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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

A Hunting Expedition For High-Order Hierarchies

Stellar hierarchical systems of high order containing more than three stars are rare and fascinating objects; their disco v ery and study highlight still unknown aspects of star formation and early evolution. We matched eclipsing binaries disco v ered by Transiting Exoplanet Surv e y Satellite with a Gaia catalogue of wide binaries and selected candidate quadruple (or higher order) systems based on e xcessiv e astrometric noise. A subset of 192 southern candidates located within 500 pc was observed by speckle interferometry, and we resolved for the first time 50 close pairs, confirming their high-order (from four to five components) multiplicity. These observations are reported, and some remarkable hierarchical systems are discussed.

Brian P Powell↗
DOE OSTI2020

Anomaly Detection in Sequential Health Care Data using Higher-Order Network Representation

The ever-increasing use of information technology (IT) in health care presents new challenges to patient care. Information errors arising from the use of Health IT and their implications on care delivery and patient outcomes have been widely reported. Information errors can lead to changes in clinical decisions, care processes, and care outcomes, among others. An altered care process, for example, could interrupt the sequence of care, which could lead to changes in care decisions and/or changes in care outcomes. We define interruptions in the care process as anomalies in the care sequence. In this paper, we present a new approach based on the higher-order network (HON) representation to detect anomalies in sequential health care data using electronic health records. The results show that there are higher-order dependencies in health care data; and that the use of HON representation is more effective than the first-order network representation for detecting anomalies in sequential health care data.

Niu, Haoran↗
DOE OSTI2020

Towards an all-orders calculation of the electroweak bubble wall velocity

We analyze Higgs condensate bubble expansion during a first-order electroweak phase transition in the early Universe. The interaction of particles with the bubble wall can be accompanied by the emission of multiple soft gauge bosons. When computed at fixed order in perturbation theory, this process exhibits large logarithmic enhancements which must be resummed to all orders when the wall velocity is large. We perform this resummation both analytically and numerically at leading logarithmic accuracy. The numerical simulation is achieved by means of a particle shower in the broken phase of the electroweak theory. The two approaches agree to the 10$\%$ level. For fast-moving walls, we find the scaling of the thermal pressure exerted against the wall to be $P\sim \gamma^2T^4$, independent of the particle masses, implying a significantly slower terminal velocity than previously suggested.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Structurally ordered nanoparticles, methods and applications

Embodiments provide a nanoparticle and a method for preparing the nanoparticle, as well as a membrane that includes the nanoparticle and a fuel cell that includes the membrane. The method comprises a thermal treatment method that provides from a nanoparticle comprising a structurally disordered material the nanoparticle comprising: (1) a structurally ordered core comprising a first material; and (2) a shell surrounding and further structurally aligned with the structurally ordered core and comprising a second material different from the first material. Particularly desirable is a nanoparticle comprising a Pt3Co@Pt/C structurally ordered core-shell composition supported upon a carbon support.

Abruna, Hector D.↗
DOE OSTI2021

Speculative instruction wakeup to tolerate draining delay of memory ordering violation check buffers

A technique for speculatively executing load-dependent instructions includes detecting that a memory ordering consistency queue is full for a completed load instruction. The technique also includes storing data loaded by the completed load instruction into a storage location for storing data when the memory ordering consistency queue is full. The technique further includes speculatively executing instructions that are dependent on the completed load instruction. The technique also includes in response to a slot becoming available in the memory ordering consistency queue, replaying the load instruction. The technique further includes in response to receiving loaded data for the replayed load instruction, testing for a data mis-speculation by comparing the loaded data for the replayed load instruction with the data loaded by the completed load instruction that is stored in the storage location.

Kalamatianos, John↗
DOE OSTI2021

Uniformly Ordered Binary Decision Algorithm for Benchmark Experiment Correlations in Whisper Validation

When performing a validation exercise for determining the upper subcritical limit of a nuclear criticality safety application, an analyst should select and perform a statistical analysis on a population of benchmark experiments that are neutronically similar to the application. The size of this population should be sufficiently large such that the statistical analysis has a high degree of confidence that the bias plus bias uncertainty (calculational margin) has been accurately quantified. A complication arises because many benchmark experiments share common components, leading to correlations in their measured effective multiplication factors. Correlations between benchmark experiments within the population reduces its predictive power. This motivates the need for methods that consider benchmark experiment correlations and ensure adequate statistical significance of results. The Whisper code is a statistical analysis pack- age that incorporates nuclear data sensitivity coefficients from MCNP to assess benchmark experiment similarity and then performs an extreme-value analysis to estimate the bias plus bias uncertainty. The original methodology in Whisper does not consider the effect of benchmark experiment correlations when making this estimation, and this summary proposes the uniformly ordered binary decision algorithm to address this shortcoming. The original methodology in Whisper computes similarity coefficients ck for an application compared to all benchmark experiments in its library and develops weighting factors for a selected population proportional to the ck values. The methodology can be interpreted as statistically emulating a validation exercise for a particular application where the weighting factors may be viewed as the likelihood that an analyst would include a particular benchmark experiment within the population. The effective sample size of the population is the expected or mean number of benchmark experiments in the population. The uniformly ordered binary decision algorithm identifies clusters of correlated benchmark experiments within the population and then computes adjusted weighting factors based on the magnitude of the correlation coefficients within the cluster to compute a reduced effective sample size accounting for the lower information content because of correlations. Benchmark experiments within the cluster are ordered randomly with equal probability and probabilistic decisions are made as to whether a benchmark. experiment within the cluster should treated as redundant with a previous one; if two redundant benchmark experiments are included, then the conservative worst case bias plus bias uncertainty is used and the pair is counted as a single benchmark experiment in the population. Results are provided for HEU solutions in a research version of the Whisper software using benchmark experiment correlations provided by DICE, the Database for the International Criticality Safety Benchmark Evaluation Project (ICSBEP). These show that there can be a significant increase in the bias plus bias uncertainty because the effective sample size is reduced, and therefore the algorithm, needing to meet sample size requirements, expands the benchmark experiment population by accepting less similar benchmark experiments that would have otherwise not been included.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Reduced Order Model for Guided Wave Propagation on Gas Pipelines to Enable Real-Time Simulation

Reduced order model for simulation of Guided wave propagation is presented here. The utilization of reduced order models ensures efficient data generation for a variety of parameters where it takes huge computational effort to simulate, crucial for timely monitoring and decision-making. Autoencoder based reduced order models are proposed here, which are trained on simulated data from open-source finite element framework, Firedrake.

Bukka, Sandeep Reddy↗
DOE OSTI2026

Determining Neutrino Mass Ordering with NOvA and Upcoming JUNO Measurements

NOvA has reported a significance of mass ordering determination using ten years of data together with external constraints from reactor-based experiments. The JUNO collaboration is poised to provide a more precise reactor-based constraint on $|Δm^2_{32}|$. In this Letter, we explore the potential impact of this anticipated measurement on the determination of the neutrino mass ordering by NOvA. We find that $3σ$ evidence of the normal ordering is achievable over a range of plausible JUNO measurements within the next five years.

Abubakar, S. [Erciyes U.]↗
DOE OSTI2022

Dimension of Restricted Classes of Interval Orders

Rabinovitch showed in 1978 that the interval orders having a representation consisting of only closed unit intervals have order dimension at most 3. Herein, this article shows that the same dimension bound applies to two other classes of posets: those having a representation consisting of unit intervals (but with a mixture of open and closed intervals allowed) and those having a representation consisting of closed intervals with lengths in {0,1}.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: Comparison with linear subspace techniques

Natural convection in porous media is a highly nonlinear multiphysical problem relevant to many engineering applications (e.g., the process of CO 2 sequestration). Here, we extend and present a non-intrusive reduced order model of natural convection in porous media employing deep convolutional autoencoders for the compression and reconstruction and either radial basis function (RBF) interpolation or artificial neural networks (ANNs) for mapping parameters of partial differential equations (PDEs) on the corresponding nonlinear manifolds. To benchmark our approach, we also describe linear compression and reconstruction processes relying on proper orthogonal decomposition (POD) and ANNs. Further, we present comprehensive comparisons among different models through three benchmark problems. The reduced order models, linear and nonlinear approaches, are much faster than the finite element model, obtaining a maximum speed-up of 7 × 10 6 because our framework is not bound by the Courant–Friedrichs–Lewy condition; hence, it could deliver quantities of interest at any given time contrary to the finite element model. Our model’s accuracy still lies within a relative error of 7% in the worst-case scenario. We illustrate that, in specific settings, the nonlinear approach outperforms its linear counterpart and vice versa. We hypothesize that a visual comparison between principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) could indicate which method will perform better prior to employing any specific compression strategy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A methodology for generating reduced-order models for large-scale buildings using the Krylov subspace method

Developing a computationally efficient but accurate building energy simulation (BES) model is important for many purposes. Model order reduction (MOR) methods are attractive and much more reliable than identification approaches, since it directly extract a lower-dimensional model from a detailed physics-based model without any pre-simulations. However, because of computational and data storage requirements, there are challenges of applying these methods to a large-scale building. To overcome the problem, this work introduces the Krylov subspace method to the building science field. Technical issues of applying the method to building applications are addressed and a suitable algorithm that overcomes those challenges is presented. Furthermore, to demonstrate the reliability of the algorithm, comparisons between the resulted reduced-order model (ROM) and a high-fidelity model from a commercial BES software for a 60-zone case study building are provided. The ROM was a factor of 100 faster than the high fidelity model but with high accuracy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Identification of a characteristic doping for charge order phenomena in Bi-2212 cuprates via RIXS

Identifying quantum critical points (QCPs) and their associated fluctuations may hold the key to unraveling the unusual electronic phenomena observed in cuprate superconductors. Recently, signatures of quantum fluctuations associated with charge order (CO) have been inferred from the anomalous enhancement of CO excitations that accompany the reduction of the CO order parameter in the superconducting state. Furthermore, to gain more insight about the interplay between CO and superconductivity, we investigate the doping dependence of this phenomenon throughout the Bi-2212 cuprate phase diagram using resonant inelastic x-ray scattering (RIXS) at the Cu L 3 edge. As doping increases, the CO wavevector decreases, saturating near a commensurate value of 0.25 r.l.u. beyond a characteristic doping p c , where the correlation length becomes shorter than the apparent periodicity (4a 0 ). Such behavior is indicative of the fluctuating nature of the CO; and the proliferation of CO excitations in the superconducting state also appears strongest at p c , consistent with expected behavior at a CO QCP. Intriguingly, p c appears to be near optimal doping, where the superconducting transition temperature T c is maximal.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A guide to the design of the virtual element methods for second- and fourth-order partial differential equations

Here we discuss the design and implementation details of two conforming virtual element methods for the numerical approximation of two partial differential equations that emerge in phase-field modeling of fracture propagation in elastic material. The two partial differential equations are: (i) a linear hyperbolic equation describing the momentum balance and (ii) a fourth-order elliptic equation modeling the damage of the material. Inspired by, we develop a new conforming VEM for the discretization of the two equations, which is implementation-friendly, i.e., different terms can be implemented by exploiting a single projection operator. We use C 0 and C 1 virtual elements for the second-and fourth-order partial differential equation, respectively. For both equations, we review the formulation of the virtual element approximation and discuss the details pertaining the implementation.

42 ENGINEERING↗
DOE OSTI2025

Grain Boundary Segregation Suppresses Local Short‐Range Ordering in Nanocrystalline High‐Entropy Alloys

Multi-principal-element alloys like high-entropy alloys (HEAs) have potential applications in many engineering fields due to their unique mechanical/functional properties. While HEAs are generally considered random solid solutions, recent studies revealed that they are prone to short-range-ordering (SRO) due to the complex multi-pair-wise interactions among the constituent elements. Meanwhile, SROs' evolution can sometimes be deleterious, and it is necessary to have control over their evolution. Examining the AlCoCrFe-Zr model alloy, long-range ordering occurs following the expectation of enthalpic predictions. Advanced characterization techniques—transmission electron microscopy, high-energy synchrotron X-ray diffraction/pair distribution function, and atom probe tomography, reveal that SRO is suppressed in as-milled and GB-decorated NC-(AlCoCrFe)100-xZrx (x = 0–1.5 atomic %). Warren-Cowley coefficient calculations are further used to validate the suppression of SRO. Besides the low segregation enthalpies of Cr, Fe, and Zr, and the high-mixing enthalpy of Cr and Fe, the short diffusion path to GBs due to high-GB density in the NC-HEAs and the higher energy state of the GBs than the matrix promotes GB-segregation that further alters the matrix chemistry and consequently disfavors SRO formation within the matrix. Despite the GB-segregation of Cr, Fe, and Zr, the matrices and GBs remain in a random solid solution.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Correlation of Oxygen Anion Redox Activity to In‐Plane Honeycomb Cation Ordering in Na x Ni y Mn 1− y O 2 Cathodes

Sodium‐ion batteries (SIBs) are one of the most promising next‐generation energy storage systems because of their abundant and low‐cost component materials. However, the lower energy density of SIBs compared with lithium‐ion batteries diminishes their practical value proposition. Among the many sodium‐based cathodes, layered transition metal oxides with high sodium content have energy densities comparable with the lithium‐ion battery technology. When charged above 4.1 V, the sodium‐based cathodes often undergo transformations because the activation of oxygen anion redox causes irreversible oxygen release, transition metal ion migration, lattice distortion, and rapid capacity decay. Here, in situ gas analysis is performed to evaluate the lattice oxygen anion redox activity in Na x Ni y Mn 1− y O 2 cathodes with P2 and O3 structural orderings. Operando X‐ray diffraction and neutron diffraction are performed to assess the structural changes related to lattice oxygen redox and transition metal ion migration in Na x Ni y Mn 1− y O 2 cathodes. The results unveil that in‐plane honeycomb cationic ordering can help suppress oxygen anion redox activity, which is critical for the future design of layered transition metal oxide cathodes that are prone to achieve high‐energy for durable SIBs.

25 ENERGY STORAGE↗
DOE OSTI2020

Sampling and resolution characteristics in reduced order models of shallow water equations: Intrusive vs nonintrusive

We investigate the sensitivity of reduced order models (ROMs) to training data spatial resolution as well as sampling rate. In particular, we consider proper orthogonal decomposition (POD), coupled with Galerkin projection (POD-GP), as an intrusive reduced order modeling technique. For non-intrusive ROMs, we consider two frameworks. The first is using dynamic mode decomposition (DMD), and the second is based on artificial neural networks (ANNs). For ANN, we utilized a residual deep neural network, and for DMD we have studied two versions of DMD approaches; one with hard thresholding and the other with sorted bases selection. Also, we highlight the differences between mean-subtracting the data (centering) and using the data without mean-subtraction. We tested these ROMs using a system of 2D shallow water equations for four different numerical experiments, adopting combinations of sampling rates and spatial resolutions. For these cases, we found that the DMD basis obtained with hard threshodling is sensitive to sampling rate. The sorted DMD algorithm helps to mitigate this problem and yields more stabilized and converging solution. Furthermore, we demonstrate that both DMD approaches without mean subtraction provide significantly more accurate results than DMD with mean-subtracting the data. On the other hand, POD is relatively insensitive to sampling rate and yields better representation of the flow field. Meanwhile, spatial resolution has little effect on both POD and DMD performances. Here, the numerical results reveal that an ANN on POD subspace (POD-ANN) performs remarkably better than POD-GP and DMD in capturing system dynamics, even with a small number of modes.

42 ENGINEERING↗
DOE OSTI2021

Nitride-Oxide-Metal Heterostructure with Self-Assembled Core–Shell Nanopillar Arrays: Effect of Ordering on Magneto-Optical Properties

Magneto-optical (MO) coupling incorporates photon-induced change of magnetic polarization that can be adopted in ultrafast switching, optical isolators, mode convertors, and optical data storage components for advanced optical integrated circuits. However, integrating plasmonic, magnetic, and dielectric properties in one single material system poses challenges since one natural material can hardly possess all these functionalities. Here, co-deposition of a three-phase heterostructure composed of a durable conductive nitride matrix with embedded core–shell vertically aligned nanopillars, is demonstrated. Additionally, the unique coupling between ferromagnetic NiO core and atomically sharp plasmonic Au shell enables strong MO activity out-of-plane at room temperature. Further, a template growth process is applied, which significantly enhances the ordering of the nanopillar array. The ordered nanostructure offers two schemes of spin polarization which result in stronger antisymmetry of Kerr rotation. The presented complex hybrid metamaterial platform with strong magnetic and optical anisotropies is promising for tunable and modulated all-optical-based nanodevices.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High-Order Mesh hr-adaptivity for Surface Fitting to Implicit Geometries

We present an ℎ𝑟-adaptivity framework for morphing a given mesh to fit a target surface prescribed as the zero isocontour of a discrete function. In this framework, high-order meshing is posed as a variational minimization problem that depends on the mesh quality prescribed via the target matrix optimization paradigm (TMOP) and position of a subset of mesh nodes with respect to the target surface. The proposed formulation ensures that the variational problem is converged and mesh quality degradation near the surface is limited, even when the mesh topology is incompatible with the target surface. Additionally, a mesh subset-based approach and ℎ-refinement is introduced to efficiently increase fitting accuracy while reducing the computational cost of the mesh morphing problem. The ℎ𝑟-adaptivity technique extends to different element types in two- and three-dimensions, and can be used in existing finite element and spectral element frameworks to obtain high-order body-fitted meshes. Various numerical experiments demonstrate the robustness and accuracy of the fitting approach for problems of practical interest such as Lagrangian hydrodynamics and topology optimization.

97 MATHEMATICS AND COMPUTING↗