Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “statistical physics”

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 865 records · Page 48

Simulation and Emulation of X-Ray Diffraction from Dynamic Compression Experiments

Many important aspects of the dynamic thermo-mechanical response of materials occur at the mesoscale, i.e. a physical scale of interactions smaller than what can be adequately described by homogenous behaviors, yet larger than the scale of the atomic lattice. Concurrent advancements in computational power, continuum theory, and experimental diagnostics are enabling unprecedented understanding of such interactions. However, we cannot develop a sufficient level of confidence in such mesoscale capability until the constitutive description of the underlying constituents is reliably representative of their actual physical behavior. Therefore, there is a strong need to combine experimental, modeling, and data-science techniques to validate models of the thermomechanical response of individual single crystals. One experimental diagnostic with high potential impact to shock physics and materials science is in-situ x-ray diffraction. This paper is primarily focused on simulation of x-ray diffraction in shock physics, but with an aim toward quantifying parametric uncertainty of simulation models. Here, we develop and demonstrate a data-science and model-driven approach to constrain the parameterization of continuum models of crystal lattice deformation associated with the shock response of crystalline materials. The framework is built around the connection between continuum hydrodynamic simulations of lattice deformation and a new Bragg diffraction simulation code, BarberShop. The dynamic deformation of a crystal lattice is modeled using the DiscoFlux model within an arbitrary Lagrangian-Eulerian hydrodynamic code, FLAG. These detailed continuum simulations of lattice deformation can be computationally slow, thus a statistical model is used to emulate the evolution of lattice deformation fields in time and across the considered model parameter space. Emulated lattice deformation fields can then be generated rapidly for any combination of physics model parameters. In turn, these fields can be fed into BarberShop to realize a rapid prediction of Bragg diffraction patterns associated with particular values of physics model parameters. The framework enables parameterization of the single crystal model to obtain Bragg diffraction patterns that most closely resemble a corresponding measurement. Furthermore, the framework naturally provides sensitivities of the lattice deformation to the physics parameters. We highlight the utility of this framework through the application to a synthetic closed-loop inverse problem leading to the parameterization of a single crystal material model. As a model problem, we consider the dynamic response of the energetic molecular crystal, cyclotrimethylenetrinitramine (or RDX), under dynamic compression induced by simulated flyer plate impact experiments.

36 MATERIALS SCIENCE↗

Nature of Zirconia on a Copper Inverse Catalyst Under CO 2 Hydrogenation Conditions

The growing concern over the escalating levels of anthropogenic CO 2 emissions necessitates effective strategies for its conversion to valuable chemicals and fuels. In this research, we embark on a comprehensive investigation of the nature of zirconia on a copper inverse catalyst under the conditions of CO 2 hydrogenation to methanol. We employ density functional theory calculations in combination with the Grand Canonical Basin Hopping method, enabling an exploration of the free energy surface including a variable amount of adsorbates within the relevant reaction conditions. Our focus centers on a model three-atom Zr cluster on a Cu(111) surface decorated with various OH, O, and formate ligands, noted Zr 3 O x (OH) y (HCOO) z /Cu(111), revealing major changes in the active site induced by various reaction parameters such as the gas pressure, temperature, conversion levels, and CO 2 /H 2 feed ratios. Further, through our analysis, we have unveiled insights into the dynamic behavior of the catalyst. Specifically, under reaction conditions, we observe a large number of composition and structures with similar free energy for the catalyst, with respect to changing the type, number, and binding sites of adsorbates, suggesting that the active site should be regarded as a statistical ensemble of diverse structures that interconvert.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Filtering, averaging, and scale dependency in homogeneous variable density turbulence

We investigate relationships between statistics obtained from filtering and from ensemble or Reynolds-averaging turbulence flow fields as a function of length scale. Generalized central moments in the filtering approach are expressed as inner products of generalized fluctuating quantities, q ' ( ξ , x ) = q ( ξ ) - q ¯ ( x ) , representing fluctuations of a field q ( ξ ) , at any point ξ, with respect to its filtered value at x. For positive-definite filter kernels, these expressions provide a scale-resolving framework, with statistics and realizability conditions at any length scale. In the small-scale limit, scale-resolving statistics become zero. In the large-scale limit, scale-resolving statistics and realizability conditions are the same as in the Reynolds-averaged description. Using direct numerical simulations (DNS) of homogeneous variable density turbulence, we diagnose Reynolds stresses, T i j , resolved kinetic energy, kr, turbulent mass-flux velocity, a i , and density-specific volume covariance, b, defined in the scale-resolving framework. These variables, and terms in their governing equations, vary smoothly between zero and their Reynolds-averaged definitions at the small and large scale limits, respectively. At intermediate scales, the governing equations exhibit interactions between terms that are not active in the Reynolds-averaged limit. For example, in the Reynolds-averaged limit, b follows a decaying process driven by a destruction term; at intermediate length scales, it is a balance between production, redistribution, destruction, and transport, where b grows as the density spectrum develops and then decays when mixing becomes strong enough. This work supports the notion of a generalized, length-scale adaptive model that converges to DNS at high resolutions and to Reynolds-averaged statistics at coarse resolutions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Automated detector simulation and reconstruction parametrization using machine learning

Rapidly applying the effects of detector response to physics objects (e.g. electrons, muons, showers of particles) is essential in high energy physics. Presently available tools for the transformation from truth-level physics objects to reconstructed detector-level physics objects involve manually defining resolution functions. These resolution functions are typically derived in bins of variables that are correlated with the resolution (e.g. pseudorapidity and transverse momentum). This process is time consuming, requires manual updates when detector conditions change, and can miss important correlations. Machine learning offers a way to automate the process of building these truth-to-reconstructed object transformations and can capture complex correlation for any given set of input variables. Such machine learning algorithms, with sufficient optimization, could have a wide range of applications: improving phenomenological studies by using a better detector representation, allowing for more efficient production of Geant4 simulation by only simulating events within an interesting part of phase space, and studies on future experimental sensitivity to new physics.

47 OTHER INSTRUMENTATION↗

Lanczos Algorithm, the Transfer Matrix, and the Signal-to-Noise Problem

This Letter introduces a method for determining the energy spectrum of lattice quantum chromodynamics by applying the Lanczos algorithm to the transfer matrix and using a bootstrap generalization of the Cullum-Willoughby method to filter out spurious eigenvalues. Proof-of-principle analyses of the simple harmonic oscillator and the lattice quantum chromodynamics proton mass demonstrate that this method provides faster ground-state convergence than the “effective mass,” which is related to the power-iteration algorithm. Lanczos provides more accurate energy estimates than multistate fits to correlation functions with small imaginary times while achieving comparable statistical precision. Two-sided error bounds are computed for Lanczos results and guarantee that excited-state effects cannot shift Lanczos results far outside their statistical uncertainties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

General Quantum Field Theory of Flavor Mixing and Oscillations

We review the canonical transformation in quantum physics known as the Bogoliubov transformation and present its application to the general theory of quantum field mixing and oscillations with an arbitrary number of mixed particles with either boson or fermion statistics. The mixing relations for quantum states are derived directly from the definition of mixing for quantum fields and the unitary inequivalence of the Fock space of energy and flavor eigenstates is shown by a straightforward algebraic method. The time dynamics of the interacting fields is then explicitly solved and the flavor oscillation formulas are derived in a unified general formulation with emphasis on antiparticle content and effect introduced by nontrivial flavor vacuum.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Neutrino Scattering in the NOvA Near Detector

aNOvA is a long-baseline accelerator-based neutrino experiment based in the USA. NOvA uses an intense neutrino beam produced at Fermilab’s accelerator complex to make physics measurements of neutrino oscillations, neutrino cross sections, and much more. For its physics goals, NOvA uses two functionally-identical detectors. The Near Detector (ND) is situated at Fermilab, 1 km from the neutrino target and the Far Detector (FD) is located at Ash River, MN, a distance of 810 km from the neutrino source. The ND receives a high statistics neutrino flux which gives a unique opportunity for high-precision neutrino cross-section measurements and is used as a control for the oscillation analyses. In this talk/poster, I will give an overview of the NOvA experiment. I will also talk about the current and future status of the NOvA’s cross-section physics program.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Probabilistic inference of the structure and orbit of Milky Way satellites with semi-analytic modelling

Semi-analytic modelling furnishes an efficient avenue for characterizing dark matter haloes associated with satellites of Milky Way-like systems, as it easily accounts for uncertainties arising from halo-to-halo variance, the orbital disruption of satellites, baryonic feedback, and the stellar-to-halo mass (SMHM) relation. We use the SatGen semi-analytic satellite generator, which incorporates both empirical models of the galaxy–halo connection as well as analytic prescriptions for the orbital evolution of these satellites after accretion onto a host to create large samples of Milky Way-like systems and their satellites. By selecting satellites in the sample that match observed properties of a particular dwarf galaxy, we can infer arbitrary properties of the satellite galaxy within the cold dark matter paradigm. For the Milky Way’s classical dwarfs, we provide inferred values (with associated uncertainties) for the maximum circular velocity v max and the radius r max at which it occurs, varying over two choices of baryonic feedback model and two prescriptions for the SMHM relation. While simple empirical scaling relations can recover the median inferred value for v max and r max , this approach provides realistic correlated uncertainties and aids interpretability. We also demonstrate how the internal properties of a satellite’s dark matter profile correlate with its orbit, and we show that it is difficult to reproduce observations of the Fornax dwarf without strong baryonic feedback. Furthermore, the technique developed in this work is flexible in its application of observational data and can leverage arbitrary information about the satellite galaxies to make inferences about their dark matter haloes and population statistics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Next-generation even-denominator fractional quantum Hall states of interacting composite fermions

The discovery of the fractional quantum Hall state (FQHS) in 1982 ushered a new era of research in many-body condensed matter physics. Among the numerous FQHSs, those observed at even-denominator Landau level filling factors are of particular interest as they may host quasiparticles obeying non-Abelian statistics and be of potential use in topological quantum computing. The even-denominator FQHSs, however, are scarce and have been observed predominantly in low-disorder two-dimensional (2D) systems when an excited electron Landau level is half filled. An example is the well-studied FQHS at filling factor ν = 5/2 which is believed to be a Bardeen-Cooper-Schrieffer-type, paired state of flux-particle composite fermions (CFs). Here, we report the observation of even-denominator FQHSs at ν = 3/10, 3/8, and 3/4 in the lowest Landau level of an ultrahigh-quality GaAs 2D hole system, evinced by deep minima in longitudinal resistance and developing quantized Hall plateaus. Quite remarkably, these states can be interpreted as even-denominator FQHSs of CFs, emerging from pairing of higher-order CFs when a CF Landau level, rather than an electron or a hole Landau level, is half-filled. Our results affirm enhanced interaction between CFs in a hole system with significant Landau level mixing and, more generally, the pairing of CFs as a valid mechanism for even-denominator FQHSs, and suggest the realization of FQHSs with non-Abelian anyons.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Automated Discovery of DOminaNt physics Informed Surrogates (ADDONIS) Framework for Improving Water Cycling Predictability

Focal Areas: The primary alignment of the proposed framework is with focal area 2, because it guides the construction of a hierarchy of predictive models generated from Statistical and AI/ ML techniques. There are connections to focal areas 1 and 3 because surrogates allow for coupled data assimilation techniques where the full model is too expensive (1) and the discovered balance laws can be used as an insightful analysis tool to validate the structure of models (3).

54 ENVIRONMENTAL SCIENCES↗

Getting lost tracking the carbon footprint of hydropower

In the transition to low-carbon electricity, well-quantified estimates of carbon dynamics are needed to ensure that emissions reduction targets are achieved. Here we review the state of the science on carbon accounting for hydropower reservoirs and identify limitations and future solutions. Nearly all research on reservoir greenhouse-gas (GHG) emissions has focused on individual reservoirs in isolation without considering their position in a freshwater network draining organic matter from upstream watersheds or the coordinated operation of reservoir cascades. Second, carbon inventories have extrapolated from a small, non-probabilistic sample of highly variable measurements of GHG emissions to unsampled reservoirs. A stronger statistical foundation is needed to estimate a global inventory and its uncertainty. Third, attribution to hydropower is based on ranks assigned to reservoir purpose. Instead, the physical influence of hydropower on carbon dynamics could be directly measured. Fourth, current carbon-accounting practices neglect time. A time-varying approach would quantify variation in emissions for electricity portfolios from changes in the fuel mix at different times and account for ancillary services, i.e., the ability to support the grid when variable renewables are not available without using natural gas. Reservoirs also sequester a significant portion of inflowing carbon in sediments and slow the carbon cycle by delaying the return of carbon to the atmosphere for decades to centuries. Together, these refinements would help to illuminate pathways toward meeting energy demand with the longest-possible delay in returning carbon to the atmosphere and without adding ancient sources to the pool of carbon cycling through aquatic ecosystems.

13 HYDRO ENERGY↗

Bayesian stability and force modeling for uncertain machining processes

Accurately simulating machining operations requires knowledge of the cutting force model and system frequency response. However, this data is collected using specialized instruments in an ex-situ manner. Bayesian statistical methods instead learn the system parameters using cutting test data, but to date, these approaches have only considered milling stability. This paper presents a physics-based Bayesian framework which incorporates both spindle power and milling stability. Initial probabilistic descriptions of the system parameters are propagated through a set of physics functions to form probabilistic predictions about the milling process. The system parameters are then updated using automatically selected cutting tests to reduce parameter uncertainty and identify more productive cutting conditions, where spindle power measurements are used to learn the cutting force model. The framework is demonstrated through both numerical and experimental case studies. Results show that the approach accurately identifies both the system natural frequency and cutting force model.

42 ENGINEERING↗

Machine learning methods for probabilistic locked-mode predictors in tokamak plasmas

A rotating tokamak plasma can interact resonantly with the external helical magnetic perturbations, also known as error fields. This can lead to locking and then to disruptions. We leverage machine learning (ML) methods to predict the locking events. We use a coupled third-order nonlinear ordinary differential equation model to represent the interaction of the magnetic perturbation and the plasma rotation with the error field. This model is sufficient to describe qualitatively the locking and unlocking bifurcations. Here, we explore using ML algorithms with the simulation data and experimental data, focusing on the methods that can be used with sparse datasets. These methods lead to the possibility of the avoidance of locking in real-time operations. We describe the operational space in terms of two control parameters: the magnitude of the error field and the rotation frequency associated with the momentum source that maintains the plasma rotation. The outcomes are quan- tified by order parameters that completely characterize the state, whether locked or unlocked. We use unsupervised ML methods to classify locked/unlocked states and note the usefulness of a certain normalization of the order parameters. Three supervised ML classifiers are used in suite to estimate the probability of locking in the region of control parameter space with hysteresis, i.e., the set of control parameters for which both locked and unlocked states can exist. The results show that a neural network gives the best estimate of the locking probability. An analogy of the present locking model with the van der Waals equation of state is also provided.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Molecular dynamics simulation of metallic Al–Ce liquids using a neural network machine learning interatomic potential

Al-rich Al-Ce alloys have the possibility of replacing heavier steel and cast-irons for use in high-temperature applications. Knowledge about the structures and properties of Al-Ce alloys at liquid state is vital for optimizing the manufacture process to produce desired allows. However, reliable molecular dynamics simulation of Al-Ce alloy systems remains a great challenge due to the lack of accurate Al-Ce interatomic potential. In this work, an artificial neural network (ANN) deep machine learning (ML) method is used to develop a reliable interatomic potential for Al-Ce alloy. Ab initio molecular dynamics (AIMD) simulation data on Al-Ce liquid with small unit cell (~200 atoms) and on the known Al-Ce crystalline compounds are collected to train the interatomic potential using ANN-ML. The obtained ANN-ML model reproduces well the energies, forces, and atomic structure of Al 90 Ce 10 liquid and crystalline phases of Al-Ce compounds in comparison with ab initio results. The developed ANN-ML potential is applied in molecular dynamics simulations to study the structures and properties of metallic Al 90 Ce 10 liquid, which would provide useful insight for guiding experimental process to produce desired Al-Ce allows.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Small angle scattering of diblock copolymers profiled by machine learning

We outline a machine learning strategy for quantitively determining the conformation of AB-type diblock copolymers with excluded volume effects using small angle scattering. Complemented by computer simulations, a correlation matrix connecting conformations of different copolymers according to their scattering features is established on the mathematical framework of a Gaussian process, a multivariate extension of the familiar univariate Gaussian distribution. We show that the relevant conformational characteristics of copolymers can be probabilistically inferred from their coherent scattering cross sections without any restriction imposed by model assumptions. This work not only facilitates the quantitative structural analysis of copolymer solutions but also provides the reliable benchmarking for the related theoretical development of scattering functions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Replica symmetry breaking for the integrable two-site Sachdev–Ye–Kitaev model

We analyze a two-body non-Hermitian two-site Sachdev–Ye–Kitaev (SYK) model with the couplings of one site complex conjugated to the other site. This model, with no explicit coupling between the sites, shows an infinite number of second-order phase transitions, which is a consequence of the factorization of the partition function into a product over Matsubara frequencies. We calculate the quenched free energy in two different ways: first in terms of the single-particle energies and second by solving the Schwinger–Dyson equations of the two-site model. The first calculation can be done entirely in terms of a one-site model. The conjugate replica enters due to non-analyticities when Matsubara frequencies enter the spectral support of the coupling matrix. The second calculation is based on the replica trick of the two-site partition function. Both methods give the same result. The free-fermion partition function can also be rephrased as a matrix model for the coupling matrix. Up to minor details, this model is the random matrix model that describes the chiral phase transition of QCD, and the order parameter of the two-body model corresponds to the chiral condensate of QCD. Comparing to the corresponding four-body model, we are able to determine which features of the free energy are due to the chaotic nature of the four-body model. The high-temperature phase of both models is entropy dominated, and in both cases, the free energy is determined by the spectral density. The chaotic four-body SYK model has a low-temperature phase whose free energy is almost temperature-independent, signaling an effective gap of the theory even though the actual spectrum does not exhibit a gap. On the other hand, the low-temperature free energy of the two-body SYK model is not flat; in fact, it oscillates to arbitrarily low temperature. This indicates a less desirable feature that the entropy of the two-body model is not always positive in the low-temperature phase, which most likely is a consequence of the non-hermiticity.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

X-ray continuum spectroscopy of inertial confinement fusion implosions at the National Ignition Facility

A methodology for measuring x-ray continuum spectra of inertial confinement fusion (ICF) implosions is described. The method relies on the use of ConSpec, a high-throughput spectrometer using a highly annealed pyrolytic graphite crystal , which measures the spectra in the ≃20–30 keV range. Due to its conical shape, the crystal is sagittally focusing a Bragg-reflected x-ray spectrum into a line, which enhances the recorded x-ray emission signal above the high neutron-induced background accompanying ICF implosions at the National Ignition Facility. To improve the overall measurement accuracy, the sensitivity of the spectrometer measured in an off-line x-ray laboratory setting was revised. The error analysis was expanded to include the accuracy of the off-line measurements, the effect of the neutron-induced background, as well as the influence of possible errors in alignment of the instrument to the ICF target. Here we demonstrate how the improved methodology is applied in the analysis of ConSpec data with examples of a relatively low-neutron-yield implosion using a tritium–hydrogen–deuterium mix as a fuel and a high-yield deuterium–tritium (DT) implosion producing high level of the background. In both cases, the shape of the measured spectrum agrees with the exponentially decaying spectral shape of bremsstrahlung emission to within ±10%. In the case of the high-yield DT experiment, non-monotonic deviations slightly exceeding the measurement uncertainties are observed and discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Interpretation of autoencoder-learned collective variables using Morse–Smale complex and sublevelset persistent homology: An application on molecular trajectories

Dimensionality reduction often serves as the first step toward a minimalist understanding of physical systems as well as the accelerated simulations of them. In particular, neural network-based nonlinear dimensionality reduction methods, such as autoencoders, have shown promising outcomes in uncovering collective variables (CVs). However, the physical meaning of these CVs remains largely elusive. In this work, we constructed a framework that (1) determines the optimal number of CVs needed to capture the essential molecular motions using an ensemble of hierarchical autoencoders and (2) provides topology-based interpretations to the autoencoder-learned CVs with Morse–Smale complex and sublevelset persistent homology. Furthermore, this approach was exemplified using a series of n-alkanes and can be regarded as a general, explainable nonlinear dimensionality reduction method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗