Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “STATISTICAL MECHANICS”

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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Guiding the Design of Heterogeneous Electrode Microstructures for Li-Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning

Electrochemical and mechanical properties of lithium-ion battery materials are heavily dependent on their 3D microstructure characteristics. A quantitative understanding of the role played by stochastic microstructures is critical for the prediction of material properties and for guiding synthesis processes. Furthermore, tailoring microstructure morphology is also a viable way of achieving optimal electrochemical and mechanical performances of lithium-ion cells. To facilitate the establishment of microstructure-resolved modeling and design methods, a review covering spatially and temporally resolved imaging of microstructure and electrochemical phenomena, microstructure statistical characterization and stochastic reconstruction, microstructure-resolved modeling for property prediction, and machine learning for microstructure design is presented here. The perspectives on the unresolved challenges and opportunities in applying experimental data, modeling, and machine learning to improve the understanding of materials and identify paths toward enhanced performance of lithium-ion cells are presented.

25 ENERGY STORAGE↗
DOE OSTI2025

Confinement-Driven Segregation Enables Glassy Polymer Hybrid Materials Featuring Disordered Hyperuniformity and Integrated Self-Healing

The blending of glassy copolymer-brush modified colloids with a viscoelastic linear copolymer featuring intrinsic self-healing enabled disordered hyperuniform hybrid materials that combined mechanical robustness with structural color, processability, environmental stability, and the ability to recover structure and properties after incurring physical damage via ‘integrated self-healing’. Symmetric linear n-butyl acrylate/methyl methacrylate (BA/MMA) were co-assembled with asymmetric glassy BA/MMA statistical copolymer brush (silica) particles. ‘Confinement-driven segregation’ resulted in a microphase-separated morphology in which the linear copolymer resided within the interstitial regions of a rigid (∼1 GPa) copolymer brush particle template with disordered hyperuniform microstructure. Diffusion of the self-healing copolymer additive into damage regions drove the recovery after damage, along with the restoration of structural color due to the materials hyperuniform microstructure. The synergistic action of intrinsic and extrinsic healing mechanisms could provide a versatile platform for the bottom-up fabrication of multifunctional hybrid materials with increased damage resistance and functional longevity.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Counterintuitive Compatibilization of Poly(-Valerolactone) and Poly(L-Lactic Acid) by Statistical Copolymers toward Compostable and Recyclable Packaging

Poly(δ-valerolactone) (PVL) and poly(l-lactic acid) (PLLA) are bioderivable, compostable, and chemically recyclable plastics with synergistic properties for addressing plastic waste accumulation in receiving environments. Though recyclable-by-design polymers often fall short of competing with incumbent materials, blending affords a means to leverage individual component strengths toward ideal tunable properties. Polymer blends are often immiscible, but a range of methodologies are available to promote mixing. Here, we report on the compatibilization of 9 immiscible PVL and PLLA blends with three different compatibilization agents: thermoplastic starch, synthesized PVL-co-PLLA statistical copolymers (SCPs), and synthesized PVL-b-PLLA block-type copolymers. Resulting degrees of compatibilization are observed through scanning electron microscopy, corroborated by thermal and mechanical analyses monitoring performance as a function of microdomain size. Small-angle and wide-angle X-ray scattering experiments are conducted to observe the influence of compatibilizers on individual crystalline phases to further elucidate material behavior. Molecular dynamics simulations provide key insights into the interfacial interactions between homopolymers and compatibilizers. Finally, a suite of end-of-life avenues is established by biodegradation in industrial composting conditions, chemical recycling by deconstruction to hydroxymethyl esters, and direct chemical depolymerization to lactone precursors in mixed feed. Overall, we highlight several promising blends and the counterintuitive SCP compatibilization phenomenon toward high-performance, sustainable materials.

Compatibilization, copolymer compatibilizer, bio-b↗
DOE OSTI2026

Learning turbulent flows with generative models for super resolution and sparse flow reconstruction

Neural operators are promising surrogates for dynamical systems but when trained with standard L 2 losses they tend to oversmooth fine-scale turbulent structures. Here, we show that combining operator learning with generative modeling overcomes this limitation. We consider three practical turbulent-flow challenges where conventional neural operators fail: spatio-temporal super-resolution, forecasting, and sparse flow reconstruction. For Schlieren jet super-resolution, an adversarially trained neural operator (adv-NO) reduces the energy-spectrum error by 15 × while preserving sharp gradients at neural operator-like inference cost. For 3D homogeneous isotropic turbulence, adv-NO trained on only 160 timesteps from a single trajectory forecasts accurately for five eddy-turnover times and offers 114 × wall-clock speed-up at inference than the baseline diffusion-based forecasters, enabling near-real-time rollouts. For reconstructing cylinder wake flows from highly sparse Particle Tracking Velocimetry-like inputs, a conditional generative model infers full 3D velocity and pressure fields with correct phase alignment and statistics. These advances enable accurate reconstruction and forecasting at low compute cost, bringing near-real-time analysis and control within reach in experimental and computational fluid mechanics.

Fluid dynamics↗
DOE OSTI2021

Bayesian inference for plasmonic nanometrology

Here, we introduce a Bayesian method for the characterization of plasmonic nanoparticles, which is applicable to both near- and far-field problems. Designed to combine data generated from any photon-plasmon interaction experiment with physically motivated theoretical models, our approach leverages state-of-the-art Markov chain Monte Carlo sampling techniques and returns parameter estimates on nanometric scales. Simulated spectral data sets, describing resonant scattering of photons from ellipsoidal and toroidal nanoparticles, are explored as concrete examples of our approach, with the resulting Bayesian estimates showing excellent agreement with the ground truth, even under conditions of high statistical noise. By incorporating Bayes factors into the method as well, we reveal how model selection can determine which one of competing geometric shapes better explains the observed data. Our comprehensive nanometrology procedure can be tailored to a variety of light-particle interaction models, and its reliance on Bayesian inference furnishes automatic uncertainty quantification. In addition to applicability to a host of plasmonic configurations such as nanoparticle dimers, trimers, and array studies, it is proposed that the presented analysis can be extended to the quantum regime, where nonclassical photon statistics may provide additional insight for inference of scatterer properties.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Statistical analysis on random quantum circuit sampling by Sycamore and Zuchongzhi quantum processors

Random quantum circuit sampling, a task to sample bit strings from a random quantum circuit, is considered a suitable benchmark task to demonstrate the outperformance of quantum computers even with noisy qubits. Recently, random quantum circuit sampling was performed on the Sycamore quantum processor with 53 qubits [Nature (London) 574, 505 (2019)] and on the Zuchongzhi quantum processor with 56 qubits [Phys. Rev. Lett. 127, 180501 (2021)]. Here, we analyze and compare the statistical properties of the outputs of the random quantum circuit sampling by the Sycamore and Zuchongzhi processors. Using the Marchenko-Pastur law of random matrices of bit strings and the Wasssertein distances between bit strings, we find that the statistical properties of Sycamore bit strings are quite different from those of Zuchongzhi bit strings, while both processors score similar values of linear cross-entropy fidelity for random circuit sampling. Some bit strings sampled by the Zuchongzhi processor pass the NIST random number tests while both Sycamore and Zuchongzhi processors show similar patterns in the heat maps of bit strings. Zuchongzhi bit strings are much closer to classical uniform random bits than those of Sycamore. It is shown that the statistical properties of bit strings of both random quantum circuits change little as the depth of the random quantum circuits increases. Our findings raise a question about the computational reliability of noisy quantum processors because two quantum processors with similar noise levels and similar qubit structures produced statistically different outputs for the same random quantum circuit sampling.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations

This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of synthetic load profiles (SLPs) simultaneously. The main contribution of MultiLoad-GAN is the capture of spatial-temporal correlations among a group of loads that are served by the same distribution transformer. This enables the generation of a large amount of correlated SLPs required for microgrid and distribution system studies. Here, the novelty and uniqueness of the MultiLoad-GAN framework are three-fold. First, to the best of our knowledge, this is the first method for generating a group of load profiles bearing realistic spatial- temporal correlations simultaneously. Second, two complementary realisticness metrics for evaluating generated load profiles are developed: computing statistics based on domain knowledge and comparing high-level features via a deep-learning classifier. Third, to tackle data scarcity, a novel iterative data augmentation mechanism is developed to generate training samples for enhancing the training of both the classifier and the MultiLoad-GAN model. Simulation results show that MultiLoad- GAN can generate more realistic load profiles than existing approaches, especially in group level characteristics. With little finetuning, MultiLoad-GAN can be readily extended to generate a group of load or PV profiles for a feeder or a service area.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Bayesian Estimation of Oscillator Parameters: Toward Anomaly Detection and Cyber-Physical System Security

Cyber-physical system security presents unique challenges to conventional measurement science and technology. Anomaly detection in software-assisted physical systems, such as those employed in additive manufacturing or in DNA synthesis, is often hampered by the limited available parameter space of the underlying mechanism that is transducing the anomaly. As a result, the formulation of anomaly detection for such systems often leads to inverse or ill-posed problems, requiring statistical treatments. Here, we present Bayesian inference of unknown parameters associated with a generic actuator considered as a representative vital element of a cyber-physical system. Via a series of experimental input-output measurements, a transfer function for the actuator is obtained numerically, which serves as our model for the proposed method. Linear, nonlinear, and delayed dynamics may be assumed for the actuator response. By devising a code-based malicious signal, we study the efficacy of Bayesian inference for its potential to produce a detection, including uncertainty quantification, with a remarkably small number of input data points. Our approach should be adaptable to a variety of real-time cyber-physical anomaly detection scenarios.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Chemomechanical weakening of muscovite quantified with in situ liquid nanoindentation

Abstract Chemomechanical weakening of layered phyllosilicate muscovite mica was studied as a function of chemical environment via in situ liquid-nanoindentation under four conditions (dry, deionized water, and two NaCl solutions of different pH). While traditional indentation analyses of layered materials with extreme mechanical anisotropy have been limited due to displacement bursts (pop-ins), here the bursts were used as proxies for delamination, fracture, and spalling events. Since displacement bursts during an indent represent a stochastic process, 120 indents were conducted for each condition to generate statistically significant amounts of data. In total, over 9000 bursts were assessed using a load–displacement threshold criterion, classifying this as a high-throughput nanoscale fracture technique. For each burst, initiation load, initiation displacement, plastic zone volume at initiation, and energy dissipation were analyzed. A power-law relationship between the burst load and burst energy was noted which separated the bursts into two continuous distributions: (1) bursts due only to the mechanics of the indent and (2) bursts due to both the mechanics of the indent and the environment. By using a cumulative probability distribution, it was found that the NaCl solutions decreased the minimum plastic zone volume necessary to initiate a displacement burst by an order of magnitude relative to the dry condition. Finally, the underlying mechanisms explaining the trends in initiation volume as a function of environment were discussed, with a focus on the chemomechanical degradation processes via chemical attack and cation exchange.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The impact of void environment on AGN

ABSTRACT We study the population of active galaxies in void environment in the Sloan Digital Sky Survey. We use optical spectroscopic information to analyse characteristics of the emission lines of galaxies, accomplished by WHAN and BPT diagrams. Also, we study Wide-field Infrared Survey Explorer(WISE) mid-IR colours to assess active galactic nucleus (AGN) activity. We investigate these different AGN classification schemes, both optical and mid-IR, and their dependence on the spatial location with respect to the void centres. To this end, we define three regions: void, the spherical region defined by voidcentric distance relative to void radius (distance/rvoid) smaller than 0.8, comprising overdensities lesser than −0.9, an intermediate/transition shell region (namely, void-wall) 0.8 < distance/rvoid < 1.2, and a region sufficiently distant from voids, the field: distance/rvoid > 2. We find statistical evidence for a larger fraction of AGN and star-forming galaxies in the void region, regardless of the classification scheme addressed (either BPT, WHAN, or WISE). Moreover, we obtain a significantly stronger nuclear activity in voids compared to the field. We find an unusually large fraction of the most massive black holes undergoing strong accretion when their host galaxies reside in voids. Our results suggest a strong influence of the void environment on AGN mechanisms associated with galaxy evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Combined Effect of the Wegener–Bergeron–Findeisen Mechanism and Large Eddies on Microphysics of Mixed-Phase Stratiform Clouds

Abstract The process of glaciation in mixed-phase stratiform clouds was investigated by a novel Lagrangian–Eulerian model (LEM) in which thousands of adjoining Lagrangian parcels moved within a turbulent-like velocity field with statistical parameters typical of the Arctic boundary layer. We used detailed bin microphysics to describe the condensation/evaporation processes in each parcel, in which droplets, aerosols, and ice particles were described using size distributions of 500 mass bins. The model also calculated aerosol mass inside droplets and ice particles. Gravitational sedimentation of droplets and ice particles was also accounted for. Assuming that droplet freezing is the primary source of ice particles, the Arctic clouds observed in Indirect and Semi-Direct Aerosol Campaign (ISDAC) were successfully simulated. The model showed that at a low ice particle concentration typical of ISDAC, large vortices (eddies) led to a quasi-stationary regime, in which mixed-phase St existed for a long time. The large eddies controlled the water partitioning in the mixed-phase clouds. Droplets formed and grew in updrafts, typically reaching the cloud top, and evaporated in downdrafts. Ice particles grew in updrafts and downdrafts. The Wegener–Bergeron–Findeisen (WBF) mechanism was efficient in downdrafts and some parts of updrafts, depending on ice concentration and vertical velocity. At low ice concentrations, the effect of ice on the phase partitioning was negligible. In this regime, liquid droplets were found near the cloud top, whereas ice particles precipitated through the cloud base. When ice concentration exceeded about 10 L −1 , the WBF mechanism led to glaciation of almost the entire cloud, with the exception of narrow cloud regions associated with strong updrafts. At ice particle concentrations of a few tens per liter, the oscillatory regime took place due to the ice–liquid interaction. The microphysical structure of mixed-phase St forms as a combined effect of cloud dynamics (large eddies) and the WBF mechanism.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Separation of Radionuclides from a Rare Earth-Containing Solution by Zeolite Adsorption

The increasing industrial demand for rare earths requires new or alternative sources to be found. Within this context, there have been studies validating the technical feasibility of coal and coal byproducts as alternative sources for rare earth elements. Nonetheless, radioactive materials, such as thorium and uranium, are frequently seen in the rare earths’ mineralization, and causes environmental and health concerns. Consequently, there exists an urgent need to remove these radionuclides in order to produce high purity rare earths to diversify the supply chain, as well as maintain an environmentally-favorable extraction process for the surroundings. In this study, an experimental design was generated to examine the effect of zeolite particle size, feed solution pH, zeolite amount, and contact time of solid and aqueous phases on the removal of thorium and uranium from the solution. The best separation performance was achieved using 2.50 g of 12-µm zeolite sample at a pH value of 3 with a contact time of 2 h. Under these conditions, the adsorption recovery of rare earths, thorium, and uranium into the solid phase was found to be 20.43 wt%, 99.20 wt%, and 89.60 wt%, respectively. The Freundlich adsorption isotherm was determined to be the best-fit model, and the adsorption mechanism of rare earths and thorium was identified as multilayer physisorption. Further, the separation efficiency was assessed using the response surface methodology based on the development of a statistically significant model.

58 GEOSCIENCES↗
DOE OSTI2024

Robust finite-temperature many-body scarring on a quantum computer

Mechanisms for suppressing thermalization in disorder-free many-body systems, such as Hilbert space fragmentation and quantum many-body scars, have recently attracted much interest in foundations of quantum statistical physics and potential quantum information processing applications. However, their sensitivity to realistic effects such as finite temperature remains largely unexplored. Here, we have utilized IBM's Kolkata quantum processor to demonstrate an unexpected robustness of quantum many-body scars at finite temperatures when the system is prepared in a thermal Gibbs ensemble. We identify such robustness in the PXP model, which describes quantum many-body scars in experimental systems of Rydberg atom arrays and ultracold atoms in tilted Bose-Hubbard optical lattices. By contrast, other theoretical models which host exact quantum many-body scars are found to lack such robustness and their scarring properties quickly decay with temperature. Our study sheds light on the important differences between scarred models in terms of their algebraic structures, which impacts their resilience to finite temperature. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

A numerical study of the meso-structure variability in the compaction process of prepreg platelet molded composites

Compaction deformations in a thermoplastic composite system molded from chopped prepreg platelets were analyzed to obtain statistical distributions of local distortions that define the final consolidated meso-structure. This study provides an insight into and allows quantification of the intrinsic geometric variability of the composite meso-structure that controls the variability of the effective composite mechanical properties. A two-phase viscoelastic finite element model was utilized to treat the behavior of molten prepreg platelets, which were assumed to be incompressible and inextensible in the fiber direction during processing. A two-step simulation procedure with discretely modeled platelets and platelet-to-platelet contacts is proposed. The analysis begins with a drop simulation of the uncontrolled material deposition followed by a 3D finite element analysis of the preform compaction and bulk material flow. The proposed model provides a method for generating a composite meso-structure ab initio starting from a statistical sampling of the initial conditions for platelet deposition. The predicted platelet thickness distribution, intra-platelet fiber waviness and out-of-plane platelet undulations were found to be in good agreement with experimental measurements. An estimate of the volume of resin pockets was inferred from the compaction simulation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Acceleration Factors for Combined‐Accelerated Stress Testing of Photovoltaic Modules

Combined‐accelerated stress testing (C‐AST) is developed to establish the durability of photovoltaic (PV) products, including for degradation modes that are not a priori known or examined in standardized tests. C‐AST aims to comprehensively represent the sample, stress factors, and their combinations using levels at the statistical tails of the natural environment. Acceleration factors for relevant climate sequences within the C‐AST cycle with respect to the Florida USA climate are estimated for selected degradation mechanisms. It is found that for degradation of the outer backsheet polymer layer, the acceleration factor of the tropical climate sequence (the longest of the climate sequences) is f ( T , G ) = 17.3 with ultraviolet photodegradation; for polyethylene terephthalate hydrolysis (backsheets), f ( T , RH ) = 426; for electrochemical corrosion (PV cell), f ( I ) = 14.1; and for PbSn solder fatigue f (Δ T , r ( T )) = 17.3. Here, T is the module temperature, G is the broadband spectrum irradiance on the plane of array of the module, RH is the relative humidity on the module surface, I is the leakage current through the module packaging, and r ( T ), the number of temperature reversals. The methods discussed herein are generally applicable for evaluating acceleration factors in other accelerated test methods.

14 SOLAR ENERGY↗
DOE OSTI2026

Transition from Vehicular to Structural Ionic Transport in Electrified Alkali Aqueous Solutions

A molecular understanding of the solvation and dynamics of ions under static electric fields is crucial for modelling a wide range of natural and technological processes. Yet, traditional simulation methods suffer from a trade-off that has to be made between accuracy and statistical convergence. To bridge this gap, herein we extend our recently introduced Perturbed Neural Network Potential Molecular Dynamics (PNNP MD) approach to investigate the solvation structures and ionic transport mechanisms of electrified alkali cationic solutions. We obtain ionic conductivities for Li+, Na+ and Cs+ from the field dependence of the ionic current density in good agreement with experiment. Surprisingly, the migration mechanism is found to be strikingly different for the three ions despite their similar ionic conductivities. While Li+ conducts predominantly through vehicular migration of a stable 4-fold coordinated ion at all field strengths, Cs+ conducts strictly through a structural diffusion mechanism, where 9-12 transient first shell water coordination bonds are broken and renewed. Notably, aqueous Na+ emerges as a “Goldilocks” ion: its ion-water interactions are strong enough to maintain distinct 5-6 fold coordination shells at zero field (unlike Cs+) yet labile enough to be strongly perturbed by electric fields (unlike Li+). As a consequence, we observe an electric field-induced transition from vehicular to structural ionic transport for Na+ that is accompanied by a marked increase in the ionic current density. Our results imply that the conductance mechanism of ions with moderate ion-solvent interactions can be effectively tuned by external electric fields.

Joll, Kit↗
DOE OSTI2023

Missing data in multi-omics integration: Recent advances through artificial intelligence

Biological systems function through complex interactions between various ‘omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the development of integration approaches that are able to capture the complex, often non-linear, interactions that define these biological systems and are adapted to the challenges of combining the heterogenous data across ‘omic views. A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples. Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more ‘omic techologies. Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data. A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review. We describe recently developed approaches, noting their primary use cases and highlighting each method's approach to handling missing data. We additionally provide an overview of the more traditional missing data workflows and their limitations; and we discuss potential avenues for further developments as well as how the missing data issue and its current solutions may generalize beyond the multi-omics context.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Measurement of the t-dependence of the Beam Asymmetry of Photoproduced eta at the GlueX Experiment at Jefferson Lab

We report on the photoproduction of eta mesons studied at the GlueX experiment at Thomas Jefferson National Laboratory in Newport News, VA. A linearly polarized photon beam at energies between 8.2 and 8.8 GeV, incident on a liquid hydrogen target, produces eta dominantly through t-channel exchanges. Azimuthal angular distributions of the eta, for the direction of the polarized photon, facilitate the extraction of the beam asymmetry Sigma for the reaction gamma p -> eta p. We can understand the production mechanisms of the eta by analyzing Sigma_eta, which is sensitive to the particles exchanged. Compared to previous GlueX results [[4],[17]], we have access to five times the statistics, thereby allowing us to extend these measurements to values beyond the previous limitation of -t lte 1.1 (GeV)2. Results will be shown for events reconstructed from the decay of eta -> gamma gamma and eta -> pi^+ pi^- pi^0 Systematic checks are also made for the neutral mode decay to confirm the stability of the extracted results. We find through data binned up to a momentum transfer of 2.0 (GeV)2 that the photoproduction of eta at these beam energies is largely carried out by natural processes, with a preliminary comparison with the photoproduction of eta' suggesting possible strangeness exchanges at wider kinematic regions.

Erbora, Tolga↗