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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Quantifying concordance of correlated cosmological data sets

We develop estimators of agreement and disagreement between correlated cosmological data sets. These account for data correlations when computing the significance of both tensions and excess confirmation while remaining statistically optimal. We discuss and thoroughly characterize different approaches commenting on the ones that have the best behavior in practical applications. We complement the calculation of their statistical distribution within the Gaussian model with one estimator that takes non-Gaussianities fully into account. To illustrate the use of our techniques, we apply these estimators to supernovae measurements of the distance-redshift relation, absolutely calibrated by the local distance ladder. The suite of best estimators that we discuss finds results that are in excellent agreement between estimators and find no indications of significant internal inconsistencies in this data set above the 1% probability threshold. Furthermore, this shows the robustness of local determinations of the Hubble constant to features in the distance-redshift relation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Assessing equation of state-independent relations for neutron stars with nonparametric models

Relations between neutron star properties that do not depend on the nuclear equation of state offer insights on neutron star physics and have practical applications in data analysis. Such relations are obtained by fitting to a range of phenomenological or nuclear physics equation of state models, each of which may have varying degrees of accuracy. In this study we revisit commonly used relations and reassess them with a very flexible set of phenomenological nonparametric equation of state models that are based on Gaussian processes. Our models correspond to two sets: equations of state which mimic hadronic models, and equations of state with rapidly changing behavior that resemble phase transitions. Here we quantify the accuracy of relations under both sets and discuss their applicability with respect to expected upcoming statistical uncertainties of astrophysical observations. We further propose a goodness-of-fit metric which provides an estimate for the systematic error introduced by using the relation to model a certain equation-of-state set. Overall, the nonparametric distribution is more poorly fit with existing relations, with the I–Love–Q relations retaining the highest degree of universality. Fits degrade for relations involving the tidal deformability, such as the binary-Love and compactness-Love relations, and when introducing phase transition phenomenology. For most relations, systematic errors are comparable to current statistical uncertainties under the nonparametric equation of state distributions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Five-point functions and the permutation group 𝑆 5

Five-point functions and five-body wave functions play an important role in many areas of nuclear and particle physics, e.g., in 2 →3 scattering processes, in the five-gluon vertex, or in the study of pentaquarks. In this work we consider the permutation group 𝑆 5 to facilitate the description of such objects. We work out the multiplets transforming under irreducible representations of 𝑆 5 and provide compact formulas allowing one to cast the permutations of an object 𝑓 12345 into combinations with definite permutation symmetry. We also give the explicit expressions for the irreducible multiplet products. We consider several practical applications as examples: We arrange the four-momenta and Lorentz invariants of a five-point function into the multiplet structure, we work out the color tensors of the five-gluon vertex in the multiplet notation, and we discuss applications for five-body wave functions like those of pentaquarks.

Bethe-Salpeter equation↗
DOE OSTI2023

Signatures of an energetic charged body streaming in a plasma

A charged body moving in a plasma can excite a variety of linear and nonlinear waves in the form of trailing wakes, fore-wake shocks, and precursor solitons. These structures can further interact with the background plasma to create secondary effects that can serve as signatures of the passage of the charged body. Using particle-in-cell simulations, we carry out a basic investigation of the dynamics of a plasma system that is being traversed by an energetic charged body. Using two different shapes of this charged source, namely, an idealized infinite length planar source and a two-dimensional thin rectangular source, we examine the differences in the nature of the excited wave structures and their consequent impact on the background plasma. Our simulations reveal interesting features such as the dependence of the precursor speeds on the total charge on the driving source, local particle trapping, and energization of the trapped particles in various regions along the traversal path leading to the formation of energetic charged beamlets. So our basic findings could find practical applications such as in analyzing the trajectories of charged objects like space debris orbiting in the ionosphere.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Truncated Nonlinear Interferometry for Quantum-Enhanced Atomic Force Microscopy

Nonlinear interferometers that replace beam splitters in Mach-Zehnder interferometers with nonlinear amplifiers for quantum-enhanced phase measurements have drawn increasing interest in recent years, but practical quantum sensors based on nonlinear interferometry remain an outstanding challenge. Here, we demonstrate the first practical application of nonlinear interferometry by measuring the displacement of an atomic force microscope microcantilever with quantum noise reduction of up to 3 dB below the standard quantum limit, corresponding to a quantum-enhanced measurement of beam displacement of 1.7 fm/√Hz. Further, we minimize photon backaction noise while taking advantage of quantum noise reduction by transducing the cantilever displacement signal with a weak squeezed state while using dual homodyne detection with a higher power local oscillator. This approach may enable quantum-enhanced broadband, high-speed scanning probe microscopy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Phase Switching as the Origin of Large Piezoelectric Response in Organic-Inorganic Perovskites: A First-Principles Study

Piezoelectrics are critical functional components of many practical applications such as sensors, ultrasonic transducers, actuators, medical imaging, telecommunications. So far, the best performing piezoelectrics are ferroelectric ceramics, many of which are toxic, heavy, hard, and cost-ineffective. Recently, a groundbreaking discovery of extraordinarily large piezoelectric coefficients in the family of organic-inorganic perovskites gave a hope for a cheaper, environmentally friendly, inexpensive, light-weight and flexible alternative. However, the origin of such response in organic-inorganic ferroelectrics whose spontaneous polarization is an order of magnitude smaller than for inorganic counterparts remains unclear. In this study we employ first-principles simulations to predict that the mechanism associated with large piezoelectric constants is of extrinsic origin and associated with switching between the stable phase and previously overlooked energetically competitive metastable phase that can be stabilized by the external stress. Here, the phase switching changes the polarization direction and, therefore, gives origin to large piezoelectric response, similar to PbZr$_{1-x}$Ti$_x$O$_3$ near morphotropic phase boundary. Existence of such metastable phases is likely to manifest as the dynamical molecular disorder above the Curie temperature and, therefore, could be intrinsic to the entire family of organic-inorganic ferroelectrics with such disorder.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Polaritonic Chern Insulators in Monolayer Semiconductors

Systems with strong light-matter interaction open up new avenues for studying topological phases of matter. Examples include exciton polaritons, mixed light-matter quasiparticles, where the topology of the polaritonic band structure arises from the collective coupling between matter wave and optical fields strongly confined in periodic dielectric structures. Distinct from light-matter interaction in a uniform environment, the spatially varying nature of the optical fields leads to a fundamental modification of the well-known optical selection rules, which were derived under the plane wave approximation. Here, in this study, we identify polaritonic Chern insulators by coupling valley excitons in transition metal dichalcogenides to photonic Bloch modes in a dielectric photonic crystal slab. We show that polaritonic Dirac points, which are markers for topological phase transition points, can be constructed from the collective coupling between valley excitons and photonic Dirac cones in the presence of both time-reversal and inversion symmetries. Lifting exciton valley degeneracy by breaking time-reversal symmetry leads to gapped polaritonic bands with nonzero Chern numbers. Through numerical simulations, we predict polaritonic chiral edge states residing inside the topological gaps. Our Letter paves the way for the further study of strong exciton-photon interaction in nanophotonic structures and for exploring polaritonic topological phases and their practical applications in polaritonic devices.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Slow-Wave Hybrid Magnonics

Cavity magnonics is an emerging research area focusing on the coupling between magnons and photons. Despite its great potential for coherent information processing, it has been long restricted by the narrow interaction bandwidth. Here, in this Letter, we theoretically propose and experimentally demonstrate a novel approach to achieve broadband photon-magnon coupling by adopting slow waves on engineered microwave waveguides. To the best of our knowledge, this is the first time that slow wave is combined with hybrid magnonics. Its unique properties promise great potentials for both fundamental research and practical applications, for instance, by deepening our understanding of the light -matter interaction in the slow wave regime and providing high -efficiency spin wave transducers. The device concept can be extended to other systems such as optomagnonics and magnomechanics, opening up new directions for hybrid magnonics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Site-Selective Polar Compensation of Mott Electrons in a Double-Perovskite Heterointerface

Double-perovskite oxides (DPOs) with two transition metal ions ($A_{2}BB'O_{6}$) offer a fascinating platform for exploring exotic physics and practical applications. Studying these DPOs as ultrathin epitaxial films on single crystalline substrates can add another dimension to engineering electronic, magnetic, and topological phenomena. Understanding the consequence of polarity mismatch between the substrate and the DPO would be the first step toward this broad goal. Here, we investigate this by studying the interface between a prototypical insulating DPO $Nd_{2}NiMnO_{6}$ and a wide band gap insulator $SrTiO_{3}$. The interface is found to be insulating in nature. By combining several experimental techniques and density functional theory, we establish a site-selective charge compensation process that occurs explicitly at the Mn site of the film, leaving the Ni sites inert. We further demonstrate that such surprising selectivity, which cannot be explained by existing mechanisms of polarity compensation, is directly associated with their electronic correlation energy scales. This study establishes the crucial role of Mott physics in polar compensation process and paves the way for designer doping strategies in complex oxides.

density functional theory↗
DOE OSTI2020

Local strain-dependent electronic structure and perpendicular magnetic anisotropy of a MnGaN 2D magnetic monolayer

Local strain-dependent spin-polarized electronic structure of a two-dimensional (2D) magnetic layer is an exciting property for practical applications. For example, it holds the promise for advanced ultrathin spintronic nanodevices with customized electronic and magnetic properties by local strain engineering. Here, we demonstrate that the spin-polarized electronic structure of a 2D manganese gallium nitride (MnGaN-2D) magnetic monolayer is sensitive to intrinsic local lattice strain, as proven by first-principles calculations and indicated by scanning tunneling spectroscopy measurements. Atomic resolution images reveal a highly non-Gaussian lattice spacing/strain distribution, while the spectroscopy reveals variations in the electronic density of states. Simulations of the MnGaN-2D monolayer based on first-principles calculations, including both isotropic and anisotropic strains, confirm a highly strain-dependent manganese partial density of states. Spin-orbit coupling is included which indicates either out-of-plane perpendicular magnetic anisotropy (PMA) or in-plane magnetic anisotropy, dependent on the type of strain whether compressive or tensile, suggesting that MnGaN-2D is magnetoelastic. The MnGaN-2D PMA is further supported by superconducting quantum interference device magnetometry measurements which reveal a high spin polarization of ~ 79 % at room temperature.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Quantum Criticality Under Imperfect Teleportation

Entanglement, measurement, and classical communication together enable teleportation of quantum states between distant parties, in principle, with perfect fidelity. To what extent do correlations and entanglement of a many-body wave function transfer under teleportation protocols? We address this question for the case of an imperfectly teleported quantum critical wave function, focusing on the ground state of a critical Ising chain. We demonstrate that imperfections, e.g., in the entangling gate adopted for a given protocol, effectively manifest as weak measurements acting on the otherwise pristinely teleported critical state. Armed with this perspective, we leverage and further develop the theory of measurement-altered quantum criticality to quantify the resilience of critical-state teleportation. We identify classes of teleportation protocols for which imperfection (i) preserves both the universal long-range entanglement and correlations of the original quantum critical state, (ii) weakly modifies these quantities away from their universal values, and (iii) obliterates long-range entanglement altogether while preserving power-law correlations, albeit with a new set of exponents. We also show that mixed states describing the average over a series of sequential imperfect teleportation events retain pristine power-law correlations due to a “built-in” decoding algorithm, though their entanglement structure measured by the negativity depends on errors similarly to individual protocol runs. These results may allow one to design teleportation protocols that optimize against errors—highlighting a potential practical application of measurement-altered criticality. Published by the American Physical Society 2024

Physics↗
DOE OSTI2026

Quantum-inspired weight-constrained neural network: Reducing variable numbers by 100× compared to standard neural networks

Although quantum machine learning has shown great promise, the practical application of quantum computers remains constrained in the noisy intermediate-scale quantum era. To take advantage of quantum machine learning, we investigate the underlying mathematical principles of these quantum models and find that the quantum neural network with amplitude encoding is equivalent to a weight-constrained neural network. Motivated by this discovery, we develop a classical weight-constrained neural network. We find that this approach can reduce the number of variables in a classical neural network by a factor of 135 while preserving its accuracy. In addition, we develop a dropout method to enhance the robustness of quantum machine learning models, which are highly susceptible to adversarial attacks. This technique can also be applied to improve the adversarial robustness of the classical weight-constrained neural network, which is essential for industry applications, such as self-driving vehicles. Our work offers an approach to reduce the complexity of large classical neural networks, addressing a critical challenge in machine learning.

quantum algorithms & computation↗
DOE OSTI2026

A Systematic Review on Coordinated Restoration Strategies for Power Distribution Grids

Power distribution grids are increasingly exposed to High-Impact Low-Probability (HILP) events, which cause widespread disruptions with severe societal and economic impacts. The growing complexity of modern grids, driven by the integration of distributed energy resources and smart grid technologies, has introduced new challenges to effective service restoration. While significant research has explored individual restoration strategies, such as network reconfiguration and microgrid formation, limited attention has been given to methods in which they can be effectively coordinated. Furthermore, the absence of systematic review papers addressing this issue hampers the development of cohesive restoration frameworks capable of addressing the operational complexities of modern grids. This paper presents a systematic review synthesizing existing knowledge on power grid restoration, identifying key limitations, and highlighting opportunities for coordinated strategies. By addressing research gaps and emphasizing the integration of diverse approaches, this study provides critical insights for advancing grid resiliency and recovery, offering a foundation for future research and practical applications in the face of HILP events.

Systematic review↗
DOE OSTI2021

Optimal Balance of Privacy and Utility with Differential Privacy Deep Learning Frameworks

As the number of online services has increased, the amount of sensitive data being recorded is rising. Simultaneously, the decision-making process has improved by using the vast amounts of data, where machine learning has transformed entire industries. This paper addresses the development of optimal private deep neural networks and discusses the challenges associated with this task. We focus on differential privacy implementations and finding the optimal balance between accuracy and privacy, benefits and limitations of existing libraries, and challenges of applying private machine learning models in practical applications. Our analysis shows that learning rate, and privacy budget are the key factors that impact the results, and we discuss options for these settings.

Kotevska, Olivera↗
DOE OSTI2020

Standard Characterization Techniques for Inorganic Phase Change Materials

Salt hydrates are promising candidates for phase change materials (PCMs) because of their low cost and high energy storage capacity. However, practical applications of salt hydrate based PCMs are limited due to their poor thermal cycling performance caused by phase separation and supercooling. Various techniques, including differential scanning calorimetry (DSC) and transient plane source (TPS) have been used to characterize salt hydrate based PCMs but may not provide full understanding of the degradation mechanism. In this manuscript, salt hydrate and graphite composites are prepared. Temperature history (T-History) method and X-ray diffraction (XRD) are utilized for measuring melting temperature, energy storage, and latent heat degradation of the PCM composites. Highlighting the importance of each technique and how each technique plays an important role in analyzing salt hydrates performance accurately. Temperature history, and X-ray diffraction are also used in conjunction to precisely predict the cause of degradation in salt hydrate PCMs with thermal cycling, as in most literatures the cause of degradation in salt hydrate are attributed to phase segregation, supercooling, and change in stichometry water concentration without any scientific validation.

Kumar, Navin↗
DOE OSTI2020

Fiber-Optic Silicon Fabry-Perot Interferometric Bolometer: The Influence of Mechanical Vibration and Magnetic Field

Plasma radiation plays an important role in the operation and control of magnetic confinement fusion and is typically measured by bolometers. Here, we study the influence of mechanical vibration and magnetic field, which are typically present in magnetic confinement fusion devices, on the performance of a fiber-optic bolometer (FOB) based on silicon Fabry-Perot interferometer (FPI) demodulated by a scanning diode laser. It is found that both the vibration and magnetic field could cause large noise due to the birefringence of the silicon FPI. To mitigate the birefringence effects, we demonstrated two effective methods: polarization maintaining FOB and polarization scrambling. We show that two methods can be effective in mitigating the effect caused by the sensor birefringence: (1) replacing the regular single-mode fiber in the FOB system with polarization-maintaining fiber and (2) using a polarization scrambler after the laser source. For both methods, no significant increase in the noise was observed in the vibration and magnetic field tests. Finally, our research highlights the importance of the polarization management for the FOB systems toward their practical applications in magnetic-confinement fusion systems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing

The recent development of deep learning has been mostly focusing on Euclidean data, such as images, videos, audios, etc. However, most real-world information and relation are often expressed as graphs. To efficiently learn from graph data, graph convolutional networks (GCNs) emerge as a promising approach, showing advantages in several practical applications such as social network analysis, knowledge discovery, 3D modeling, motion capturing, etc. Real-world graphs are usually extremely large and imbalanced, posting significant performance demand and design challenges on the hardware dedicated for GCN inference. In this paper, we propose an architecture design called UW-GCN to accelerate graph convolutional network inference. To tackle the major performance bottleneck from workload imbalance, we propose dynamic neighborhood stealing and remote chunk shuffling techniques, relying on hardware flexibility to achieve hardware auto-tuning under negligible area or delay overhead. Specifically, UW-GCN is able to smartly profile the sparse graph pattern while continuously adjusting the workload distribution via routing reconfiguration among parallel processing elements (PEs). The ideal configuration is then reused in the remaining iterations. To the best of our knowledge, this is the first accelerator design particularly for GCN and the first work relying on hardware auto-tuning, which is normally based on software, to achieve near-optimal workload balance in processing sparse structures.

Geng, Tong↗
DOE OSTI2020

Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models

Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equations (PDEs). Several (nearly data free) approaches have been recently reported that successfully solve PDEs, with examples including deep feed forward networks, generative networks, and deep encoder-decoder networks. However, practical adoption of these approaches is limited by the difficulty in training these models, especially to make predictions at large output resolutions (≥1024×1024). Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models - training in reasonable time as well as distributing the storage requirements. Our framework provides several out of the box functionality including (a) loss integrity independent of number of processes, (b) synchronized batch normalization, and (c) distributed higher-order optimization methods. We show excellent scalability of this framework on both cloud as well as HPC clusters, and report on the interplay between bandwidth, network topology and bare metal vs cloud. We deploy this approach to train generative models of sizes hitherto not possible, showing that neural PDE solvers can be viably trained for practical applications. We also demonstrate that distributed higher-order optimization methods are 2-3× faster than stochastic gradient-based methods and provide minimal convergence drift with higher batch-size.

PDEs↗