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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Particlization in fluid dynamical simulations of heavy-ion collisions: The IS3D module

The IS3D particlization module simulates the emission of hadrons from heavy-ion collisions via Monte-Carlo sampling of the Cooper–Frye formula which converts fluid dynamical information into local phase-space distributions for hadrons. The code package includes multiple choices for the non-equilibrium correction to these distribution functions: the 14-moment approximation, first-order Chapman–Enskog expansion, and two types of modified equilibrium distributions. This makes it possible to explore to what extent heavy-ion experimental data are sensitive to different choices for δf n , presently the main source of theoretical uncertainty in the particlization stage. Here, we validate our particle sampler with a high degree of precision by generating several million hadron emission events from a longitudinally boost-invariant hypersurface and comparing the event-averaged particle spectra and space–time distributions to the Cooper–Frye formula.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Iron-iminopyridine complexes as charge carriers for non-aqueous redox flow battery applications

Non-aqueous redox flow batteries (RFBs) have been gaining increased attention in the energy storage arena. Some of their attractive features include the promise for high energy densities, wider voltage windows compared to aqueous systems, as well as wider operating temperature ranges. One of the major challenges in the development of these systems is the lack of electroactive materials that can undergo reversible redox events within the larger potential window of organic solvents. In this paper, we present the design, synthesis, and measurement of electrochemical properties of some iron-based imine- and iminopyridine complexes as promising candidates for RFB applications. Synthesized complexes afforded three reversible redox couples over a ~3 V range. The redox events were also computationally explored and are in good agreement with experimental data. Theoretical calculations show that the redox event at the positive potential can be characterized as metal-centered event corresponding to Fe 3+ → Fe 2+ reduction, while the two redox events at negative potentials are associated with the consecutive reductions of iminopyridine or diimine moieties. This work has led to the identification of a promising anolyte material, [tris(imino)pyridine)Fe][OTf] 2 (1), which is soluble in acetonitrile and is synthesized in two simple steps. This species shows outstanding performance when cycled between 1.0 V and 2.35 V, with 93% coulombic efficiency, 84% energy efficiency, and a capacity decay rate of 0.61% per cycle at 5 mA cm –2 . Further modifications to this kind of charge carrier may lead to the development of high energy density materials for grid scale energy storage applications.

25 ENERGY STORAGE↗
DOE OSTI2022

Cosmological constraints on dark matter interactions with ordinary matter

Dark matter interactions with electrons or protons during the early Universe leave imprints on the cosmic microwave background and the matter power spectrum, and can be probed through cosmological and astrophysical observations. These interactions lead to momentum and heat exchange between the ordinary and dark matter components, which in turn results in a transfer of pressure from the ordinary to the dark matter. Here, we explore these interactions using a diverse suite of data: cosmic microwave background anisotropies, baryon acoustic oscillations, the Lyman-α forest, and the abundance of Milky-Way subhalos. We derive constraints using model-independent parameterizations of the dark matter–electron and dark matter–proton interaction cross sections and map these constraints onto concrete dark matter models. Our constraints are complementary to other probes of dark matter interactions with ordinary matter, such as direct detection, big bang nucleosynthesis, various astrophysical systems, and accelerator-based experiments. They exclude sufficiently large cross sections for a large range of dark matter masses, which cannot be accessed by direct-detection experiments due to the overburden from the Earth’s atmosphere or crust.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Rapid Identification of Synthetic Routes to Functional Metastable Phases Using X-ray Probed Laser Anneal Mapping (XPLAM) Time-Temperature Quench Maps

Many material systems have known or predicted functional phases that are metastable at standard temperature and pressure. While substantial advances have been made in the high-throughput and combinatorial synthesis of materials with a range of stoichiometries, investigation of thermal processing remains largely the domain of iterative uniform anneals or static gradients. Here we develop X-ray probed laser anneal mapping (XPLAM), a high throughput technique coupling spatially resolved X-ray diffraction with microsecond to millisecond laser gradient anneals to produce temperature–dwell–transformation (TDT) diagrams of the phase as a function of quench time and temperature. In addition to showing regimes where specific metastable phases form preferentially, TDT diagrams provide insight into the submillisecond kinetics of solid–solid phase transitions. This is a unique tool for mapping reaction pathways for metastable phases. As a first demonstration of XPLAM, we study Bi2O3, which has a rich set of polytypes, including the d-phase with an exceptionally high oxygen ion conductivity. We demonstrate the first annealing-driven synthesis of room temperature d-Bi2O3. We expect XPLAM to prove a powerful technique for rapid identification of synthetic routes to metastable phases and to generate the exhaustive data sets required for machine learning-guided exploration of materials processing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Elucidating the Roles of Local and Nonlocal Rate Enhancement Mechanisms in Plasmonic Catalysis

Plasmonic metal nanoparticles (e.g., Ag, Au, and Cu) constitute a class of materials that interact with light via the excitation of localized surface plasmon resonance (LSPR). Numerous studies have reported substantial enhancements in the rates of chemical reactions on illuminated plasmonic nanoparticle catalysts compared to corresponding systems in the absence of illumination. There are two mechanisms that have been proposed to explain the LSPR-induced chemical reactivity. One mechanism assumes a local plasmon-induced hot charge-carrier-mediated activation of the reactants, while the other assumes an LSPR-induced equilibrium heating of the catalyst, which leads to energy transfer to and chemical reaction of the adsorbed reactants. Here, in this contribution, we developed a setup amenable to accurate in situ catalyst temperature and kinetic reaction rate measurements. We employed this setup to study the LSPR-induced rate enhancement in a case study of the CO oxidation reaction on plasmonic, monometallic Ag nanoparticle catalysts supported on α-Al 2 O 3 . We explored various Ag loadings and clustering levels. Our data show that the equilibrium heating of the catalyst cannot fully explain the illumination-induced plasmonic rate enhancements. This is the case even for high loading and clustering of Ag nanoparticles, where the equilibrium heating significantly increases. Based on the analysis, we propose that local effects, related to the plasmon-induced activation of adsorbates (reactants) via electronic excitation of the reactant or photothermal heating of the reactants that is highly localized to the individual nanoparticles, play a critical role in driving LSPR-induced chemical reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Bayesian Model for HOPE Mass Spectrometers on Van Allen Probes

Space instruments rely heavily on modeling to predict and understand the instrument response, enabling a determination of the capabilities and resolution. The Bayesian approach provides a framework to incorporate prior knowledge and propagate uncertainty to predict the instrument response. We present an empirical Bayes model for the end-to-end performance of the Helium, Oxygen, Proton, and Electron mass spectrometers aboard the Van Allen Probes mission. In this model, we use a combination of external modeling, laboratory calibration, and expert opinion to construct the time-of-flight spectra and demonstrate good agreement with on-orbit data. The empirical Bayes model is applied to explore doubly charged ions and carbon, nitrogen, oxygen discrimination during the Van Allen Probes mission.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

The global spectrum of tree crown architecture

Abstract Trees can differ enormously in their crown architectural traits, such as the scaling relationships between tree height, crown width and stem diameter. Yet despite the importance of crown architecture in shaping the structure and function of terrestrial ecosystems, we lack a complete picture of what drives this incredible diversity in crown shapes. Using data from 374,888 globally distributed trees, we explore how climate, disturbance, competition, functional traits, and evolutionary history constrain the height and crown width scaling relationships of 1914 tree species. We find that variation in height–diameter scaling relationships is primarily controlled by water availability and light competition. Conversely, crown width is predominantly shaped by exposure to wind and fire, while also covarying with functional traits related to mechanical stability and photosynthesis. Additionally, we identify several plant lineages with highly distinctive stem and crown forms, such as the exceedingly slender dipterocarps of Southeast Asia, or the extremely wide crowns of legume trees in African savannas. Our study charts the global spectrum of tree crown architecture and pinpoints the processes that shape the 3D structure of woody ecosystems.

Science & Technology - Other Topics↗
DOE OSTI2024

Detecting defects that reduce breakdown voltage using machine learning and optical profilometry

Abstract Semiconductor wafer manufacturing relies on the precise control of various performance metrics to ensure the quality and reliability of integrated circuits. In particular, GaN has properties that are advantageous for high voltage and high frequency power devices; however, defects in the substrate growth and manufacturing are preventing vertical devices from performing optimally. This paper explores the application of machine learning techniques utilizing data obtained from optical profilometry as input variables to predict the probability of a wafer meeting performance metrics, specifically the breakdown voltage (V bk ). By incorporating machine learning techniques, it is possible to reliably predict performance metrics that cause devices to fail at low voltage. For diodes that fail at a higher (but still below theoretical) breakdown voltage, alternative inspection methods or a combination of several experimental techniques may be necessary.

42 ENGINEERING↗
DOE OSTI2024

Insights into the broadband emission of the TeV blazar Mrk 501 during the first X-ray polarization measurements

Aims.We present the first multiwavelength study of Mrk 501 that contains simultaneous very-high-energy (VHE)γ-ray observations and X-ray polarization measurements from the Imaging X-ray Polarimetry Explorer (IXPE). Methods.We used radio-to-VHE data from a multiwavelength campaign carried out between March 1, 2022, and July 19, 2022 (MJD 59639 to MJD 59779). The observations were performed by MAGIC,Fermi-LAT,NuSTAR,Swift(XRT and UVOT), and several other instruments that cover the optical and radio bands to complement the IXPE pointings. We characterized the dynamics of the broadband emission around the X-ray polarization measurements through its multiband fractional variability and correlations, and compared changes observed in the polarization degree to changes seen in the broadband emission using a multi-zone leptonic scenario. Results.During the IXPE pointings, the VHE state is close to the average behavior, with a 0.2–1 TeV flux of 20%–50% of the emission of the Crab Nebula. Additionally, it shows low variability and a hint of correlation between VHEγ-rays and X-rays. Despite the average VHE activity, an extreme X-ray behavior is measured for the first two IXPE pointings, taken in March 2022 (MJD 59646 to 59648 and MJD 59665 to 59667), with a synchrotron peak frequency > 1 keV. For the third IXPE pointing, in July 2022 (MJD 59769 to 59772), the synchrotron peak shifts toward lower energies and the optical/X-ray polarization degrees drop. All three IXPE epochs show an atypically low Compton dominance in theγ-rays. The X-ray polarization is systematically higher than at lower energies, suggesting an energy stratification of the jet. While during the IXPE epochs the polarization angles in the X-ray, optical, and radio bands align well, we find a clear discrepancy in the optical and radio polarization angles in the middle of the campaign. Such results further support the hypothesis of an energy-stratified jet. We modeled broadband spectra taken simultaneous to the IXPE pointings, assuming a compact zone that dominates in the X-rays and the VHE band, and an extended zone stretching farther downstream in the jet that dominates the emission at lower energies.NuSTARdata allow us to precisely constrain the synchrotron peak and therefore the underlying electron distribution. The change between the different states observed in the three IXPE pointings can be explained by a change in the magnetization and/or the emission region size, which directly connects the shift in the synchrotron peak to lower energies with the drop in the polarization degree.

Astronomy & Astrophysics↗
DOE OSTI2020

Brighter-fatter Effect in Near-infrared Detectors—III. Fourier-domain Treatment of Flat Field Correlations and Application to WFIRST

Weak gravitational lensing has emerged as a leading probe of the growth of cosmic structure. However, the shear signal is very small and accurate measurement depends critically on our ability to understand how non-ideal instrumental effects affect astronomical images. The Wide-Field Infrared Survey Telescope (WFIRST) will fly a focal plane containing 18 Teledyne H4RG-10 near-infrared detector arrays, which present different instrument calibration challenges from previous weak lensing observations. Previous work [Paper I: Hirata & Choi, PASP, 132, 014501 (2020); and Paper II: Choi & Hirata, PASP, 132, 014502 (2020)] has shown that correlation functions of flat field images, including cross-correlations between different time slices that are enabled by the non-destructive read capability of the infrared detectors, are effective tools for disentangling linear and nonlinear inter-pixel capacitance (IPC) and the brighter-fatter effect (BFE). Here we present a Fourier-domain treatment of the flat field correlations, which allows us to expand the previous formalism to all orders in IPC, BFE, and classical nonlinearity. We show that biases in simulated flat field analyses in Paper I are greatly reduced through the use of this formalism. We then apply this updated formalism to flat field data from three WFIRST flight candidate detectors, and explore the robustness to variations in the analysis. We find that the BFE is present in all three detectors, and that its contribution to the flat field correlations dominates over the nonlinear IPC, in accordance with the results from Paper II on a development detector. The magnitude of the BFE is such that the effective area of a pixel is increased by (3.54 ± 0.03) × 10 -7 for every electron deposited in a neighboring pixel (sensor chip assembly [SCA] 20829, statistical error, not IPC-deconvolved). We compare IPC maps from flat field autocorrelation measurements to those obtained from the single pixel reset method and find a median difference of 0.113% for SCA 20829. After further diagnosis of this difference, we ascribe it largely to an additional source of cross-talk, the vertical trailing pixel effect, and recommend further work to develop a model for this effect. These results represent a significant step toward calibration of the non-ideal effects in WFIRST detectors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Taxogenomic analysis of Pichia senei sp. nov. and new insights into hybridization events in the Pichia cactophila species complex

Three strains of a novel yeast species were isolated from necrotic cactus tissues of Cereus saddianus and Micranthocereus dolichospermaticus and from phytotelmata of Bromelia karatas. DNA sequence analysis of the Internal Transcribed Spacer (ITS) region and D1/D2 domains of the large subunit ribosomal RNA, along with whole genome phylogenomic analysis, showed that this yeast is most closely related to Pichia insulana, Pichia cactophila, and Pichia inconspicua. The new species differs by 10–13 nucleotide substitutions from these species in D1/D2 sequences and exhibits <90% genome-wide average nucleotide identity to them. The name Pichia senei sp. nov. is proposed for the novel species, which is homothallic and produces asci with one to four hat-shaped ascospores. The holotype is CBS 16311 (MycoBank MB 858723). Taxogenomic analyses of the P. cactophila species complex, including P. senei, provide new insights about the hybridizations events that shaped this group. Pichia insulana and P. inconspicua are identified as the parental lineages that originated P. cactophila, and P. senei also appears closely related to one of the progenitors of P. inconspicua. We assess phylogeny, heterozygosity, and ploidy to explore the processes shaping diversity, showing how genomic data support yeast species delimitation and reveal complex hybridization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A spatially resolved spectral analysis of giant radio galaxies with MeerKAT

ABSTRACT In this study we report the spatially resolved, wideband spectral properties of three giant radio galaxies (GRGs) in the COSMOS field: MGTC J095959.63+024608.6, MGTC J100016.84+015133.0, and MGTC J100022.85+031520.4. One of these galaxies, MGTC J100022.85+031520.4, is reported here for the first time, with a projected linear size of 1.29 Mpc at a redshift of 0.1034. Unlike the other two, it is associated with a brightest cluster galaxy (BCG), making it one of the few GRGs known to inhabit cluster environments. We examine the spectral age distributions of the three GRGs using new MeerKAT UHF-band (544–1088 MHz) observations, and L-band (900–1670 MHz) data from the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) survey. We test two models of spectral ageing, the Jaffe–Perola and Tribble models, using the Broadband Radio Astronomy Tools (brats) software, and find that they agree well with each other. We estimate the Tribble spectral age for MGTC J095959.63+024608.6 as 68 Myr, for MGTC J100016.84+015133.0 as 47 Myr, and for MGTC J100022.85+031520.4 as 67 Myr. We find significant disagreements between these spectral age estimates and the estimates of the dynamical ages of these GRGs, modelled in cluster and group environments. Our results highlight the need for additional processes that are not accounted for in either the dynamic age or the spectral age estimations.

Charlton, K. K. L. (ORCID:0000000229252047)↗
DOE OSTI2021

Speed of Sound and Baryon Cumulants in Heavy-Ion Collisions

We present a method that may allow an estimate of the value of the speed of sound as well as its logarithmic derivative with respect to the baryon number density in matter created in heavy-ion collisions. To this end, we utilize well-known observables: cumulants of the baryon number distribution. In analyses aimed at uncovering the phase diagram of strongly interacting matter, cumulants gather considerable attention as their qualitative behaviour along the explored range of collision energies is expected to aid in detecting the QCD critical point. We show that the cumulants may also reveal the behavior of the speed of sound in the temperature and baryon chemical potential plane. We demonstrate the applicability of such estimates within two models of nuclear matter, and explore what might be understood from known experimental data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Quapprox: A Framework for Benchmarking the Approximability of Variational Quantum Circuit

Most of the existing quantum neural network models, such as variational quantum circuits (VQCs), are limited in their ability to explore the non-linear relationships in input data. This gradually becomes the main obstacle for it to tackle realistic applications, such as natural language processing, medical image processing, and wireless communications. Recently, there have emerged research efforts that enable VQCs to perform non-linear operations. However, it is still unclear on the approximability of a given VQC (i.e., the order of non-linearity that can be handled by a specified design). In response to this issue, we developed an automated tool designed to benchmark the approximation of a given VQC. The proposed tool will generate a set of synthetic datasets with different orders of non-linearity and train the given VQC on these datasets to estimate their approximability. Our experiments benchmark VQCs with different designs, where we know their theoretic approximability. We then show that the proposed tool can precisely estimate the approximability, which is consistent with the theoretic value, indicating that the proposed tool can be used for benchmarking the approximability of a given quantum circuit for learning tasks.

artificial intelligence↗
DOE OSTI2020

A Critical Exploration of the Efficiency Impacts of Demand Response From HVAC in Commercial Buildings

Increasing quantities of renewable energy generation has yielded a need for greater energy storage capacity in power systems. Thermal storage in variable air volume (VAV) heating, ventilation, and air conditioning (HVAC) in commercial buildings has been identified as an inexpensive source of grid storage, but the true costs are not known. Recent literature explores the inefficiency associated with providing grid services from these HVAC-based demand response (DR) resources by employing a battery analogy to calculate round-trip efficiency (RTE). Results vary significantly across studies and in some cases reported efficiencies are strikingly low. This article has three objectives to address these prior results. First, we synthesize and expand on insights into existing literature by systematically exploring the potential causes for the discrepancies in results. We reinforce previous work indicating baseline modeling may drive differences across studies and deduce that control accuracy plays a role in the major differences between experiments and simulation. Second, we discuss why the RTE metric is problematic for DR applications, discuss another proposed metric, additional energy consumption (AEC), and propose an extension, which we call uninstructed energy consumption (UEC), to evaluate DR performance. Finally, we explore the merits of different metrics using experimental data and highlight UEC's reduced sensitivity to the characteristics of the DR signal than previously proposed metrics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Decision-Making Under Uncertainty for a Digital Thread-Enabled Design Process

Abstract Digital thread is a data-driven architecture that links together information from all stages of the product lifecycle. Despite increasing application in manufacturing, maintenance/operations, and design related tasks, a principled formulation of analyzing the decision-making problem under uncertainty for the digital thread remains absent. The contribution of this article is to present a formulation using Bayesian statistics and decision theory. First, we address how uncertainty propagates in the product lifecycle and how the digital thread evolves based on the decisions we make and the data we collect. By using these mechanics, we explore designing over multiple product generations or iterations and provide an algorithm to solve the underlying multistage decision problem. We illustrate our method on an example structural design problem where our method can quantify and optimize different types and sequences of decisions, ranging from experimentation, manufacturing, and sensor placement/selection, to minimize total accrued costs.

Engineering↗
DOE OSTI2021

Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning

Abstract One of the critical components in Industrial Gas Turbines (IGT) is the turbine blade. Design of turbine blades needs to consider multiple aspects like aerodynamic efficiency, durability, safety and manufacturing, which make the design process sequential and iterative. The sequential nature of these iterations forces a long design cycle time, ranging from several months to years. Due to the reactionary nature of these iterations, little effort has been made to accumulate data in a manner that allows for deep exploration and understanding of the total design space. This is exemplified in the process of designing the individual components of the IGT resulting in a potential unrealized efficiency. To overcome the aforementioned challenges, we demonstrate a probabilistic inverse design machine learning framework, namely PMI (PMI), to carry out an explicit inverse design. PMI calculates the design explicitly without costly iteration and overcomes the challenges associated with ill-posed inverse problems. In this work the framework will be demonstrated on inverse aerodynamic design of three-dimensional turbine blades.

Engineering↗