–L relation. These are promising results for the ongoing development of Mg ii cosmological probes. The first and third of these results differ significantly from those we found elsewhere from analyses of reverberation-measured H β QSOs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Quantum simulation of nuclear inelastic scattering

We present a time-dependent quantum algorithm for nuclear inelastic scattering in the time-dependent basis function on qubits approach. This algorithm aims to quantum simulate a subset of the nuclear inelastic scattering problems that are of physical interest in which the internal degrees of freedom of the reaction system are excited by time-dependent external interactions. We expect that our algorithm will enable an exponential speedup in simulating the dynamics of the subset of the inelastic scattering problems, which would also be advantageous for the applications to more complicated scattering problems. For a demonstration problem, we solve for the Coulomb excitation of the deuteron, where the quantum simulations are performed with IBM Qiskit.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Entropic lens on stabilizer states

The n-qubit stabilizer states are those left invariant by a 2 n -element subset of the Pauli group. The Clifford group is the group of unitaries which take stabilizer states to stabilizer states; a physically motivated generating set, the Hadamard, phase, and controlled-not (cnot) gates which comprise the Clifford gates, impose a graph structure on the set of stabilizers. We explicitly construct these structures, the “reachability graphs,” at n ≤ 5. When we consider only a subset of the Clifford gates, the reachability graphs separate into multiple, often complicated, connected components. Seeking an understanding of the entropic structure of the stabilizer states, which is ultimately built up by cnot gate applications on two qubits, we are motivated to consider the restricted subgraphs built from the Hadamard and cnot gates acting on only two of the n qubits. We show how the two subgraphs already present at two qubits are embedded into more complicated subgraphs at three and four qubits. We argue that no additional types of subgraph appear beyond four qubits, but that the entropic structures within the subgraphs can grow progressively more complicated as the qubit number increases. Starting at four qubits, some of the stabilizer states have entropy vectors which are not allowed by holographic entropy inequalities. Here, we comment on the nature of the transition between holographic and nonholographic states within the stabilizer reachability graphs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Fast ground-state-to-ground-state separation of small ion crystals

Rapid separation of linear crystals of trapped ions into different subsets is critical for realizing trapped ion quantum computing architectures where ions are rearranged in trap arrays to achieve all-to-all connectivity between qubits. Here we introduce a general theoretical framework that can be used to describe the separation of same-species and mixed-species crystals into smaller subsets. The framework relies on an efficient description of the evolution of Gaussian motional states under quadratic Hamiltonians that only requires a special solution of the classical equations of motion of the ions to describe their quantum evolution under the influence of a time-dependent applied potential and the ions' mutual Coulomb repulsion. We provide time-dependent applied potentials suitable for separation of a mixed-species three-ion crystal on timescales similar to that of free expansion driven by Coulomb repulsion, with all modes along the crystal axis starting and ending close to their ground states. Three separately confined mixed-species ions can be combined into a crystal held in a single well without energy gain by time-reversal of this separation process.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗