Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Phase correlation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Fluctuation dynamics near the QCD critical point

The evolution of nonhydrodynamic slow processes near the QCD critical point is explored with the novel hydro+ framework, which extends the conventional hydrodynamic description by coupling it to additional explicitly evolving slow modes describing long wavelength fluctuations. Here, their slow relaxation is controlled by critical behavior of the correlation length and is independent from gradients of matter density and pressure that control the evolution of the hydrodynamic quantities. In this exploratory study, we follow the evolution of the slow modes on top of a simplified QCD matter background, allowing us to clearly distinguish and study, both separately and in combination, the main effects controlling the dynamics of critical slow modes. In particular, we show how the evolution of the slow modes depends on their wave number, the expansion of and advection by the fluid background, and the behavior of the correlation length. Nonequilibrium contributions from the slow modes to bulk matter properties that affect the bulk dynamics (entropy, pressure, temperature, and chemical potential) are discussed and found to be small.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Higher-order cumulants and correlation functions of proton multiplicity distributions in s N N = 3 GeV Au + Au collisions at the RHIC STAR experiment

Here, we report a measurement of cumulants and correlation functions of event-by-event proton multiplicity distributions from fixed-target Au+Au collisions at $\sqrt{s_\text{NN}}$ = 3 GeV measured by the STAR experiment. Protons are identified within the rapidity (y) and transverse momentum ($p_T$) region –0:9 < $\textit{y}$ < 0 and 0:4 < pT < 2:0 GeV/c in the center-of-mass frame. A systematic analysis of the proton cumulants and correlation functions up to sixth-order as well as the corresponding ratios as a function of the collision centrality, $p_T$, and $\textit{y}$ are presented. The effect of pileup and initial volume fluctuations on these observables and the respective corrections are discussed in detail. The results are compared to calculations from the hadronic transport UrQMD model as well as a hydrodynamic model. In the most central 5% collisions, the value of proton cumulant ratio $C_4 = C_2$ is negative, drastically different from the values observed in Au+Au collisions at higher energies. Compared to model calculations including Lattice QCD, a hadronic transport model, and a hydrodynamic model, the strong suppression in the ratio of $C_4/C_2$ at 3 GeV Au+Au collisions indicates an energy regime dominated by hadronic interactions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Triplet Resonating Valence Bond State and Superconductivity in Hund’s Metals

A central idea in strongly correlated systems is that doping a Mott insulator leads to a superconductor by transforming the resonating valence bonds (RVBs) into spin-singlet Cooper pairs. Here, we argue that a spin-triplet RVB (tRVB) state, driven by spatially, or orbitally anisotropic ferromagnetic interactions can provide the parent state for triplet superconductivity. We apply this idea to the iron-based superconductors, arguing that strong on site Hund’s interactions develop intra-atomic tRVBs between the $t_{2_g}$ orbitals. On doping, the presence of two iron atoms per unit cell allows these interorbital triplets to coherently delocalize onto the Fermi surface, forming a fully gapped triplet superconductor. In this work, this mechanism gives rise to a unique staggered structure of on site pair correlations, detectable as an alternating π phase shift in a scanning Josephson tunneling microscope.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Nonmonotonic Energy Dependence of Net-Proton Number Fluctuations

Nonmonotonic variation with collision energy (√ s NN) of the moments of the net-baryon number distribution in heavy-ion collisions, related to the correlation length and the susceptibilities of the system, is suggested as a signature for the quantum chromodynamics critical point. Here, we report the first evidence of a nonmonotonic variation in the kurtosis times variance of the net-proton number (proxy for net-baryon number) distribution as a function of √ s NN with 3.1 σ significance for head-on (central) gold-on-gold (Au+Au) collisions measured solenoidal tracker at Relativistic Heavy Ion Collider. Data in noncentral Au+Au collisions and models of heavy-ion collisions without a critical point show a monotonic variation as a function of √ s NN.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Strain-Induced Quantum Phase Transitions in Magic-Angle Graphene

In this work, we investigate the effect of uniaxial heterostrain on the interacting phase diagram of magic-angle twisted bilayer graphene. Using both self-consistent Hartree-Fock and density-matrix renormalization group calculations, we find that small strain values (ε~0.1%-0.2%) drive a zero-temperature phase transition between the symmetry-broken "Kramers intervalley-coherent" insulator and a nematic semimetal. The critical strain lies within the range of experimentally observed strain values, and we therefore predict that strain is at least partly responsible for the sample-dependent experimental observations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Four-Spin Terms and the Origin of the Chiral Spin Liquid in Mott Insulators on the Triangular Lattice

At strong repulsion, the triangular-lattice Hubbard model is described by s=1/2 spins with nearest-neighbor antiferromagnetic Heisenberg interactions and exhibits conventional 120° order. Using the infinite density matrix renormalization group and exact diagonalization, we study the effect of the additional four-spin interactions naturally generated from the underlying Mott-insulator physics of electrons as the repulsion decreases. Although these interactions have historically been connected with a gapless ground state with emergent spinon Fermi surface, we find that, at physically relevant parameters, they stabilize a chiral spin liquid (CSL) of Kalmeyer-Laughlin (KL) type, clarifying observations in recent studies of the Hubbard model. Finally, we present a self-consistent solution based on a mean-field rewriting of the interaction to obtain a Hamiltonian with similarities to the parent Hamiltonian of the KL state, providing a physical understanding for the origin of the CSL.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Quantum Melting of Generalized Wigner Crystals in Transition Metal Dichalcogenide Moiré Systems

The generalized Wigner crystal (GWC) is a novel quantum phase of matter driven by further-range interaction at fractional fillings of a lattice. The role of further-range interaction as the driver for the incompressible state is akin to the Wigner crystal. On the other hand, the significant role of commensurate filling is akin to the Mott insulator. Recent progress in simulator platforms presents unprecedented opportunities to investigate quantum melting in the strongly interacting regime through synergy between theory and experiments. However, the earlier theory literature presents diverging predictions. We study the quantum freezing of GWC through large-scale density matrix renormalization group simulations of a triangular lattice extended Hubbard model. We find a single first-order phase transition between the Fermi liquid and the √3×√3 GWC state. Furthermore, the GWC state shows long-range antiferromagnetic 120° Néel order. Our results present the simplest answers to the question of the quantum phase transition into the GWC phase and the properties of the GWC phase.

Density matrix renormalization group↗
DOE OSTI2021

Quantum cluster algorithm for data classification

Abstract We present a quantum algorithm for data classification based on the nearest-neighbor learning algorithm. The classification algorithm is divided into two steps: Firstly, data in the same class is divided into smaller groups with sublabels assisting building boundaries between data with different labels. Secondly we construct a quantum circuit for classification that contains multi control gates. The algorithm is easy to implement and efficient in predicting the labels of test data. To illustrate the power and efficiency of this approach, we construct the phase transition diagram for the metal-insulator transition of VO 2 , using limited trained experimental data, where VO 2 is a typical strongly correlated electron materials, and the metallic-insulating phase transition has drawn much attention in condensed matter physics. Moreover, we demonstrate our algorithm on the classification of randomly generated data and the classification of entanglement for various Werner states, where the training sets can not be divided by a single curve, instead, more than one curves are required to separate them apart perfectly. Our preliminary result shows considerable potential for various classification problems, particularly for constructing different phases in materials.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Macro-micro multiscale modeling to assist the design of HPDC Al castings microstructure and alloys for EV super-large body structures (Phase 1)

Implementation of High Pressure Die Casting (HPDC) Aluminum (Al) body structures for high volume electrified vehicles (EV) to improve electric efficiency remains a key strategy within many original equipment manufacturer (OEM)s. In addition to high strength for safety requirements, superior Self-Piercing Riveting (SPR) performance is demanded for HPDC Al alloys to be compatible with high volume SPR joining. In this work, it is proposed to extend and validate an existing Contractor finite element multiscale macro-micro modeling approach to quantify the influence of the microstructure of HPDC alloys on the fracture strain/displacement under 3-point bend and clinch testing. The success of this work will allow to replace solution treatment stage with low energy consumption heat treatment (HT) processes, or to design new non heat treatable (NHT) HPDC Al alloys to eliminate HT requirements. Ultimately, this project will facilitate the application of HPDC Al alloys for super-large vehicle structures to significantly reduce vehicle weight, and thus improving energy efficiency. The purpose of this project is to extend and validate an existing finite element code, which is based on the Contractor developed macro-micro multi-scale modeling approach, to numerically simulate the three-point bending and clinch test and study the influences of material microstructural characteristics and phase properties on the rivetability. The macro-micro modeling approach begins with a sample scale model and identify the location, which is mostly prone to failure, the deformation history of the boundaries of that location calculated will be used to drive a microstructure-based sub-models where the material microstructure and microscale properties are considered. Using this approach, the wrap-bending failure for two Al alloys are correctly predicted for the first time. This will start with phase I effort of building a framework of macro-micro three point bending test and clinch test of Al10SiMgMn HPDC alloy in the as-cast and T7 heat treated conditions. Those results will then be validated with experimental test results. The phase II effort will involve the utilization of the knowledge learned in phase I to establish the quantitative correlation between the microstructure characteristics and the riveting performance, which will be further used to guide the optimization of HPDC Al alloy microstructure using heat treatment process to achieve sufficient rivetability to join large thin-wall HPDC alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2025

An Early Investigation of the HHL Quantum Linear Solver for Scientific Applications

In this paper, we explore using the Harrow–Hassidim–Lloyd (HHL) algorithm to address scientific and engineering problems through quantum computing, utilizing the NWQSim simulation package on a high-performance computing platform. Focusing on domains such as power-grid management and climate projection, we demonstrate the correlations of the accuracy of quantum phase estimation, along with various properties of coefficient matrices, on the final solution and quantum resource cost in iterative and non-iterative numerical methods such as the Newton–Raphson method and finite difference method, as well as their impacts on quantum error correction costs using the Microsoft Azure Quantum resource estimator. We summarize the exponential resource cost from quantum phase estimation before and after quantum error correction and illustrate a potential way to reduce the demands on physical qubits. This work lays down a preliminary step for future investigations, urging a closer examination of quantum algorithms’ scalability and efficiency in domain applications.

hybrid software for QC-HPC↗
DOE OSTI2023

Evaluating the Impact of Redox Potential on the Corrosion of Q125, 316L, and C276 Steel in Low-Temperature Geothermal Systems

Time series experiments were used to explore the fluid redox impact on the corrosion of Q125, 316L, and C276 steels in low-ionic-strength and neutral water at temperature and pressure conditions associated with low-temperature geothermal systems. After exposing polished samples of each steel grade to an oxidizing (H2O2) and a reducing (Zn-doped) fluid for intervals of 24 h, 1 week, and 6 weeks, the atomic force microscopy results revealed general corrosion for Q125, while 316L and C276 exhibited pitting, crevice expansion, and edge attack corrosion. Secondary depositional features are frequently found as topographic highs, adjacent to pitting corrosion. These features may be identified as there is a very strong spatial correlation between the height retrace and phase retrace surface maps. All steels became progressively rougher over time after exposure to both fluids, while the corrosion rates were more complex. Samples exposed to the reducing fluid experienced an increase in the corrosion rate over time, while C276 and 316L experienced a decrease in the corrosion rate. Finally, a novel data validation technique was developed to address the intrinsic scalability of corrosion. The results indicate that the AFM scan area does not affect the measured surface roughness over nearly three orders of magnitude.

Bowman, Samuel (ORCID:000000021510174X)↗
DOE OSTI2020

Spatiotemporal Pattern Recognition in the PMU Signals in the WECC system

Phasor measurement unit (PMU) data has been used by multiple power system applications, including state estimation, post event analysis, oscillation detection, model validation, and many others. Still, due to its big data nature and availability to general research institutions, comprehensive understanding of the spatiotemporal patterns and underlying mechanisms are incomplete. This study applies a set of signal processing and machine learning approaches aiming at deciphering the characteristic behaviors of multiple phasor measurement units (PMUs) attributes (e.g., voltage, frequency, rate of change of frequency, phase angle), including their auto-correlation, cross-dependence, similarities and discrepancies across units and temporal scales, and distributions of anomalies and their linkages to potential external factors such as weather events. Data analytics are applied to PMUs from the U.S. Western Electricity Coordinating Council (WECC) system. The PMU measurements, recorded events, outages, and weather extremes are all from real world datasets. The findings from the study and mechanistic understanding of the PMU dynamics help provide guidance on system control or preventing blackouts. The derived metrics can be directly used for adjusting or filtering simulated PMU data used for advanced algorithm development.

Hou, Zhangshuan↗