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At least 127 records · Page 7

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DOE OSTI2021

Surface prediction and measurement for modulated tool path (MTP) turning

Here, this paper describes a time-domain simulation for predicting surface finish in modulated tool path (MTP) turning, which uses sinusoidal axis motions in the feed direction to produce discontinuous chips for ductile workpiece materials. The simulation includes: the low frequency and low amplitude tool oscillation in the feed direction; the time-varying chip thickness, cutting force, and tool displacement; and the plastic side flow effect used to calibrate the effective tool nose radius. Comparisons between predicted and measured surface profiles are presented for turning a 6061-T6 aluminum cylinder as a function of the MTP oscillation frequency and amplitude with discontinuous chip formation.

42 ENGINEERING↗

DOE OSTI2025

Machine learning-accelerated path integral molecular dynamics simulations of reactive organic electrolytes

Hydrogen bonded electrolytes that exhibit accelerated proton transport via sequential reactive hops have drawn interest for their promise in clean energy applications. Molecular dynamics simulations of these electrolytes offer the opportunity to uncover microscopic mechanistic details that could be used to design and tune the properties of candidate electrolyte technologies. However, accurately modeling the proton transfer reactions and transport properties that give rise to high charge conductivites in these electrolytes proves computationally challenging because of the need to perform lengthy condensed phase simulations, treating both the electronic and nuclear degrees of freedom quantum mechanically. In this paper, we demonstrate that such a modeling task can be efficiently achieved with the use of density functional theory (DFT)-trained machine learning potentials (MLP) to accelerate path integral molecular dynamics (PIMD) simulations. We highlight the practical utility of this approach by using it to benchmark how closely PIMD simulations employing different DFT exchange–correlation functionals reproduce the composition-dependent densities, diffusion coefficients, and electrical conductivities of mixtures consisting of imidazole and levulinic acid. Even with the speedup afforded by our MLPs, PIMD simulations remain quite expensive. Furthermore, in order to render PIMD more computationally tractable, we introduce and benchmark the accuracy of a ring polymer contraction approach that leverages a computationally efficient short-range MLP to accelerate our PIMD simulations by an additional factor of four.

Chemical bonding↗

DOE OSTI2021

Quantification of air leakage paths: a comparison of airflow and acoustic measurements

Today, the fan pressurization method is the most frequently used method to evaluate a building's airtightness. However, the localization and quantification of leaks remain difficult. In this paper, an acoustic method is introduced to estimate the leakage size of single leaks. Acoustic and airflow measurements were conducted and compared in laboratory tests within the same boundary conditions. This work aims to investigate if various leak sizes can be predicted using acoustic measurement methods. The test apparatus consists of two chambers, separated by a test wall. This wall represents a single characteristic air leakage path. Various types of wall structures with different slit geometries, wall thicknesses, and insulation materials were investigated. The acoustic measurements were performed with a sound source placed in one chamber and ultrasonic microphones located in both chambers. These results were compared to measured airflows through the test wall to provide estimates of uncertainty in the acoustic approach, which indicate a linear trend. Finally, these laboratory measurements were compared to the same measurements at a real office building. Finally, although the acoustic measurement uncertainty is still significant (greater than +/- 50%), the acoustic method has the potential to give an order of magnitude of single leak sizes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Pairwise connected tensor network representation of path integrals

It has been recently shown how the tensorial nature of real-time path integrals (PIs) involving the Feynman-Vernon influence functional can be utilized with matrix product states, taking advantage of the finite length of the bath-induced memory. Tensor networks (TNs) promise to provide a unified language to express the structure of a PI. A generalized TN specifically incorporating the pairwise interaction structure of the influence functional and its invariance with respect to the average forward-backward position or the sojourn value in the form of the blip representation is derived and implemented. This pairwise connected TNPI (PC-TNPI) is illustrated through applications to typical spin-boson problems and explorations of the differences caused by the exact form of the spectral density. The storage and performance scalings are reported, showing the compactness of the representation and the efficiency of the contraction process. Finally, taking advantage of the compressed representation, the viability of using PC-TNPI for simulating multistate problems is demonstrated. The PC-TNPI structure can be shown to yield other TN algorithms currently in use. Consequently, it should be possible to use it as a starting point for deriving other optimized procedures.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Path integral approach to quantum anomalies in interacting models

The prediction and subsequent discovery of topological semimetal phases of matter in solid state systems has instigated a surge of activity investigating the exotic properties of these unusual materials. Among these are transport signatures which can be attributed to the chiral anomaly; the breaking of classical chiral symmetry in a quantum theory. This remarkable quantum phenomenon, first discovered in the context of particle physics has now found new life in condensed-matter physics, connecting topological quantum matter and band theory with effective field theoretic models. In this paper we investigate the interplay between interactions and the chiral anomaly in field theories inspired by semimetals using Fujikawa's path integral method. Starting from models in one spatial dimension we discuss how the presence of interactions can affect the consequences of the chiral anomaly leading to renormalization of excitations and their transport properties. This is then generalized to the three-dimensional case where we show that the anomalous response of the system, namely, the chiral magnetic and quantum Hall effects, are modified by the presence of interactions. These properties are investigated further through the identification of anomalous modes which exist within interacting Weyl semimetals. These massive excitations are nonperturbative in nature and are a direct consequence of the chiral anomaly. The effects of interactions on mixed axial-gravitational anomalies are then investigated and the conditions required for interaction effects to be observed are discussed.

Anomalies↗

DOE OSTI2025

Advancements in Constitutive Model Calibration: Leveraging the Power of Full‐Field DIC Measurements and In Situ Load Path Selection for Reliable Parameter Inference

Accurate material characterization and model calibration are essential for computationally supported high-consequence engineering decisions. Historically, characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data are collected for a specific model of interest, (3) use deterministic methods that provide best-fit parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work brings together several recent advancements into an improved workflow called interlaced characterization and calibration (ICC) that advances the state-of-the-art in constitutive model calibration. The ICC paradigm (1) employs tools to efficiently use full-field data to calibrate high-fidelity material models, (2) aligns the data needed with the data collected by adopting an optimal experimental design protocol, (3) quantifies parameter uncertainty through Bayesian inference and (4) incorporates these advancements into a quasi real-time feedback loop. The ICC framework is demonstrated here on the calibration of a material model using simulated full-field data for an aluminium cruciform specimen being deformed biaxially. The cruciform is actively driven through the myopically preferred load path using Bayesian optimal experimental design, which selects load steps that yield the maximum expected information gain (EIG). Principal component analysis (PCA) is performed on the model predictions of full-field displacements, and fast surrogate models are built to approximate the input-output relationships of the expensive finite element model. Furthermore, the tools developed and demonstrated here show that high-fidelity constitutive models can be efficiently and reliably calibrated with quantified uncertainty, thus supporting credible decision-making and potentially increasing the agility of solid mechanics modelling by enabling utilization of computational simulations at earlier stages of the design cycle.

Bayesian optimal experimental design↗

DOE OSTI2023

A path integral ground state Monte Carlo algorithm for entanglement of lattice bosons

A ground state path integral quantum Monte Carlo algorithm is introduced that allows for the study of entanglement in lattice bosons at zero temperature. The Rényi entanglement entropy between spatial subregions is explored across the phase diagram of the one dimensional Bose-Hubbard model for systems consisting of up to L=256 L = 256 sites at unit-filling without any restrictions on site occupancy, far beyond the reach of exact diagonalization. The favorable scaling of the algorithm is demonstrated through a further measurement of the Rényi entanglement entropy at the two dimensional superfluid-insulator critical point for large system sizes, confirming the existence of the expected entanglement boundary law in the ground state. The Rényi estimator is extended to measure the symmetry resolved entanglement that is operationally accessible as a resource for experimentally relevant lattice gases with fixed total particle number.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

MWRRETV2 Value-Added Product Report: The Retrieval of Liquid Water Path and Precipitable Water Vapor from Microwave Radiometer – 3-Channel (MWR3C) Data Sets

This report provides a short description of the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Microwave Radiometer – 3-Channel (MWR3C) Retrieval (MWRRETV2) Value-Added Product (VAP) algorithm. MWRRETV2 is an updated version of MWRRET, which extends the physical-iterative retrieval of liquid water path (LWP) and precipitable water vapor PWV) to 3-channel microwave radiometers beyond ARM's original standard 2-channel (23-and 31-GHz) systems. The 89-GHz channel in the 3-channel microwave radiometers has approximately three times the sensitivity to liquid water as the 31-GHz channel, thus improving LWP retrieval accuracy when LWP is less than 100 g/m2. We present a general overview of the technique, input parameters, and output products, and describe data quality checks.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Persistent Homology Metrics Reveal Quantum Fluctuations and Reactive Atoms in Path Integral Dynamics

Nuclear quantum effects (NQEs) are known to impact a number of features associated with chemical reactivity and physicochemical properties, particularly for light atoms and at low temperatures. In the imaginary time path integral formalism, each atom is mapped onto a “ring polymer” whose spread is related to the quantum mechanical uncertainty in the particle’s position, i.e., its thermal wavelength. A number of metrics have previously been used to investigate and characterize this spread and explain effects arising from quantum delocalization, zero-point energy, and tunneling. Many of these shape metrics consider just the instantaneous structure of the ring polymers. However, given the significant interest in methods such as centroid molecular dynamics and ring polymer molecular dynamics that link the molecular dynamics of these ring polymers to real time properties, there exists significant opportunity to exploit metrics that also allow for the study of the fluctuations of the atom delocalization in time. Here we consider the ring polymer delocalization from the perspective of computational topology, specifically persistent homology, which describes the 3-dimensional arrangement of point cloud data, (i.e. atomic positions). We employ the Betti sequence probability distribution to define the ensemble of shapes adopted by the ring polymer. The Wasserstein distances of Betti sequences adjacent in time are used to characterize fluctuations in shape, where the Fourier transform and associated principal components provides added information differentiating atoms with different NQEs based on their dynamic properties. We demonstrate this methodology on two representative systems, a glassy system consisting of two atom types with dramatically different de Broglie thermal wavelengths, and ab initio molecular dynamics simulation of an aqueous 4 M HCl solution where the H-atoms are differentiated based on their participation in proton transfer reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Integrated Micro‐Scale Concentrating Photovoltaics: A Scalable Path Toward High‐Efficiency, Low‐Cost Solar Power

The global energy market is seeing increases in the electricity demand of a couple of percentage points annually. The photovoltaic (PV) industry is also growing rapidly every year. One of the PV technologies is concentrator photovoltaics (CPV). CPV uses high‐efficiency multijunction solar cells and optics to concentrate sunlight, thereby significantly reducing the amount of semiconductor material needed. Yet, due to the high upfont manufacturing cost of CPV, it currently does not offer a competitive price against silicon PV. With this a new branch is introduced to the industry, micro‐CPV, which can be broadly explained as the miniaturization of the solar cells and optical components. The motivation for micro‐CPV is lowering the cost by decreasing the material volume and enabling new system architectures and high‐throughput manufacturing methods, while still maintaining high electrical efficiencies, taking advantage of a lower thermal load, shorter optical paths, lower resistive losses, and lower material volumes. Herein, a comprehensive review of the technological advances is presented, key synergies between micro‐CPV and other industries sharing similar challenges are identified, exemplified by micro‐light emitting diodes display manufacturing. New assembly process development in these industries will facilitate commercial adoption of micro‐CPV with continued miniaturization while driving down the cost.

Jost, Norman↗

DOE OSTI2026

Physical instabilities and the phase of the Euclidean path integral

We compute the phase of the Euclidean gravity partition function on manifolds of the form S p × M q . We find that the total phase is equal to the phase in pure gravity on S p times an extra phase that arises from negative mass squared fields that we obtain when we perform a Kaluza-Klein reduction to S p . The latter can be matched to the phase expected for physical negative modes seen by a static path observer in dS p . In the case of S p × S q the answer can be interpreted in terms of a computation in the static patch of dS p or dS q . We also provide the phase when we have a product of many spheres. We clarify the procedure for determining the precise phase factor. We discuss some aspects of the interpretation of this phase.

Models of Quantum Gravity↗

DOE OSTI2025

The gravitational path integral from an observer’s point of view

One of the fundamental problems in quantum gravity is to describe the experience of a gravitating observer in generic spacetimes. In this paper, we develop a framework for describing non-perturbative physics relative to an observer using the gravitational path integral. We apply our proposal to an observer that lives in a closed universe and one that falls behind a black hole horizon. We find that the Hilbert space that describes the experience of the observer is much larger than the Hilbert space in the absence of an observer. In the case of closed universes, the Hilbert space is not one-dimensional, as calculations in the absence of the observer suggest. Rather, its dimension scales exponentially with ${G}_{N}^{-1}$. Similarly, from an observer’s perspective, the dimension of the Hilbert space in a two-sided black hole is increased. We compute various observables probing the experience of a gravitating observer in this Hilbert space. We find that an observer experiences non-trivial physics in the closed universe in contrast to what it would see in a one-dimensional Hilbert space. In the two-sided black hole setting, our proposal implies that non-perturbative corrections to effective field theory for an infalling observer are suppressed until times exponential in the black hole entropy, resolving a recently-raised puzzle in black hole physics. While the framework that we develop is exemplified in the toy-model of JT gravity, most of our analysis can be extended to higher dimensions and, in particular, to generic spacetimes not admitting a conventional holographic description, such as cosmological universes or black hole interiors.

2D gravity↗

DOE OSTI2022

3D Time-Lapse Electrical Resistivity Imaging of Rock Damage Patterns and Gas Flow Paths Resulting from Two Underground Chemical Explosions

Abstract Rock damage from underground nuclear explosions (UNEs) has a strong influence on sub-surface gas movement and on seismic waveform characteristics, both of which are used to detect UNEs. Although advanced numerical simulation capabilities exist to predict rock damage patterns and corresponding detection signals, those predictions are dependent on (generally) unknown properties of the host rock. For example, the effects of in-situ mechanical heterogeneities on the explosively generated damage/fractures that provide gas flow pathways to the surface are not well understood, due largely to the difficulty in accessing and characterizing the near-source region. In this paper we demonstrate the emerging use of electrical resistivity tomography (ERT) for imaging rock damage and gas flow patterns resulting from two relatively small-scale underground chemical explosions. Pre-explosion ERT and crosshole seismic imaging revealed a natural fracture zone within the test bed. Post-explosion imaging revealed that the damage zone was non-symmetric and was focused primarily within the pre-existing fracture zone, located 10 m above the first explosion and 5 m above the second explosion. Time-lapse ERT imaging of heated air injected into the detonation borehole revealed the primary gas flow paths to be within the upper margin of the same primary damage zone. These results point to the utility of ERT imaging for understanding rock damage and gas flow patterns under experimental conditions, and to the importance of understanding the effects of geologic heterogeneity on UNE detection signals, particularly gas surface breakthrough times.

58 GEOSCIENCES↗

DOE OSTI2026

Thermophysical Properties of Liquid Tritium: A Path Integral Monte Carlo Study

Here, we present worm-algorithm, path integral Monte Carlo simulations of bulk liquid tritium. The simulations are benchmarked against empirically known thermophysical properties of liquid deuterium and liquid tritium. Results for the pair correlation function, chemical potential, isothermal compressibility, isochoric heat capacity, and single-particle momentum distributions are reported. Given the benchmark comparisons, our predictions of liquid tritium properties are expected to be accurate to within a few percent. Our simulations unambiguously demonstrate the significance of nuclear quantum effects to the properties of liquid tritium. In particular, under saturated vapor pressure, the average molecular kinetic energy of the liquid is found to be more than 60% higher than the value expected from the classical equipartition theorem.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Bettye Washington Greene: An Industrial Chemist and Inventor Who Lit a Path for Innovation

American civil rights pioneer John Lewis once wrote: “You are a light. You are the light. Never let anyone— any person or any force—dampen, dim or diminish your light.” At a time when the civil rights movement was shedding light on inequalities for African Americans, Dr. Bettye Washington Greene (March 20, 1935 – June 16, 1995) was forging a path that would light the way–figuratively and literally—for generations to come. She was one of the few African American women to earn a Ph.D. in chemistry in the 1960s, an era marked by the passing of the Voting Rights Act of 1965. Washington Greene’s doctoral thesis evaluated light-scattering methods to determine the size of particles, which influences molecular effectiveness, activity, and stability. The fundamental knowledge she provided critically aided the pharmaceutical, catalyst and cosmetics industries [1]. Following her doctoral defense, Bettye went on to become the first African American female employed in a scientific research role at the Dow Chemical Company, where her innovations were awarded three patents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Advanced Electrode Structures for Proton Exchange Membrane Fuel Cells: Current Status and Path Forward

Abstract Proton exchange membrane fuel cells (PEMFCs) have demonstrated their viability as a promising candidate for clean energy applications. However, performance of conventional PEMFC electrodes, especially the cathode electrode, suffers from low catalyst utilization and sluggish mass transport due to the randomly distributed components and tortuous transport pathways. Development of alternative architectures in which the electrode structure is controlled across a range of length scales provides a promising path toward overcoming these limitations. Here, we provide a comprehensive review of recent research and development of advanced electrode structures, organized by decreasing length-scale from the millimeter-scale to the nanometer-scale. Specifically, advanced electrode structures are categorized into five unique architectures for specific functions: (1) macro-patterned electrodes for enhanced macro-scale mass transport, (2) micro-patterned electrodes for enhanced micro-scale mass transport, (3) electrospun electrodes with fiber-based morphology for enhanced in-plane proton transport and through-plane O 2 transport, (4) enhanced-porosity electrodes for improved oxygen transport through selective inclusion of void space, and (5) catalyst film electrodes for elimination of carbon corrosion and ionomer poisoning. The PEMFC performance results achieved from each alternative electrode structure are presented and tabulated for comparison with conventional electrode architectures. Moreover, analysis of mechanisms by which new electrode structures can improve performance is presented and discussed. Finally, an overview of current limitations and future research needs is presented to guide the development of electrode structures for next generation PEMFCs. Graphical Abstract Development of improved electrode architectures with the control of structure on length scales ranging from millimeters to nanometers could enable a new generation of fuel cells with increased performance and reduced cost. This paper presents an in-depth review and critical analysis of recent developments and future outlook on the design of advanced electrode structures.

25 ENERGY STORAGE↗

DOE OSTI2022

Predicting path-dependent diffusion barrier spectra in vast compositional space of multi-principal element alloys via convolutional neural networks

The emergent multi-principal element alloys (MPEAs) provide a vast compositional space to search for novel materials for technological advances. How to screen promising compositions from such an ample design space for targeted properties is a grand challenge. In this report, we demonstrate the state-of-the-art deep learning technology—convolutional neural network (CNN)—in predicting path-dependent vacancy migration energy barrier spectra, which are critical to diffusion behavior and many high-temperature properties, in the hyperdimensional composition space of MPEAs. The developed CNN model, fully capturing local chemical features surrounding each vacancy, accurately and efficiently predicts migration energy barrier of MPEAs with different degrees of chemical short-range order and at any unseen compositions. By varying the size of the local region encapsulating vacancy in the CNN model, we reveal that the length scale influencing vacancy migration is surprisingly extensive, up to its six nearest neighboring shells. The efforts of the CNN model make it promising for developing a database of diffusion barriers for various MPEA systems, which would have profound implications for accelerating alloy screening and discovering new compositions with desirable properties.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Efficient bi-directional coupling of 3D computational fluid-particle dynamics and 1D Multiple Path Particle Dosimetry lung models for multiscale modeling of aerosol dosimetry

The development of predictive aerosol dosimetry models has been a major focus of environmental toxicology and pharmaceutical health research for decades. One-dimensional (1D) models successfully predict overall deposition averages but fail to accurately predict local deposition. Computational fluid-particle dynamics (CFPD) models provide site-specific predictions but at a computational cost that prohibits whole lung predictions. Thus, there is a need for developing multiscale strategies to provide a realistic subject-specific picture of the fate of inhaled aerosol in the lungs. CT-based 3D/CFPD models of the large airways were bidirectionally coupled with individualized 1D Navier-Stokes airflow and particle transport based upon the widely used Multiple Path Particle Dosimetry Model (MPPD). Distribution of airflows among lobes was adjusted by measured lobar volume changes observed in CT images between FRC and FRC + 1.5 L. Additionally, as a test of the effectiveness of the coupling procedures, deposition modeling of previous 1 µm aerosol exposure studies was performed. The complete coupled model was run for 3 breaths, with the computation-intense portion being the 3D CFD Lagrangian particle tracking calculation. The average deposition per breath was 11% in the combined multiscale model with site-specific doses available in the CFPD portion of the model and airway- or region-specific deposition available for the MPPD portion. In conclusion, the key methods developed in this study enable predictions of ventilation heterogeneities and aerosol deposition across the lungs that are not captured by 3D or 1D models alone. Overall, these methods can be used as the foundation for multi-scale modeling of the full respiratory system.

60 APPLIED LIFE SCIENCES↗