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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Hierarchical Bayesian Thermonuclear Rate for the 7 Be(n,p) 7 Li Big Bang Nucleosynthesis Reaction

Big Bang nucleosynthesis provides the earliest probe of standard model physics, at a time when the universe was less than 1000 seconds old. It determines the abundances of the lightest nuclides, which give rise to the subsequent history of the visible matter in the universe. This work derives new 7 Be(n,p) 7 Li thermonuclear reaction rates based on all available experimental information. This reaction sensitively impacts the primordial abundances of 7 Be and 7 Li during big bang nucleosynthesis. We critically evaluate all available data and disregard experimental results that are questionable. For the nuclear model, we adopt an incoherent sum of single-level, two-channel, R -matrix approximation expressions, which are implemented into a hierarchical Bayesian model, to analyze the remaining six data sets we deem most reliable. In the fitting of the data, we consistently model all known sources of uncertainty, including discrepant absolute normalizations of different data sets, and also take the variation of the neutron and proton channel radii into account, hence providing less biased estimates of the 7 Be(n,p) 7 Li thermonuclear rates. From the resulting posteriors, we extract R -matrix parameters ($E_r, γ^2_n, γ^2_p$) and derive excitation energies and partial and total widths. Our fit is sensitive to the contributions of the first three levels above the neutron threshold. Reaction rates were computed by integrating 10,000 samples of the reduced cross section. Our 7 Be(n,p) 7 Li thermonuclear rates have uncertainties between 1.5% and 2.0% at temperatures of ≤1 GK. Finally, we compare our rates to previous results and find that the 7 Be(n,p) 7 Li rates most commonly used in big bang simulations have uncertainties that are too optimistic.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Physics-constrained Deep Recurrent Neural Models of Building Thermal Dynamics

We develop physics-constrained and control-oriented predictive deep learning models for the thermal dynamics of a real-world commercial office building. The proposed method is based on the systematic encoding of physics-based prior knowledge into a structured recurrent neural architecture. Specifically, our model mimics the structure of the building thermal dynamics model and leverages penalty methods to model inequality constraints. Additionally, we use constrained matrix parameterization based on the Perron-Frobenius theorem to bound the eigenvalues of the learned network weights. We interpret the stable eigenvalues as dissipativeness of the learned building thermal model. We demonstrate the effectiveness of the proposed approach on a dataset obtained from an office building with $20$ thermal zones.

Building Energy, structured neural networks↗
DOE OSTI2024

A flexible class of priors for orthonormal matrices with basis function-specific structure

Statistical modeling of high-dimensional matrix-valued data motivates the use of a low-rank representation that simultaneously summarizes key characteristics of the data and enables dimension reduction. Low-rank representations commonly factor the original data into the product of orthonormal basis functions and weights, where each basis function represents an independent feature of the data. However, the basis functions in these factorizations are typically computed using algorithmic methods that cannot quantify uncertainty or account for basis function correlation structure a priori. While there exist Bayesian methods that allow for a common correlation structure across basis functions, empirical examples motivate the need for basis function-specific dependence structure. We propose a prior distribution for orthonormal matrices that can explicitly model basis function-specific structure. The prior is used within a general probabilistic model for singular value decomposition to conduct posterior inference on the basis functions while accounting for measurement error and fixed effects. We discuss how the prior specification can be used for various scenarios and demonstrate favorable model properties through synthetic data examples. Finally, we apply our method to two-meter air temperature data from the Pacific Northwest, enhancing our understanding of the Earth system’s internal variability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Finite-temperature density matrix embedding theory

We describe a formulation of the density matrix embedding theory at finite temperature. We present a generalization of the ground-state bath orbital construction that embeds a mean-field finite-temperature density matrix up to a given order in the Hamiltonian, or the Hamiltonian up to a given order in the density matrix. In this work, we assess the performance of the finite-temperature density matrix embedding on the one-dimensional Hubbard model both at half-filling and away from it, and the two-dimensional Hubbard model at half-filling, comparing to exact data where available, as well as results from finite-temperature density matrix renormalization group, dynamical mean-field theory, and dynamical cluster approximations. The accuracy of finite-temperature density matrix embedding appears comparable to that of the ground-state theory, with, at most, a modest increase in bath size, and competitive with that of cluster dynamical mean-field theory.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Machine-learning prediction for quasiparton distribution function matrix elements

There have been rapid developments in the direct calculation in lattice QCD (LQCD) of the Bjorken-x dependence of hadron structure through large-momentum effective theory (LaMET). LaMET overcomes the previous limitation of LQCD to moments (that is, integrals over Bjorken x) of hadron structure, allowing LQCD to directly provide the kinematic regions where the experimental values are least known. LaMET requires large-momentum hadron states to minimize its systematics and allow us to reach small- x reliably. This means that very fine lattice spacing to minimize lattice artifacts at order $(P_za)^n$ will become crucial for next-generation LaMET-like structure calculations. Furthermore, such calculations require operators with long Wilson-link displacements, especially in finer lattice units, increasing the communication costs relative to that of the propagator inversion. In this work, we explore whether machine-learning algorithms can make predictions of correlators to reduce the computational cost of these LQCD calculations. We consider two algorithms, gradient-boosting decision tree and linear models, applied to LaMET data, the matrix elements needed to determine the kaon and $η_s$ unpolarized parton distribution functions (PDFs), meson distribution amplitude (DA), and the nucleon gluon PDF. We find that both algorithms can reliably predict the target observables with different prediction accuracy and systematic errors. The predictions from smaller displacement z to larger ones work better than those for momentum p due to the higher correlation among the data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Finite-size subthermal regime in disordered SU ( N ) -symmetric Heisenberg chains

SU⁡(N) symmetry is incompatible with the many-body localized (MBL) phase, even when strong disorder is present. However, recent studies have shown that finite-size SU⁡(2) systems exhibit nonergodic, subthermal behavior, characterized by the breakdown of the eigenstate thermalization hypothesis, and by the excited eigenstates entanglement entropy that is intermediate between area and volume law. In this paper, we extend previous studies of the SU⁡(2)-symmetric disordered Heisenberg model to larger systems, using the time-dependent density matrix renormalization group (tDMRG) method. We simulate quench dynamics from weakly entangled initial states up to long times, finding robust subthermal behavior at stronger disorder. Although we find an increased tendency towards thermalization at larger system sizes, the subthermal regime persists at intermediate time scales, nevertheless, and therefore should be accessible experimentally. At weaker disorder, we observe signatures of thermalization; however, entanglement entropy exhibits slow sublinear growth, in contrast to conventional thermalizing systems. Furthermore, we study dynamics of the SU⁡(3)-symmetric disordered Heisenberg model. Similarly, strong disorder drives the system into subthermal regime, albeit thermalizing phase is broader compared to the SU⁡(2) case. Finally, our findings demonstrate the robustness of the subthermal regime in spin chains with non-Abelian continuous symmetry, and are consistent with eventual thermalization at large system sizes and long time scales, suggested by previous studies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

A cavitation and dynamic void growth model for a general class of strain-softening amorphous materials

In this work, we derive a homogenized framework for cavitation and dynamic void growth in a general class of strain-softening materials with particular emphasis on amorphous materials. Analytic solutions are derived by considering an assemblage of spherical shells subject to dynamic hydrostatic tensile pressure. The isochoric response of the matrix material is captured by a free volume theory based amorphous viscoplasticity model. The framework accounts for the coupled rate-dependent, strain-softening behavior typically exhibited in amorphous materials. Validation of the homogenized theory is carried out against numerous direct numerical simulations with excellent agreement. Furthermore, we find that a reduction in cavitation strength and significant acceleration of dynamic void growth rates is induced as a consequence of strain-softening in the amorphous matrix material. Lastly, the model is utilized to interpret some non-intuitive experimental observations of amorphous materials subject to shock compression and subsequent spall failure.

36 MATERIALS SCIENCE↗
DOE OSTI2024

CFT and lattice correlators near an RG domain wall between minimal models

Conformal interfaces separating two conformal field theories (CFTs) provide maps between different CFTs, and naturally exist in nature as domain walls between different phases. One particularly interesting construction of a conformal interface is the renormalization group (RG) domain wall between CFTs. For a given Virasoro minimal model \mathcal{M}_{k+3,k+2} ℳ k + 3 , k + 2 , an RG domain wall can be generated by a specific deformation which triggers an RG flow towards its adjacent Virasoro minimal model \mathcal{M}_{k+2,k+1} ℳ k + 2 , k + 1 with the deformation turned on over part of the space. An algebraic construction of this domain wall was proposed by Gaiotto in [J. High Energy Phys. 12, 103 (2012)]. In this paper, we will provide a study of this RG domain wall for the minimal case k=2 k = 2 , which can be thought of as a nonperturbative check of the construction. In this case the wall is separating the Tricritical Ising Model (TIM) CFT and the Ising Model (IM) CFT. We will check the analytical results of correlation functions from the RG brane construction with the numerical density matrix renormalization group (DMRG) calculation using a lattice model proposed in [arXiv.1206.1332, Science 344, 280 (2014)], and find a perfect agreement. We comment on possible experimental realizations of this RG domain wall.

Cogburn, Cameron V.↗
DOE OSTI2021

Random circuit block-encoded matrix and a proposal of quantum LINPACK benchmark

The LINPACK benchmark reports the performance of a computer for solving a system of linear equations with dense random matrices. Although this task was not designed with a real application directly in mind, the LINPACK benchmark has been used to define the list of TOP500 supercomputers since the debut of the list in 1993. We propose that a similar benchmark, called the quantum LINPACK benchmark, could be used to measure the whole machine performance of quantum computers. The success of the quantum LINPACK benchmark should be viewed as the minimal requirement for a quantum computer to perform a useful task of solving linear algebra problems, such as linear systems of equations. We propose an input model called the Random Circuit Block-Encoded Matrix (RACBEM), which is a proper generalization of a dense random matrix in the quantum setting. The RACBEM model is efficient to be implemented on a quantum computer and can be designed to optimally adapt to any given quantum architecture, with relying on a black-box quantum compiler. Besides solving linear systems, the RACBEM model can be used to perform a variety of linear algebra tasks relevant to many physical applications, such as computing spectral measures, time series generated by a Hamiltonian simulation, and thermal averages of the energy. We implement these linear algebra operations on IBM Q quantum devices as well as quantum virtual machines, and demonstrate their performance in solving scientific computing problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Quantum Phases of Transition Metal Dichalcogenide Moiré Systems

Moiré systems provide a rich platform for studies of strong correlation physics. Recent experiments on heterobilayer transition metal dichalcogenide Moiré systems are exciting in that they manifest a relatively simple model system of an extended Hubbard model on a triangular lattice. Inspired by the prospect of the hetero-transition metal dichalcogenide Moiré system’s potential as a solid-state-based quantum simulator, we explore the extended Hubbard model on the triangular lattice using the density matrix renormalization group. Specifically, we explore the two-dimensional phase space spanned by the key tuning parameters in the extended Hubbard model, namely, the kinetic energy strength and the further-range Coulomb interaction strengths. We find competition between Fermi fluid, chiral spin liquid, spin density wave, and charge order. In particular, our finding of the optimal further-range interaction for the chiral correlation presents a tantalizing possibility.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Skyrmion superconductivity: DMRG evidence for a topological route to superconductivity

It was recently suggested that the topology of magic-angle twisted bilayer graphene's (MATBG) flat bands could provide a novel mechanism for superconductivity distinct from both weakly coupled BCS theory and the d-wave phenomenology of the high-T c cuprates. In this work, we examine this possibility using a density matrix renormalization group (DMRG) study of a model which captures the essential features of MATBG's symmetry and topology. Here, using large-scale cylinder-DMRG calculations to obtain the ground state and its excitations as a function of the electron doping, we find clear evidence for superconductivity driven by the binding of electrons into charge-2e skyrmions. Remarkably, this binding is observed even in the regime where the unscreened Coulomb repulsion is by far the largest energy scale, demonstrating the robustness of this topological, all-electronic pairing mechanism.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

ThinCurr: An open-source 3D thin-wall eddy current modeling code for the analysis of large-scale systems of conducting structures

In this paper we present a new thin-wall eddy current modeling code, ThinCurr, for studying inductively-coupled currents in 3D conducting structures -- with primary application focused on the interaction between currents flowing in coils, plasma, and conducting structures of magnetically-confined plasma devices. The code utilizes a boundary finite element method on an unstructured, triangular grid to accurately capture device structures. The new code, part of the broader Open FUSION Toolkit, is open-source and designed for ease of use without sacrificing capability and speed through a combination of Python, Fortran, and C/C++ components. Scalability to large models is enabled through use of hierarchical off-diagonal low-rank compression of the inductance matrix, which is otherwise dense. Ease of handling large models of complicated geometry is further supported by automatic determination of supplemental elements through a greedy homology approach. Here, a detailed description of the numerical methods of the code and verification of the implementation of those methods using cross-code comparisons against the VALEN code and Ansys commercial analysis software is shown.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

A new woven composite constitutive model validated by shock wave experiments

In this paper, we present results of plate impact simulations of shock compressed woven glass fiber-reinforced plastic (GRP) performed using the Arbitrary Lagrangian–Eulerian three-dimensional finite element code. A hyperelastic large-strain-based empirical Continuum Damage Mechanics (CDM) formulation is employed to describe damage initiation and growth in the shock-compressed GRP. The model parameters calibration scheme utilizes the Velocity Interferometer System for Any Reflector normal particle velocity measurements at the free surface of the GRP target plates. The impact velocity in the experiments ranged from 8.5 to 418 m/s. The finite element model considered planar 0°/90° bidirectional plies with an individual ply thickness of 0.68 mm, stacked to reach a total laminate thickness of 6.8 mm. The anisotropic elastic strains were estimated from the experimentally determined tetragonal symmetry stiffness matrix for the GRP. The strain-based damage model captures several salient features observed in the measured free surface particle wave profiles, including the shock rise time, onset of Elastic—Elastic Cracking, and the shape of the nonlinear portion of the experimental particle velocity profiles. Furthermore, the CDM model predicts the dominant damage mode to be matrix microcracking due to shear and the associated bulk expansion (bulking) under the global compressive loading in the plate impact configuration.

42 ENGINEERING↗
DOE OSTI2021

Isospin mixing and the cubic isobaric multiplet mass equation in the lowest T=2, A=32 quintet

The isobaric multiplet mass equation (IMME) is known to break down in the first T=2,A=32 isospin quintet. In this work we combine high-resolution experimental data with state-of-the-art shell-model calculations to investigate isospin mixing as a possible cause for this violation. The experimental data are used to validate isospin-mixing matrix elements calculated with newly developed shell-model Hamiltonians. Our analysis shows that isospin mixing with nonanalog T=1 states contributes to the IMME breakdown, making the requirement of an anomalous cubic term inevitable for the multiplet.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

TAUOLA update for decay channels with e + e – pairs in the final state

With the arrival of high luminosity B-factories like the Belle II experiment, τ decay measurements have become more precise than ever, allowing rarer processes to be explored, and finer details of τ decays to be studied. These are important to understand the spectrum of intermediate particles produced in τ decays. Therefore Monte Carlo generators, like the TAUOLA program, have to facilitate precision analysis as well as confront new models that constantly emerge with the availability of high statistics experimental data. New decay channels and models may lead to large variation of matrix elements size within the available phase space, as in the case when e + e - pairs are present in final state. It requires appropriate presampler of the phase space generator and a proper documentation to help users introduce their own models. While releasing a new update, it is important for the TAUOLA Monte Carlo library to maintain the general structure of the previous versions to preserve backward compatibility. The aim is to minimize changes from the user perspective. Finally, this paper presents a demonstration of new models implementation facilitated by the current update.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

SAM Enhancements and Model Developments for Molten-Salt-Fueled Reactors

To support the development and utilization of the SAM code for molten-salt-fueled reactor (MSR) safety analysis and licensing, an effort was devoted to enhancing code capabilities and developing reference models for the MSR primary loop. A reference standard problem of a prototypical reactor design is foundational to NRC to verify the adequacy of computer codes and evaluation models for a specific reactor type. A thermo-fluid model of the Molten Salt Reactor Experiment (MSRE) has been developed. The MSRE primary loop model consists of a 2-D core region and external core components in 1-D or 0-D. Both the steady-state and a transient scenario are simulated. The porous medium model is utilized for both the molten salt fluid channels and the moderator matrix in the MSRE core. The use of the porous medium model for the MSRE core is verified with the higher-fidelity simulation results using 1-D representations of the fluid channels and 3-D modeling of core structures. To further enhance SAM multi-scale simulation capabilities for MSRs, a mass transport model is developed and implemented in SAM multi-dimension flow module for modeling of species transport such as delayed neutron precursors in MSRs.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Is the Matrix Completion of Reduced Density Matrices Unique?

Reduced density matrices are central to describing observables in many-body quantum systems. In electronic structure theory, the two-particle reduced density matrix (2-RDM) suffices to determine the energy and other key properties. Recent work has used matrix completion, leveraging the low-rank structure of RDMs and approximate theoretical models, to reconstruct the 2-RDM from partial data and thus reduce the computational cost. However, matrix completion is, in general, an under-determined problem. Revisiting Rosina’s theorem (Rosina, M. Queen’s Papers on Pure and Applied Mathematics , 1968, No. 11, 369), we here show that the matrix completion is unique under certain conditions, identifying the subset of 2-RDM elements that enables its exact reconstruction from incomplete information. Building on this, we introduce a hybrid quantum–stochastic algorithm that achieves exact matrix completion, demonstrated through applications to the Fermi–Hubbard model.

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

Machine-Learning-based Algorithms for Automated Image Segmentation Techniques of Transmission X-ray Microscopy (TXM)

Four state-of-the-art Deep Learning-based Convolutional Neural Networks (DCNN) were applied to automate the semantic segmentation of a 3D Transmission x-ray Microscopy (TXM) nanotomography image data. The standard U-Net architecture as baseline along with UNet++, PSPNet, and DeepLab v3+ networks were trained to segment the microstructural features of an AA7075 micropillar. A workflow was established to evaluate and compare the DCNN prediction dataset with the manually segmented features using the Intersection of Union (IoU) scores, time of training, confusion matrix, and visual assessment. Comparing all model segmentation accuracy metrics, it was found that using pre-trained models as a backbone along with appropriate training encoder-decoder architecture of the Unet++ can robustly handle large volumes of x-ray radiographic images in a reasonable amount of time. This opens a new window for handling accurate and efficient image segmentation of in situ time-dependent 4D x-ray microscopy experimental datasets.

36 MATERIALS SCIENCE↗