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At least 1,423 records · Page 79

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DOE OSTI2016

Determination of grain boundary mobility during recrystallization by statistical evaluation of electron backscatter diffraction measurements

One of the key aspects influencing microstructural design pathways in metallic systems is grain boundary motion. The present work introduces a method by means of which direct measurement of grain boundary mobility vs. misorientation dependence is made possible. The technique utilizes datasets acquired by means of serial electron backscatter diffraction (EBSD) measurements. The experimental EBSD measurements are collectively analyzed, whereby datasets were used to obtain grain boundary mobility and grain aspect ratio with respect to grain boundary misorientation. The proposed method is further validated using cellular automata (CA) simulations. Single crystal aluminium was cold rolled and scratched in order to nucleate random orientations. Subsequent annealing at 300 °C resulted in grains growing, in the direction normal to the scratch, into a single deformed orientation. Growth selection was observed, wherein the boundaries with misorientations close to Σ7 CSL orientation relationship (38° 〈111〉) migrated considerably faster. The obtained boundary mobility distribution exhibited a non-monotonic behavior with a maximum corresponding to misorientation of 38° ± 2° about 〈111〉 axes ± 4°, which was 10–100 times higher than the mobility values of random high angle boundaries. Correlation with the grain aspect ratio values indicated a strong growth anisotropy displayed by the fast growing grains. The observations have been discussed in terms of the influence of grain boundary character on grain boundary motion during recrystallization. - Highlights: • Statistical microstructure method to measure grain boundary mobility during recrystallization • Method implementation independent of material or crystal structure • Mobility of the Σ7 boundaries in 5N Al was calculated as 4.7 × 10{sup –8} m{sup 4}/J ⋅ s. • Pronounced growth selection in the recrystallizing nuclei in Al • Boundary mobility values during recrystallization 2–3 orders of magnitude larger vis-à-vis curvature driven motion.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Statistical analysis of HAADF-STEM images to determine the surface coverage and distribution of immobilized molecular complexes

The surface immobilization of molecular catalysts is attractive because it combines the benefits of homogeneous and heterogeneous catalysis. However, determining the surface coverage and distribution of a molecular catalyst on a solid support is often challenging, inhibiting our ability to design improved catalytic systems. Here, in this work, we demonstrate that the combination of scanning transmission electron microscopy (STEM) and image analysis of the individual positions of heavy atoms in transition metal complexes via a convolutional neural network (CNN) allows statistically robust determination of the surface coverage and distribution of immobilized molecular catalysts. These observations provide information about how changes in the functionalization conditions, attachment group, and structure of the molecular catalyst affect the surface coverage and distribution, providing insight into the chemical mechanism of surface immobilization. The method could be generally valuable for correlating the surface coverage and distribution to the activity, selectivity, and stability of a catalytic system.

HAADF-STEM↗

DOE OSTI2026

Reliable statistics-based detection and investigation of anomalies in a SMART valve system

Reliable anomaly detection and diagnosis are critical for the safe operation of complex engineered systems. This study presents a unified framework that integrates statistical, model-based, and data-driven techniques for anomaly detection and investigation, demonstrated on SMART valve systems in hybrid energy applications. Four detection methods—mean deviation, seasonal extreme studentized deviate, ARIMA forecasting, and matrix profiling—were implemented and compared. Matrix profiling was particularly effective in revealing subtle deviations and hidden relationships among variables. Anomaly investigation was performed by analyzing variable-level and grouped signal profiles, with system topology incorporated to distinguish primary faults from propagated effects. Grouping signals by type enhanced interpretability, enabling accurate localization of anomalies across multi-dimensional datasets. Experimental results confirmed the framework's capability to consistently detect and isolate anomalies while providing actionable insights into system interdependencies. The proposed methodology offers a robust, interpretable, and scalable solution for condition monitoring, with potential applications in safety-critical domains such as nuclear energy, aerospace, and process industries.

ARIMA models↗

DOE OSTI2014

Statistical estimation of the performance of a fast-neutron multiplicity system for nuclear material accountancy

Statistical analyses have been performed to develop bounding estimates of the expected performance of a conceptual fast-neutron multiplicity system (FNMS) for assaying plutonium. The conceptual FNMS design includes 32 cubic liquid scintillator detectors, measuring 7.62 cm per side, configured into 4 stacked rings of 8 detectors each. Expected response characteristics for the individual FNMS detectors, as well as the response characteristics of the entire FNMS, were determined using Monte Carlo simulations based on prior validation experiments. The results from these simulations were then used to estimate the Pu assay capabilities of the FNMS in terms of counting time, assay mass, and assay mass variance, using assay mass variance as a figure of merit. The analysis results are compared against a commonly used thermal-neutron coincidence counter. The advantages of using a fast-neutron counting system versus a thermal-neutron counting system are significant. Most notably, the time required to perform an assay to an equivalent assay mass variance is greatly reduced with a fast-neutron system, by more than an order of magnitude compared with that of the thermal-neutron system, due to the reduced probability of random summing with the fast system. The improved FNMS performance is especially relevant for assays involving Pu masses of 10 g or more.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Classification of events from α -induced reactions in the MUSIC detector via statistical and ML methods

The Multi-Sampling Ionization Chamber (MUSIC) detector is typically used to measure nuclear reaction cross sections relevant for nuclear astrophysics, fusion studies, and other applications. From the MUSIC data produced in one experiment scientists carefully extract an order of 10 3 events of interest from about 10 9 total events, where each event can be represented by an 18-dimensional vector. However, the standard data classification process is based on expert driven, manually intensive data analysis techniques that require several months to identify patterns and classify the relevant events from the collected data. Here, to address this issue, we present a method for the classification of events originating from specific α-induced reactions by combining statistical and machine learning methods that require significantly less input from the domain scientist, relative to the standard technique. Here, we applied the new method to two experimental data sets and compared our results with those obtained using traditional methods. With few exceptions, the number of events classified by our method agrees within ±20% with the results obtained using traditional methods. With the present method, which is the first of its kind for the MUSIC data, we have established the foundation for the automated extraction of physical events of interest from experiments using the MUSIC detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

High fidelity multiphysics tightly coupled model for a lead cooled fast reactor concept and application to statistical calculation of hot channel factors

A tightly coupled multiphysics code system is established using the MOOSE framework for hot channel factor (HCF) evaluation on a Lead Fast Reactor (LFR) concept. The coupled system is driven by the Griffin multiphysics coupling capability under which the MOOSE Heat Transfer module and NekRS computational fluid dynamics solver are coupled for conjugate heat transfer using the Cardinal application. The coupled capability is demonstrated on an LFR assembly model based on materials and geometry of a prototypical lead-cooled fast reactor design by Westinghouse Electric Company, LLC. Moreover, the work integrates the Multiphysics Object Oriented Simulation Environment (MOOSE) Stochastic Tools Module (STM) to perform calculations for statistical analysis of HCF. Furthermore, the coupling strategy and workflow demonstrated in this paper is not only useful for predicting accurate hot channel factors for different kinds of advanced reactors but also for other engineering applications such as control rod worth assessment, generation of high-fidelity database for Artificial intelligence (AI)/machine learning (ML) training, design optimization and multi-resolution modeling.

Cardinal↗

DOE OSTI2021

Evaluating MPI resource usage summary statistics

The Message Passing Interface (MPI) remains the dominant programming model for scientific applications running on today’s high-performance computing (HPC) systems. This dominance stems from MPI’s powerful semantics for inter-process communication that has enabled scientists to write applications for simulating important physical phenomena. MPI does not, however, specify how messages and synchronization should be carried out. Those details are typically dependent on low-level architecture details and the message characteristics of the application. Therefore, analyzing an application’s MPI resource usage is critical to tuning MPI’s performance on a particular platform. The result of this analysis is typically a discussion of the mean message sizes, queue search lengths and message arrival times for a workload or set of workloads. While a discussion of the arithmetic mean in MPI resource usage might be the most intuitive summary statistic, it is not always the most accurate in terms of representing the underlying data. In this paper, we analyze MPI resource usage for a number of key MPI workloads using an existing MPI trace collector and discrete-event simulator. Our analysis demonstrates that the average, while easy and efficient to calculate, is a useful metric for characterizing latency and bandwidth measurements, but may not be a good representation of application message sizes, match list search depths, or MPI inter-operation times. Additionally, we show that the median and mode are superior choices in many cases. We also observe that the arithmetic mean is not the best representation of central tendency for data that are drawn from distributions that are multi-modal or have heavy tails. Furthermore, the results and analysis of our work provide valuable guidance on how we, as a community, should discuss and analyze MPI resource usage data for scientific applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Remote sensing and statistical analysis of the effects of hurricane María on the forests of Puerto Rico

Widely recognized as one of the worst natural disaster in Puerto Rico's history, hurricane María made landfall on September 20, 2017 in southeast Puerto Rico as a high-end category 4 hurricane on the Saffir-Simpson scale causing widespread destruction, fatalities and forest disturbance. This study focused on hurricane María's effect on Puerto Rico's forests as well as the effect of landform and forest characteristics on observed disturbance patterns. In this work, we used Google Earth Engine (GEE) to assess the severity of forest disturbance using a disturbance metric based on Landsat 8 satellite data composites with pre and post-hurricane María. Forest structure, tree phenology characteristics, and landforms were obtained from satellite data products, including digital elevation model and global forest canopy height. Our analyses showed that forest structure, and characteristics such as forest age and forest type affected patterns of forest disturbance. Among forest types, highest disturbance values were found in sierra palm, transitional, and tall cloud forests; seasonal evergreen forests with coconut palm; and mangrove forests. For landforms, greatest disturbance metrics was found at high elevations, steeper slopes, and windward surfaces. As expected, high levels of disturbance were also found close to the hurricane track, with disturbance less severe as hurricane María moved inland. Results demonstrated that forest and landform characteristics accounted for 34% of the variation in spatial forest spectral disturbance patterns. This study demonstrated an informative regional approach, combining remote sensing with statistical analyses to investigate factors that result in variability in hurricane effects on forest ecosystems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Temperature- and composition-dependent hydrogen diffusivity in palladium from statistically-averaged molecular dynamics

Solid-state hydrogen storage materials undergo complex phase transformations whose kinetics is often limited by hydrogen diffusion. Among metal hydrides, palladium hydride undergoes a diffusional phase transformation upon hydrogen uptake, during which the hydrogen diffusivity varies with hydrogen composition and temperature. Here we perform robust statistically-averaged molecular dynamics simulations to obtain a well-converged analytical expression for hydrogen diffusivity in bulk palladium that is valid throughout all stages of the reaction. Our studies confirm significant dependence of the diffusivity on composition and temperature that elucidate key trends in the available experimental measurements. Whereas at low hydrogen compositions, a single process dominates, at high hydrogen compositions, diffusion is found to exhibit behavior consistent with multiple hopping barriers. Further analysis, supported by nudged elastic band computations, suggests that the multi-barrier diffusion can be interpreted as two distinct mechanisms corresponding to hydrogen-rich and hydrogen-poor local environments.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Ensemble average and nearest particle statistics in disperse multiphase flows

A relation between the ensemble average and the nearest particle statistics is derived. The relation accounts for interactions among all the particles, not only the nearest one, and can be used to study long-range particle interactions without the difficulty of a divergent integral. As an example, this relation is applied to calculate the particle sedimentation velocity to the first order of the particle volume fraction. Using the relation, an important particle–fluid–particle stress is introduced for general multiphase flows.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Statistical characterization of a shock interacting with an inclined gas column

We present a statistical characterization of the interaction between a planar shock and a finite-diameter, cylindrical column of dense gas based on three-dimensional, large-eddy simulation results. In the simulation, the column of gas is initially inclined at an angle $\alpha _0$ with respect to the shock plane. Effects of the initial column angle on the mixing characteristics are examined at Mach number 2.0 for column incline angles $1^\circ$ , $5^\circ$ , $10^\circ$ and $30^\circ$ . Mean velocity profiles show that the column angle affects the gas velocity components in the vertical plane, but not in the spanwise direction. The gas undergoes higher initial upward acceleration at larger initial column incline angles. With time, the gas motion tends to become one-dimensional in the streamwise direction. Initially, velocity fluctuations are most intense within the interior of the column, but concentrate near the column leading edge over time. At high wavenumbers $\kappa$ , the turbulent kinetic energy spectra follow a power-law scaling of $\kappa ^{-1}$ . The structure functions of the mass fraction do not clearly demonstrate power-law scaling except at early times for $\alpha _0=30^\circ$ , manifesting overall trends very similar to those observed in earlier experiments. Probability distributions of the mass fraction show independence of the mean and the standard deviation of the mixed gas on $\alpha _0$ . The column angle was also found to have little effect on the mixing efficiency characterized by the molecular mixedness. Velocity components in the streamwise and transverse directions tend towards a bimodal distribution for larger $\alpha _{0}$ .

42 ENGINEERING↗

DOE OSTI2026

A Chemocatalytic Route to Stereoregular Poly(3-hydroxyhexanoate) and Its Statistical and Tri-Block Copolymers

Poly(3-hydroxyhexanoate) (P3HHx)-based poly(3- hydroxyalkanoate)s (PHAs) are biologically produced, commercially implemented PHAs, but little is known in the open literature about the structure and property characterizations of discrete, authentic homopolymer P3HHx, and its copolymers with poly(3- hydroxybutyrate) (P3HB). Here, we introduce a chemocatalytic route to stereoregular (both isotactic and syndiotactic) P3HHx and PHA copolymers with P3HB, including statistical copolymer P3HBHx with various levels of 3HHx incorporation and discrete hard−soft−hard triblock copolymer P3HB-b-P3HHx-b-P3HB, and present extensive characterizations of their structures, thermal properties, and mechanical performance. The synthesis is efficiently achieved by one-pot polymerization of eight-membered di(n-propyl) and dimethyl (for copolymerization) diolides catalyzed by chiral molecular catalysts. Notably, P3HBHx can be rapidly produced in quantitative yield and exhibits high molar mass (M n up to 551 kg/mol) as well as both high modulus (up to 1.39 GPa) and ductility (up to 445%), while P3HB-b-P3HHx-b-P3HB further extends application temperature windows by possessing a unique combination of low T g (−18 °C) and high T m (154 °C) values. These findings highlight the stereomicrostructural and architectural versatility of chemocatalytic routes to PHAs, which, in turn, can be utilized to largely tune the PHA thermal properties and mechanical performance.

Biopolymers↗

DOE OSTI2025

First-Principles Statistical Mechanics Study of Magnetic Fluctuations and Order–Disorder in the Spinel LiNi 0.5 Mn 1.5 O 4 Cathode

While significant magnetic interactions exist in lithium transition metal oxides, commonly used as Li-ion cathodes, the interplay between magnetic couplings, disorder, and redox processes remains poorly understood. In this work, we focus on the high-voltage spinel LiNi 0.5 Mn 1.5 O 4 (LNMO) cathode as a model system on which to apply a computational framework that uses first principles-based statistical mechanics methods to predict the finite temperature magnetic properties of materials and provide insights into the complex interplay between magnetic and chemical degrees of freedom. Density functional theory calculations on multiple distinct Ni–Mn orderings within the LNMO system, including the ordered ground-state structure (space group P4332), reveal a preference for a ferrimagnetic arrangement of the Ni and Mn sublattices due to strong antiferromagnetic superexchange interactions between neighboring Mn 4+ and Ni 2+ ions and ferromagnetic Mn–Mn and Ni–Ni couplings, as revealed by magnetic cluster expansions. These results are consistent with qualitative predictions using the Goodenough-Kanamori-Anderson rules. Simulations of the finite temperature magnetic properties of LNMO are conducted using Metropolis Monte Carlo. We find that a “semiclassical” Monte Carlo sampling method based on the Heisenberg Hamiltonian accurately predicts experimental magnetic transition temperatures observed in magnetometry measurements. This study highlights the importance of a robust computational toolkit that accurately captures the complex chemomagnetic interactions and predicts finite temperature magnetic behavior to help analyze experimental magnetic and magnetic resonance spectroscopy data acquired ex situ and operando.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Antifreeze Hydrogels from Amphiphilic Statistical Copolymers

Prevention of ice formation is a critical issue for many applications, but routes to overcome the large thermodynamic driving force for crystallization of water at significant supercooling are limited. Here, we demonstrate that supramolecular hydrogels formed from statistical copolymers of 2-hydroxyethyl acrylate (HEA) and 2-( N -ethylperfluorooctane sulfonamido)ethyl methacrylate (FOSM) exhibit a degree of ice formation suppression unprecedented in a synthetic material. The mechanisms of ice prevention by these hydrogels mimic two methods used by nature: (1) hydrogen bonding of water to highly hydrophilic macromolecular chains and (2) nanoconfinement of water between hydrophobic moieties. From systematic variation in the copolymer composition to control the nanoscale (<4 nm) separation of the self-assembled hydrophobic nanodomains, the main mechanism by which these supramolecular hydrogels inhibit large amounts of water from freezing appears to be soft nanoconfinement. Nearly complete ice inhibition was achieved in hydrogels when the nanodomain separation was <3 nm (i.e., confinement volume ~15 nm 3 ) where <290 water molecules are present. Dielectric spectroscopy is consistent with two primary populations of water: a population of water with a bulk-like dynamics as well as T g (136 K) and a minority population of water with suppressed dynamics and an enhanced T g near 151 K that is attributed to interfacial water. The nanostructured design of these supramolecular hydrogels provides a blueprint concept for controlling and manipulating ice formation in concentrated soft matter using the length scale between hydrophobic domains and the hydrophilicity of the network water-soluble component. Furthermore, these insights have the potential to provide solutions to challenges with ice in engineering applications where confinement of water to nanoscale dimensions is possible.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Statistical Mechanics-Based Theoretical Investigation of Solvation Effects on Glucose Anomer Preferences

The importance of solvation effects on the stability of glucose anomers has been studied by the combination of quantum mechanics and statistical mechanics, namely, the reference interaction site model self-consistent field spatial electron density distribution. The preferences of α- and β-glucose in H 2 O are well reproduced with the obtained ratio of 35:65 for α- and β-glucose, respectively. Indirect interactions and bulk effects, described by the Onsager model, are relatively small compared to the direct solute–solvent interactions, especially in [DMIM]Cl and dimethyl sulfoxide. From the decomposition of solvation free energy and solvation structures, it can be seen that the interactions with the solvent molecules greatly contribute to the anomer preferences.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Proteomics Goes to Court: A Statistical Foundation for Forensic Toxin/Organism Identification Using Bottom-Up Proteomics

Bottom-up proteomics is increasingly being used to characterize unknown environmental, clinical, and forensic samples. Proteomics-based bacterial identification typically proceeds by tabulating peptide “hits” (i.e. confidently identified peptides) associated with the organisms in a database; those organisms with enough hits are declared present in the sample. This approach has proven successful in laboratory studies, however, important research gaps remain. First, the common-practice reliance on unique peptides for identification is susceptible to a phenomenon known as signal erosion. Second, no general guidelines are available for determining how many hits are needed to make a confident identification. These gaps inhibit the transition of this approach to real-world samples where conditions vary and large databases may be needed. We propose statistical criteria for organism identification that applies regardless of sample quality or data analysis pipeline. These criteria are straightforward, producing a p-value on the result. We test the criteria on LC-MS/MS datasets representing 919 different bacterial and toxin datasets acquired with multiple data collection platforms. Results reveal a >95% correct species-level identification rate, demonstrating the effectiveness and robustness of proteomics-based organism/toxin identification.

Jarman, Kristin H.↗

DOE OSTI2024

Crystallization of Bottlebrush Statistical Copolymers of Polystyrene and Poly(ethylene oxide)

Here, bottlebrush statistical copolymers (BSCPs) with poly(ethylene oxide) (PEO) and polystyrene (PS) side chains grafted to a polynorbornene (PNB) backbone were synthesized by ring-opening metathesis polymerization (ROMP). The impact of the glassy PS side chains on the crystallization of the PEO side chains as a function of the backbone length, grafting densities, and fraction of the PS and PEO side chains is described. The bottlebrush architecture, where the side chains are anchored to the backbone, inherently constrains the mobility of PEO. Compared with the bulk crystallization temperature of PEO, the higher glass transition temperature of PS places further constraints on PEO crystallization. The limited mobility of PEO leads to cold crystallization behavior during heating. Reduced grafting densities, in turn, reduce side-chain crowding, leading to less extended pendant structures. The degree of crystallinity of PEO was found to decrease at lower grafting densities to a point where crystallization was not observed. The average distance between bottlebrush backbones increased linearly with the backbone length, suggesting that the backbone forms a distinct mesodomain. For BSCPs with asymmetric volume fractions of PS and PEO side chains, the degree of crystallinity of PEO increases with a change from cold crystallization to normal nucleation and growth during cooling, as a result of reduced constraints on PEO mobility by the glassy PS side chains.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Statistical Analysis of Intertube Tunneling Contacts in the Macroscopic Electrical Conductivity of Carbon Nanotube Fibers

Here, this study investigates the influence of tunneling contact resistances between carbon nanotubes (CNTs) on electron transport and electrical conductivity of macroscopic carbon nanofibers (CNFs), which profoundly impacts the performance of CNT thin film electronics, CNF electron emitters and cathodes, and energy conversion and storage devices. Utilizing a self-consistent electrical contact model coupling a transmission line model with tunneling current, we calculate the contact resistances of a plethora of CNT-CNT contacts within a CNF fiber, which consists of aligned, densely packed CNTs. A statistical analysis is conducted, using Gaussian distributions to account for variations in contact lengths, tunneling gap distances, and single CNT aspect ratios, to calculate the CNT-CNT contact resistance and the overall resistance of CNT fiber. By scaling our model to a macroscopic level, our results are in good agreement with experimental measurements. Our calculation suggests that while increasing the contact overlap length diminishes individual CNT-CNT contact resistance, it could paradoxically increase macroscopic CNT fiber resistance for a given constant CNF mass density, which is due to that fact that a larger overlap length allows more CNTs to pack along an electrical conduction path per unit length, leading to more tunneling contact junctions connected in series and thus less number of parallel conduction paths within the fiber cross section. Increasing tunneling gap distance increases both individual contact and overall fiber resistance. This research provides a simple design tool for tailoring CNT fiber electrical properties to promote real-world applications using CNTs or similar low-dimensional materials.

36 MATERIALS SCIENCE↗