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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A New Constraint on the Nuclear Equation of State from Statistical Distributions of Compact Remnants of Supernovae

Abstract Understanding how matter behaves at the highest densities and temperatures is a major open problem in both nuclear physics and relativistic astrophysics. Our understanding of such behavior is often encapsulated in the so-called high-temperature nuclear equation of state (EOS), which influences compact binary mergers, core-collapse supernovae, and other phenomena. Our focus is on the type (either black hole or neutron star) and mass of the remnant of the core collapse of a massive star. For each six candidates of equations of state, we use a very large suite of spherically symmetric supernova models to generate a sample of synthetic populations of such remnants. We then compare these synthetic populations to the observed remnant population. Our study provides a novel constraint on the high-temperature nuclear EOS and describes which EOS candidates are more or less favored by an information-theoretic metric.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Theoretical Correlation of Elemental Distribution of Nd and Pr in Ce-Fe-B Microstructure With Hard Magnetic Properties

Relatively resource-rich but property inferior-Ce-Fe-B magnet can be improved by partial replacement of Ce by Nd and/or Pr. In addition to the amount of Nd/Pr, their distribution profile in microstructure plays an important role. From our first principles density functional theory (DFT) calculation, the substitution energy of Ce by Pr/Nd is negative in Ce 2 Fe 14 B (2:14:1) while that for laves phase, CeFe 2 is positive, implying that Nd/Pr stabilize 2:14:1 and suppress the formation of the CeFe 2 phase. Further, micromagnetic simulation indicates that homogenized distribution of Nd/Pr improves squareness of demagnetization curve, while core (Ce-rich)-shell (Nd/Pr-rich) 2:14:1 grain structure enhances coercivity. Magnetic properties of Ce-Fe-B can be optimized by manipulating distribution profile of chemical element in microstructure based on their subtle difference in thermodynamic property, which is an effective pathway to design optimized chemical composition and processing route for high-performance magnet.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Powder spreading, densification, and part deformation in binder jetting additive manufacturing

Binder jetting additive manufacturing (AM) can print complex structures in economical and scalable manner. Binder jetting AM comprises of deposition and weak binding of particles, known as green part, at room temperature and subsequent binder removal and sintering densification at high temperatures. However, during the densification (i.e., sintering), the part significantly deforms due to volume shrinkage. The deformation during sintering is difficult to predict, which prevents the widespread application of this technology. In this research, powder spreading simulation using discrete element method (DEM) was performed first to capture local variations in powder bed configuration. Second, finite element method (FEM) with a phenomenological continuum constitutive model was used to predict part shrinkage during the sintering process. DEM simulation showed variations in packing density, particle segregation, formation of uneven powder bed surface, and shift in particle size distribution (PSD). The sintering simulation modeled part deformation with a reasonable accuracy of 3% for solid-state sintering and intermediate liquid phase sintering. A demonstration case with non-uniform initial packing density showed that inhomogeneous green part density and PSD should be accounted for prediction of part deformation in binder jetting AM.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Quantifying the Influence of Size, Shape, and Density of Microplastics on Their Transport Modes: A Modeling Approach

Microplastics (MPs) pose significant risks to marine ecosystems and human health, necessitating accurate predictions of their distributions in aquatic environments for effective risk mitigation. However, understanding MP transport dynamics is challenging because of the inadequate representation of MP characteristics such as size, shape, and density in numerical models. Further, the accuracy of the MP vertical profiles in existing models has not been thoroughly validated. Thus, we developed an MP transport model within the Finite Volume Community Ocean Model framework (FVCOM-MP) by integrating MP characteristics. We validated FVCOM-MP against experimental and analytical data, focusing on various MP transport modes and transitions. FVCOM-MP successfully replicates MP profiles in different transport modes, including the bedload, surface-load, suspended-load, and mixed-load modes. Additionally, we introduce phase diagrams for classifying MP transport modes based on particle characteristics, enhancing our understanding of MP dynamics in aquatic systems. The transport modes for a number of real-world MP particles, including fishing line, plastic bag/bottle fragments, synthetic fibers, tire wear particles, polyvinyl chloride and expanded polystyrene pellets, were analyzed with these phase diagrams.

Microplastic transport, Settling velocity, Rising ↗
DOE OSTI2021

Challenges and future perspectives on sodium and potassium ion batteries for grid-scale energy storage

The energy crisis and environmental pollution require the advancement of large-scale energy storage techniques. Among the various commercialized technologies, batteries have attracted enormous attention due to their relatively high energy density and long cycle life. Nevertheless, the limited supply and uneven distribution of lithium minerals, as well as their high cost, has greatly hindered the application of lithium-ion batteries in large-scale energy storage. Therefore, building next-generation alternative rechargeable batteries that feature low cost, long service life, and high safety is of the utmost importance. Over the past several years, we have witnessed many successful research outcomes for sodium and potassium ion batteries (SIBs and PIBs) in regard to electrode and electrolyte materials. In this Perspective, we summarize the current developments on SIBs/PIBs and their challenges when facing practical applications, including their cost, energy density, ion diffusivity in solids/electrolytes/interphases, cycle life, and safety concerns. Furthermore, we provide an overview of strategies to overcome the remaining challenges for achieving better electrochemical performance. Finally, we outline several possible directions for the future development of these two battery chemistries, with the hope of aiding the transition from the laboratory to next-generation practical applications for SIBs/PIBs batteries in grid-scale energy storage.

Energy Storage↗
DOE OSTI2022

Generalized parton distributions through universal moment parameterization: zero skewness case

We present a global analysis program for the generalized parton distributions (GPDs) based on conformal moment expansion. We apply the strategy of universal moment parameterization to fit both the collinear parton distribution functions (PDFs) from phenomenology and generalized form factors from lattice calculations, and show that the parameterization is flexible enough to accommodate these constraints. In addition, we can also fit direct lattice calculations of GPDs from large-momentum effective theory. In this work we focus on the analysis of t-dependent PDFs which correspond to GPDs in the ξ → 0 limit. The strategy also applies to the ξ ≠ 0 region with extra parameters, and therefore can be fitted to experimental observables in the future. With a demonstrative example of fitted GPDs, we exhibit the quark transverse angular momentum densities of the proton as well as the impact parameter space distributions of quarks in both unpolarized and transversely polarized protons.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Tailoring catalyst layer structures for anion exchange membrane fuel cells by controlling the size of ionomer aggreates in dispersion

The development of anion exchange membranes and ionomers plays a critical role in performance of anion exchange membrane fuel cells. However, the optimal design of catalyst layer structures still remains unexplored despite its significance. Herein, the rational design guide is presented for cathode and anode catalyst layers using m-TPN1 ionomer by controlling the size of ionomer aggregates in dispersion and comparing various catalyst layer structures. The size of m-TPN1 aggregates decreases, creating a more uniform ionomer distribution and a larger triple-phase-boundary. As ionomer distribution of the cathode catalyst layer becomes more uniform, the power density of membrane-electrode-assembly is enhanced. Also, the power density at fully humidified condition strongly correlates with the porosity of anode catalyst layer due to the limitation of hydrogen transport at anode by water flooding.

42 ENGINEERING↗
DOE OSTI2020

Characterizing Peatland Microtopography Using Gradient and Microform-Based Approaches

Abstract Peatlands represent an important component of the global carbon cycle, storing 180–621 Gt of carbon (C). Small-scale spatial variations in elevation, frequently referred to as microtopography, influence ecological processes associated with the peatland C cycle, including Sphagnum photosynthesis and methane flux. Microtopography can be characterized with measures of topographic variability and by using conceptual classes (microforms) linked to function: most commonly hummocks and hollows. However, the criteria used to define these conceptual classes are often poorly described, if at all, and vary between studies. Such inconsistencies compel development of explicit quantitative methods to classify microforms. Furthermore, gradient-based characterizations that describe spatial variability without the use of microforms are lacking in the literature. Therefore, the objectives of this study were to (1) calculate peatland microtopographical elevation gradients and measures of spatial variability, (2) develop three microform classification methods intended for specific purposes, and (3) evaluate and contrast classification methods. Our results suggest that at spatial scales much larger than microforms, elevation distributions are unimodal and are well approximated with parametric probability density functions. Results from classifications were variable between methods and years and exhibited significant differences in mean hollow areal coverages of a raised ombrotrophic bog. Our results suggest that the conceptualization and classification of microforms can significantly influence microtopographic structural metrics. The three explicit methods for microform classification described here may be used and built upon for future applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Selective single-atom adsorption for precision separation of lead ions in tap water via capacitive deionization

Capacitive deionization (CDI) offers a cost-effective and low-energy method for selective removal of Pb 2+ from drinking water. Modifying CDI electrode surfaces with functional groups presents a versatile approach to enhancing selective ion adsorption capacity. However, a comprehensive understanding of the selectivity and removal efficiency of Pb 2+ among diverse functional groups remains unexplored. Here, we investigated the effects of different functional groups (-SH, -COOH, and -NH 2 ) attached to the graphene oxide (GO) electrode surfaces on Pb 2+ selectivity and removal efficiency. Surprisingly, GO-COOH demonstrated single-atom adsorption of Pb 2+ , displaying superior removal efficiency and selectivity compared with -SH and -NH 2 , although -SH possesses significant chelation capability for Pb 2+ . Both density functional theory (DFT) calculations and X-ray pair distribution function (PDF) analyses confirmed that Pb 2+ exhibits a theoretically higher affinity to -COOH. Further, this research deepens our understanding of the interactions between functional groups and heavy metal ions, enabling selective and rapid separation of target cations for water purification.

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

Charge-Density-Specific Response of Grafted Polyelectrolytes to Electric Fields: Bending or Tilting?

The response of polyelectrolytes (PEs) to applied electric fields drive applications in energetics, diagnostics, materials development, and many more. Here we employ all-atom molecular dynamics (MD) simulations to probe the response of grafted PE brushes to axial electric fields. For PEs with large charge densities, the electric field triggers a left-right asymmetric distribution of counterions around the PE backbone: consequently, depending on the location (left or right), there is an unequal screening and an unequal force on the PE functional groups causing a bending-driven brush height reduction. However, for the weakly charged PEs, the electric field causes a uniform distribution of the counterions across the brush enforcing a uniform (without left-right asymmetry) partially unscreened PE brush: therefore, all the brush segments experience similar force causing a brush tilting-driven brush height reduction. As a result, such bending versus tilting responses is commensurate with the electric-field-driven increase (decrease) in the flexibility of strongly (weakly) charged PEs.

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

Decoding defect statistics from diffractograms via machine learning

Abstract Diffraction techniques can powerfully and nondestructively probe materials while maintaining high resolution in both space and time. Unfortunately, these characterizations have been limited and sometimes even erroneous due to the difficulty of decoding the desired material information from features of the diffractograms. Currently, these features are identified non-comprehensively via human intuition, so the resulting models can only predict a subset of the available structural information. In the present work we show (i) how to compute machine-identified features that fully summarize a diffractogram and (ii) how to employ machine learning to reliably connect these features to an expanded set of structural statistics. To exemplify this framework, we assessed virtual electron diffractograms generated from atomistic simulations of irradiated copper. When based on machine-identified features rather than human-identified features, our machine-learning model not only predicted one-point statistics (i.e. density) but also a two-point statistic (i.e. spatial distribution) of the defect population. Hence, this work demonstrates that machine-learning models that input machine-identified features significantly advance the state of the art for accurately and robustly decoding diffractograms.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Unusual electronic transport in (1 - x )Cu 2 Se-( x )CuInSe 2 hierarchical composites

The ability to control the relative density of electronic point defects as well as their energy distribution in semiconductors could afford a systematic modulation of their electronic, optical, and optoelectronic properties. Using a model binary hybrid system Cu 2 Se–CuInSe 2 , we have investigated the correlation between phase composition, microstructure, and electronic transport behavior in the synthesized composites. We found that both Cu 2 Se and CuInSe 2 phases coexist at multiple length scales, ranging from sub-ten nanometer to several micrometers, leading to the formation of a hybrid hierarchical microstructure. Astonishingly, the electronic phase diagram of the (1 - x)Cu 2 Se–(x)CuInSe 2 (15% ≤ x ≤ 100%) hierarchical composites remarkably deviates from the trend normally expected for composites between a heavily doped semiconductor (Cu 2 Se) and a poorly conducting phase (CuInSe 2 ). A sudden 3-fold increase in the electrical conductivity and carrier concentration along with a marginal increase in the carrier mobility is observed for composites at the vicinity of equimolar composition (48% ≤ x ≤ 52%). The carrier concentration increases from ~1.5 × 10 20 cm -3 for the composites with x ≤ 45% to 5.0 × 10 20 cm -3 for x = 50%, and remains constant at 4.5 × 10 20 cm -3 with x value in the range of 52% < x ≤ 90%, then quickly drops to 8 × 10 18 cm -3 for pristine CuInSe 2 phase (x = 100%). The atypical electronic behavior was rationalized in the light of the formation of an inter-band (IB) within the band gap, which arises from the hybridization of native electronic point defects from both Cu 2 Se and CuInSe 2 phases in the resulting hierarchical composites. The result points to a new strategy to modulate the electronic structure of semiconductor composites to maximize interaction and coupling between two fundamentally contrasting properties enabling access to electronic hybrid systems with potential applications as interactive and stimuli-responsive multifunctional materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Quantum to classical parton evolution in the QGP

We study the time evolution of the density matrix of a high energy quark in the presence of a dense QCD background that is modeled as a stochastic Gaussian color field. At late times, we find that only the color singlet component of the quark’s reduced density matrix survives the in-medium evolution and that the density matrix becomes asymptotically diagonal in both transverse position and momentum spaces. In addition, we observe an accelerated entropy growth due to the larger phase space being explored by the quark and that the quantum and classical quark entropies converge at late times. We further observe that the quark state loses all memory of the initial condition. Combined with the fact that the reduced density matrix satisfies Boltzmann-diffusion transport, we conclude that the quark reduced density matrix can be interpreted as a classical phase space distribution.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Transition characteristics and electron kinetics in microhollow cathode discharges

We demonstrate the transition characteristics and electron kinetics of microdischarges in a microgap with a cathode having microstructures using two-dimensional particle-in-cell/Monte Carlo collision (2d3v) simulations. It is found that the microdischarge is inside the hollow cavity at lower pressures, forming microhollow cathode discharges (MHCDs), while the dominant discharge moves outside the cavity at higher pressures, locating above the cathode rectangular protrusion tip. The spatial distributions of the microdischarge parameters (e.g., electric potential and electron density) are presented by capturing the transition characteristics. The electron kinetics of the microdischarges are examined based on the moment analysis of the electron Boltzmann equation. The collisional and non-collisional components of the electron power absorption are decomposed, which were seldomly investigated for microdischarges previously. The results provide fundamental insights into MHCD formation with a structured electrode at varying pressure conditions, which could be beneficial for the design and fabrication of microplasma devices in practical applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Simulation and measurement of beam-induced heating of ceramic vacuum chambers

In this article, we summarize recent theoretical and experimental studies of the impedance and beam-induced heating of titanium-coated ceramic vacuum chambers used in the NSLS-II injection kickers. The impedance was calculated using the field matching theory assuming planar approximation and compared with the mpedanceake2 code. For the coating thickness of a few microns, we demonstrated that the beam-induced power is dissipated in the titanium coating and that the longitudinally averaged two-dimensional power density is approximated by an analytical expression, thus allowing the use of a simplified model of the power density as input for the code to simulate the temperature distribution with realistic nonuniform thickness of the Ti coating. For a few values of the NSLS-II beam current, we measured the beam-induced heating of two ceramic chambers using thermal sensors installed along the chamber and compared the measurement results with the simulations. Published by the American Physical Society 2024

36 MATERIALS SCIENCE↗
DOE OSTI2023

Phase transition amplification of proton number fluctuations in nuclear collisions from a transport model approach

The time evolution of particle number fluctuations in nuclear collisions at intermediate energies (E lab = 1.23–10⁢A GeV) is studied by means of the UrQMD-3.5 transport model. The transport description incorporates baryonic interactions through a density-dependent potential. This allows for an implementation of a first-order phase transition including a mechanically unstable region at large baryon density. The scaled variance of the baryon and proton number distributions is calculated in the central cubic spatial volume of the collisions at different times. A significant enhancement of fluctuations associated with the unstable region is observed. This enhancement persists to late times, reflecting a memory effect for the fluctuations. The presence of the phase transition has a much smaller influence on the observable event-by-event fluctuations of protons in momentum space.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Constraining the dark matter contribution of $\gamma$ rays in clusters of galaxies using Fermi -LAT data

Clusters of galaxies are the largest gravitationally bound systems in the Universe. Their dynamics are dominated by dark matter (DM), which makes them among the best targets for indirect DM searches. We analyze 12 years of data collected by the Fermi Large Area Telescope (Fermi-LAT) in the direction of 49 clusters of galaxies selected for their proximity to the Earth and their high x-ray flux, which makes them the most promising targets. We first create physically motivated models for the DM density around each cluster considering different assumptions for the substructure distribution. Then we perform a combined search for a $\gamma$-ray signal in the Fermi-LAT data between 500 MeV and 1 TeV. We find a signal of $\gamma$ rays potentially associated with DM that is at a statistical significance of 2.5⁢σ-3.0⁢σ when considering a slope for the subhalo mass distribution α =1.9 and minimum mass of M min =10 -6⁢ M ⊙ . The best-fit DM mass and annihilation cross sections for a b⁢$\overline{b}$ annihilation channel are m X =40–60 GeV and $\langle$σ⁢ν$\rangle$=(2–4)×10 -25 cm 3 /s. When we consider α =2.0 and M min =10 -9⁢ M ⊙ , the best fit of the cross section reduces to $\langle$σ⁢ν$\rangle$=(4–10)×10 -26 cm 3 /s. For both DM substructure models there is a tension between the values of $\langle$σ⁢ν$\rangle$ that we find and the upper limits obtained with the nondetection of a $\gamma$-ray flux from Milky Way dwarf spheroidal galaxies. In conclusion, this signal is thus more likely associated with $\gamma$ rays produced in the intracluster region by cosmic rays colliding with gas and photon fields.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

A Data-Driven Nonparametric Approach for Probabilistic Load-Margin Assessment Considering Wind Power Penetration

A modern power system is characterized by an increasing penetration of wind power, which results in large uncertainties in its states. These uncertainties must be quantified properly; otherwise, the system security may be threatened. Facing this challenge, here we propose a cost-effective, data-driven approach to assessing a power system's load margin probabilistically. Using actual wind data, a kernel density estimator is applied to infer the nonparametric wind speed distributions, which are further merged into the framework of a vine copula. The latter enables us to simulate complex multivariate and highly dependent model inputs with a variety of bivariate copulae that precisely represent the tail dependence in the correlated samples. Furthermore, to reduce the prohibitive computational time of traditional Monte-Carlo simulations that process a large amount of samples, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model through a Bayesian-learning framework. To accelerate the convergence rate of this Bayesian algorithm, a truncated polynomial chaos surrogate, which serves as a highly efficient, parametric Bayesian prior, is developed. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. Results of simulations that are performed on several test systems reveal the impressive performance of the proposed method in the probabilistic load-margin assessment.

17 WIND ENERGY↗