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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Modeling the Effect of Evaporative Flux and Colloidal Aggregation on Crud Deposition

During the last few years lot of research has been conducted to investigate and predict the Crud deposition in PWRs. One of the common findings of various experimental and computational studies is that thickness or amount of crud deposits is directly related to evaporative flux maps. Evaporation driven deposition profile on the surfaces takes different forms, i.e. from ring to disk shapes during initial stages and in the form of porous to impervious continuous layers covering the hot fuel surface at the later stages. The amount and thickness of these deposits although are directly related to evaporative flux, the relationship is much complex and can be dependent upon several other factors. A popular problem relating evaporative flux to particulate deposition has been well studied in the form of drying of sessile colloidal drops. It was proposed that, this phenomenon is due to a geometrical constraint: the free surface, constrained by a pinned contact line squeezes the fluid outwards to compensate for evaporative losses and thereby transporting particles to the contact line. Later on a quantitative relationship between evaporative flux and deposition rate was proposed and validated by the same group, which showed power-law relationship. Deposition studies conducted with controlled concentration of particles with subcooled nucleate boiling also showed a power-law relationship rather than a simpler linear relationship whereas physics of evaporation would tend to concentrate particles at linear rate. In a typical subcooled nucleate boiling system, bubble growth and departure time is of the order 1-2 ms, measured experimentally. Diffusion time constant for dilute spherical metallic colloids such as crud particulates, generally present as particulate in process piping, range from 0.0025-2.5 sec in water for a size range 0.1-1.0 μm. Colloidal behavioral characteristics during the growth of bubbles can be computed with escape time constant at collision, which is dependent on effective Hamaker constant. For minimum effective Hamaker constant of Hematite is -2.5 kT and diffusion time constant τ = 0.0025 sec, escape time constant is computed as Kramer rule, τexp[A/kT] magnitude is 0.03 sec or 30 ms. This clearly indicates that evaporation will cause particles to come together and they cannot escape at a fast enough rate which would lead them to form agglomerates. Therefore both effects of evaporative flux in conjunction with the agglomeration kinetics must be considered for modeling the crud deposition if the evaporative flux is known. In this paper, we propose a phenomenological model of colloidal transport and aggregation with evaporative flux. The dynamic of particles interactions while drying of sessile colloidal drops is developed by coupling general transport equation with Smoluchowshki Coagulation equation. Crud deposition on the nuclear fuel has been a serious problem with severe consequences in nuclear power plants. In several studies it has been consistently reported that crud deposition is directly related to evaporative flux on the nuclear fuel surfaces. However, it has not been established how to quantify the deposition rates of crud type particulates if the evaporative flux is known. This complex multiphysics problem is similar to coffee-ring effect, where the governing physics is understood more clearly. In this work, a simplified geometry is used to model the effects of evaporative flux acting at a boundary on the colloidal concentration. The effect of this condition on change in concentration is shown numerically. Further, agglomeration is suspected to play a significant role in high evaporative flux such as bubble nucleation. The coagulation kinetics is modeled using uniform particle distribution and the effective total concentration of colloids including original particles and agglomerates is studied. Once this model is validated with a simple separate effect experiments, it can be integrated to large scale PWR models of evaluating evaporative flux and chemistry models developed by Electrical Power Research Institute (EPRI). (authors)

42 ENGINEERING↗
DOE OSTI2016

Theory of multiple quantum dot formation in strained-layer heteroepitaxy

We develop a theory for the experimentally observed formation of multiple quantum dots (QDs) in strained-layer heteroepitaxy based on surface morphological stability analysis of a coherently strained epitaxial thin film on a crystalline substrate. Using a fully nonlinear model of surface morphological evolution that accounts for a wetting potential contribution to the epitaxial film's free energy as well as surface diffusional anisotropy, we demonstrate the formation of multiple QD patterns in self-consistent dynamical simulations of the evolution of the epitaxial film surface perturbed from its planar state. The simulation predictions are supported by weakly nonlinear analysis of the epitaxial film surface morphological stability. We find that, in addition to the Stranski-Krastanow instability, long-wavelength perturbations from the planar film surface morphology can trigger a nonlinear instability, resulting in the splitting of a single QD into multiple QDs of smaller sizes, and predict the critical wavelength of the film surface perturbation for the onset of the nonlinear tip-splitting instability. The theory provides a fundamental interpretation for the observations of “QD pairs” or “double QDs” and other multiple QDs reported in experimental studies of epitaxial growth of semiconductor strained layers and sets the stage for precise engineering of tunable-size nanoscale surface features in strained-layer heteroepitaxy by exploiting film surface nonlinear, pattern forming phenomena.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Penning plasma based simultaneous light emission source of visible and VUV lights

In this paper, a laboratory-based penning plasma discharge source is reported which has been developed in two anode configurations and is able to produce visible and VUV lights simultaneously. The developed source has simultaneous diagnostics facility using Langmuir probe and optical emission spectroscopy. The two anode configurations, namely, double ring and rectangular configurations, have been studied and compared for optimum use of the geometry for efficient light emissions and recording. The plasma is produced using helium gas and admixture of three noble gases including helium, neon, and argon. The source is capable to produce eight spectral lines for pure helium in the VUV range from 20 to 60 nm and total 24 spectral lines covering the wavelength range 20–106 nm for the admixture of gases. The large range of VUV lines is generated from gaseous admixture rather from the sputtered materials. The recorded spectrum shows that the plasma light radiations in both visible and VUV range are larger in double ring configuration than that of the rectangular configurations at the same discharge operating conditions. To clearly understand the difference, the imaging of the discharge using ICCD camera and particle-in-cell simulation using VORPAL have also been carried out. The effect of ion diffusion, metastable collision with the anode wall and the nonlinear effects are correlated to explain the results.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Your Clean Graphene is Still Not Clean

Researchers working with thin samples, such as monolayer graphene, are consistently struggling against contamination. Indeed, the problem of hydrocarbon contamination is known from the earliest days of electron microscopy and efforts to reduce this problem are ubiquitous to almost all high-vacuum experiments. Accurate knowledge of the behavior of such contamination is essential for electron beam (e-beam) based atomic fabrication, where it is aspired to select and control matter on an atom-by-atom basis. Here, the vexing question of hydrocarbon contamination on graphene is taken up. Image intensity is used to directly reveal the presence of diffusing hydrocarbons on ostensibly clean graphene. These diffusing hydrocarbons are previously inferred but not directly observed. Surprising dynamic variations of the concentration of these hydrocarbons impels questions about their origin. Here, some possible explanations are presented and some tentative conclusions are drawn. This work updates the conceptual model of “clean graphene” and offers refinements to the description of e-beam induced hydrocarbon deposition.

atomic fabrication↗
DOE OSTI2021

Multi-fidelity Bayesian neural networks: Algorithms and applications

Here we propose a new class of Bayesian neural networks (BNNs) that can be trained using noisy data of variable fidelity, and we apply them to learn function approximations as well as to solve inverse problems based on partial differential equations (PDEs). These multi-fidelity BNNs consist of three neural networks: The first is a fully connected neural network, which is trained following the maximum a posteriori probability (MAP) method to fit the low-fidelity data; the second is a Bayesian neural network employed to capture the cross-correlation with uncertainty quantification between the low- and high-fidelity data; and the last one is the physics-informed neural network, which encodes the physical laws described by PDEs. For the training of the last two neural networks, we first employ the mean-field variational inference (VI) to maximize the evidence lower bound (ELBO) to obtain informative prior distributions for the hyperparameters in the BNNs, and subsequently we use the Hamiltonian Monte Carlo (HMC) method to estimate accurately the posterior distributions for the corresponding hyperparameters. We demonstrate the accuracy of the present method using synthetic data as well as real measurements. Specifically, we first approximate a one- and four-dimensional function, and then infer the reaction rates in one- and two-dimensional diffusion-reaction systems. Moreover, we infer the sea surface temperature (SST) in the Massachusetts and Cape Cod Bays using satellite images and in-situ measurements. Taken together, our results demonstrate that the present method can capture both linear and nonlinear correlation between the low- and high-fidelity data adaptively, identify unknown parameters in PDEs, and quantify uncertainties in predictions, given a few scattered noisy high-fidelity data. Finally, we demonstrate that we can effectively and efficiently reduce the uncertainties and hence enhance the prediction accuracy with an active learning approach, using as examples a specific one-dimensional function approximation and an inverse PDE problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Treatment of the polar coordinate singularity in axisymmetric wave propagation using high-order summation-by-parts operators on a staggered grid

In this paper, we develop a high-order finite difference scheme for axisymmetric wave propagation in a cylindrical conduit filled with a viscous fluid. The scheme is provably stable, and overcomes the difficulty of the polar coordinate singularity in the radial component of the diffusion operator. The finite difference approximation satisfies the principle of summation-by-parts (SBP), which is used to establish stability using the energy method. To treat the coordinate singularity without losing the SBP property of the scheme, a staggered grid is introduced and quadrature rules with weights set to zero at the endpoints are considered. Finally, the accuracy of the scheme is studied both for a model problem with periodic boundary conditions at the ends of the conduit and its practical utility is demonstrated by modeling acoustic-gravity waves in a magmatic conduit.

42 ENGINEERING↗
DOE OSTI2015

GRIZZLY Model of Multi-Reactive Species Diffusion, Moisture/Heat Transfer and Alkali-Silica Reaction for Simulating Concrete Aging and Degradation

Concrete is widely used in the construction of nuclear facilities because of its structural strength and its ability to shield radiation. The use of concrete in nuclear power plants for containment and shielding of radiation and radioactive materials has made its performance crucial for the safe operation of the facility. As such, when life extension is considered for nuclear power plants, it is critical to have accurate and reliable predictive tools to address concerns related to various aging processes of concrete structures and the capacity of structures subjected to age-related degradation. The goal of this report is to document the progress of the development and implementation of a fully coupled thermo-hydro-mechanical-chemical model in GRIZZLY code with the ultimate goal to reliably simulate and predict long-term performance and response of aged NPP concrete structures subjected to a number of aging mechanisms including external chemical attacks and volume-changing chemical reactions within concrete structures induced by alkali-silica reactions and long-term exposure to irradiation. Based on a number of survey reports of concrete aging mechanisms relevant to nuclear power plants and recommendations from researchers in concrete community, we’ve implemented three modules during FY15 in GRIZZLY code, (1) multi-species reactive diffusion model within cement materials; (2) coupled moisture and heat transfer model in concrete; and (3) anisotropic, stress-dependent, alkali-silica reaction induced swelling model. The multi-species reactive diffusion model was implemented with the objective to model aging of concrete structures subjected to aggressive external chemical attacks (e.g., chloride attack, sulfate attack, etc.). It considers multiple processes relevant to external chemical attacks such as diffusion of ions in aqueous phase within pore spaces, equilibrium chemical speciation reactions and kinetic mineral dissolution/precipitation. The moisture/heat transfer module was implemented to simulate long-term spatial and temporal evolutions of the moisture and temperature fields within concrete structures at both room and elevated temperatures. The ASR swelling model implemented in GRIZZLY code can simulate anisotropic expansions of ASR gel under either uniaxial, biaxial and triaxial stress states, and can be run simultaneously with the moisture/heat transfer model and coupled with various elastic/inelastic solid mechanics models that were implemented in GRIZZLY code previously. This report provides detailed descriptions of the governing equations, constitutive equations and numerical algorithms of the three modules implemented in GRIZZLY during FY15, simulation results of example problems and model validation results by comparing simulations with available experimental data reported in the literature. The close match between the experiments and simulations clearly demonstrate the potential of GRIZZLY code for reliable evaluation and prediction of long-term performance and response of aged concrete structures in nuclear power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Testing nonlocal models of electron thermal conduction for magnetic and inertial confinement fusion applications

Three models for nonlocal electron thermal transport are here compared against Vlasov-Fokker-Planck (VFP) codes to assess their accuracy in situations relevant to both inertial fusion hohlraums and tokamak scrape-off layers. The models tested are (i) a moment-based approach using an eigenvector integral closure (EIC) originally developed by Ji, Held, and Sovinec [Phys. Plasmas 16, 022312 (2009)]; (ii) the non-Fourier Landau-fluid (NFLF) model of Dimits, Joseph, and Umansky [Phys. Plasmas 21, 055907 (2014)]; and (iii) Schurtz, Nicolaï, and Busquet’s [Phys. Plasmas 7, 4238 (2000)] multigroup diffusion model (SNB). We find that while the EIC and NFLF models accurately predict the damping rate of a small-amplitude temperature perturbation (within 10% at moderate collisionalities), they overestimate the peak heat flow by as much as 35% and do not predict preheat in the more relevant case where there is a large temperature difference. The SNB model, however, agrees better with VFP results for the latter problem if care is taken with the definition of the mean free path. Additionally, we present for the first time a comparison of the SNB model against a VFP code for a hohlraum-relevant problem with inhomogeneous ionisation and show that the model overestimates the heat flow in the helium gas-fill by a factor of ∼2 despite predicting the peak heat flux to within 16%.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Solidification and grain formation in alloys: a 2D application of the grand-potential-based phase-field approach

Solidification is a significant step in the forming of crystalline structures during various manufacturing and material processing techniques. Solidification characteristics and the microstructures formed during the process dictate the properties and performance of the materials. Hence, understanding how the process conditions relate to various microstructure formations is paramount. In this work, a grand-potential-based multi-phase, multi-component, multi-order-parameter phase-field model is used to demonstrate the solidification of alloys in 2D. This model has several key advantages over other multi-phase models such as it decouples the bulk energy from the interfacial energy, removes the constraints for the phase concentration variable, and prevents spurious third-phase formation at the two phase interfaces. Here, the model is implemented in a finite-element-based phase-field modeling code. The role of various modeling parameters in governing the solidification rate and the shape of the solidified structure is evaluated. It is demonstrated that the process conditions such as temperature gradient, thermal diffusion, cooling rate, etc, influence the solidification characteristics by altering the level of undercooling. Furthermore, the capability of the model to capture directional solidification and polycrystalline structure formation exhibiting various grain shapes is illustrated. In both these cases, the process conditions have been related to the growth rate and associated shape of the dendritic structure. As a result, this work serves as a stepping stone towards resolving the larger problem of understanding the process–structure–property–performance correlation in solidified materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Solving disorder in (3D) real space: a comparative study of the three-dimensional difference pair distribution function and atomic resolution holography reconstructions

The quantitative analysis of local ordering principles in disordered crystalline systems has gained much attention over the past few years, as it is often considered crucial for optimizing material functionality. This development has been driven by significant advancements in computational and experimental methods, which have led to the establishment and widespread use of various analytical techniques. In this study, we perform model calculations to compare the effectiveness of atomic resolution holography and three-dimensional difference pair distribution function analysis (3D-ΔPDF). Using Cu 3 Au as a model system, we demonstrate an approach to derive local order parameters quantitatively and show that both techniques are well suited to quantifying chemical short-range order correlations and local bond-distance variations. By evaluating the strengths and limitations of both techniques, we advocate for their combined use to solve complex short-range order problems accurately.

3D-ΔPDF↗
DOE OSTI2025

DIMPLES: Distributed Influence Maximization for Pandemic pLanning on Exascale Systems

We study exascale parallel algorithms for the selection of intervention or monitoring strategies in massive realistic socio-technical networks through scalable Influence Maximization (InfMax) algorithms. We employ novel techniques to enable efficient scaling on up to 8k nodes of OLCF Frontier, with 65k AMD GPUs and 458k AMD CPU cores. Current state-of-the-art InfMax tools are limited to networks with only a few million actors (vertices) and a few hundred million interactions (edges). By overcoming these limitations, we show that our approach is capable of processing a realistic social contact network of the United States with 285 million nodes and about 8 billion edges. This two orders-of-magnitude improvement over the previous state-of-the-art is obtained by leveraging algorithmic advancements for the InfMax problem and designing several problem-specific approaches to overlap communication with computation, improve GPU efficiency, and lower the application’s memory requirements. We evaluate strong scaling for computing 10k most influential seeds using up to 8k nodes of an exascale system, and weak scaling from 128 to 8k system nodes for seed sets ranging from 625 to 40k seeds. We achieve the fastest-known runtime of 25 minutes while performing 48 million diffusion simulations totaling 2.31 petabytes to identify 40k influential seeds using 8k nodes, and take 5.75 minutes to identify 10k seeds while using 4k nodes.

Minutoli, Marco [Pacific Northwest National Labora↗
DOE OSTI2017

Polarized 3He gas circulating technologies for neutron analyzers

We outline our project to develop a circulating polarized helium-3 system for developing of large, quasi-continuously operating neutron analyzers. The project consisted of four areas: 1) Development of robust external cavity narrowed diode laser output with spectral line width < 0.17 nm and power of 2000 W. 2) Development of large glass polarizing cells using cell surface treatments to obtain long relaxation lifetimes. 3) Refinements of the circulation system with an emphasis on gas purification and materials testing. 4) Design/fabrication of a new polarizer system. 5) Preliminary testing of the new polarizer. 1. Developed Robust High-Power Narrowed Laser The optical configuration of the laser was discussed in the proposal and will be reviewed in the body of this report. The external cavity is configured to mutually lock the wavelength of five 10-bar laser stacks. All the logistical milestones were been met and critical subsystems- laser stack manifold and power divider, external laser cavity, and output telescope- were assembled and tested at low power. Each individual bar is narrowed to ~0.05 nm; when combined the laser has a cumulative spectral width of 0.17 nm across the entire beam due to variations of the bars central wavelength by +/- 0.1 nm, which is similar to that of Volume Bragg Grating narrowed laser bars. This configuration eliminates the free-running “pedestal” that occurs in other external cavity diode lasers. The full-scale laser was completed in 2016 and was used in both the older and newer helium polarizers. This laser was operated at 75% power for periods of up to 8 hours. Once installed, the spectrum became slightly broader (~.25 nm) at full power; this is likely due to very slight misalignments that occurred during handling. 2. Developed the processes to create uniform sintered sol-gel coatings. Our work on cell development comprised: 1) Production of large GE180 cells and explore different means of cell preparation, and 2) Development of apply sol-gel coatings to the interior of both borosilicate and aluminosilicate cells. We applied six sol-gel coatings. By modifying the mixture and developing procedures to drain and dry the cell, we produced visually uniform coatings on the interior of the cells. We now have perfected that process as described below in our report. We were able to accelerate the testing of cells using an ex situ method that avoids installing each cell into a polarizer. In the project’s last year, we conducted 38 external tests of 8 different cells. We also installed two sol-gel coated cells in our polarizers. We created cell with long ex situ relaxation lifetimes, one of which exceeded 40 hours. However, when installed in the polarizer the measured lifetime is 8 hours or less. 3. Demonstrated cycling of polarized gas and ex situ cell testing We are now cycling polarized gas from the polarizer to glass vessels and back. This has allowed us, for the first time, to make ex situ T1 measurements of polarizing cells without installing them into the polarizer itself. This has greatly improved our productivity in producing cells and evaluating our cell preparation processes. We continued development of the gas handling system in parallel with fabricating new polarizer. The integrated system was tested by the end of 2016. We now regularly cycle gas into and out of the polarizer. 4. Completed new polarizer infrastructure and control systems. We completed the new polarizer infrastructure in November 2016. The polarizer subsystems are 1) the frame, 2) the oil flow system, 3) the gas handling system, 4) the pressure vessel, with embedded solenoid, 5) cell mounting hardware with heat spreaders, and 6) electrical power and instrumentation. 5. Carried out initial tests of polarizer. We completed initial testing of the polarizer in April and May of 2017. These tests were carried out for periods up to 6 hours with laser power between 750 and 1300 Watts. The laser performed well and the polarization with asymptotic to 45 percent, which was below expectations. This low value resulted from a stationary thermal inversion in the cell that caused most of the laser power to be absorbed near the laser inlet window and deprived the lower portions of the cell of pumping laser light. Possible solutions to this problem include enhanced cooling of the cell near the laser entry and slight detuning of the laser. 6. Ongoing work. Our polarizer development efforts are ongoing to pursue our interest in neutron analyzers, nuclear targets, and providing helium for medical imaging. Current tests in the pipeline include: 1. Testing cooling enhancements to improve laser penetration of spectrally narrow lasers; 2. Testing of a cell with isolation valves that minimizes diffusive contact with gas handling hardware during polarization; 3. Testing of smaller hybrid cells with reduced alkali loading; 4. Producing polarized helium-3 for MRI imaging at the University of Missouri.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Kinetic corrections from analytic non-Maxwellian distribution functions in magnetized plasmas

In magnetized plasma physics, almost all developed analytic theories assume a Maxwellian distribution function (MDF) and in some cases small deviations are described using the perturbation theory. The deviations with respect to the Maxwellian equilibrium, called kinetic effects, are required to be taken into account especially for fusion reactor plasmas. Generally, because the perturbation theory is not consistent with observed steady-state non-Maxwellians, these kinetic effects are numerically evaluated by very central processing unit (CPU)-expensive codes, avoiding the analytic complexity of velocity phase space integrals. We develop here a new method based on analytic non-Maxwellian distribution functions constructed from non-orthogonal basis sets in order to (i) use as few parameters as possible, (ii) increase the efficiency to model numerical and experimental non-Maxwellians, (iii) help to understand unsolved problems such as diagnostics discrepancies from the physical interpretation of the parameters, and (iv) obtain analytic corrections due to kinetic effects given by a small number of terms and removing the numerical error of the evaluation of velocity phase space integrals. This work does not attempt to derive new physical effects even if it could be possible to discover one from the better understandings of some unsolved problems, but here we focus on the analytic prediction of kinetic corrections from analytic non-Maxwellians. As applications, examples of analytic kinetic corrections are shown for the secondary electron emission, the Langmuir probe characteristic curve, and the entropy. This is done by using three analytic representations of the distribution function: the Kappa distribution function, the bi-modal or a new interpreted non-Maxwellian distribution function (INMDF). The existence of INMDFs is proved by new understandings of the experimental discrepancy of the measured electron temperature between two diagnostics in JET. As main results, it is shown that (i) the empirical formula for the secondary electron emission is not consistent with a MDF due to the presence of super-thermal particles, (ii) the super-thermal particles can replace a diffusion parameter in the Langmuir probe current formula, and (iii) the entropy can explicitly decrease in presence of sources only for the introduced INMDF without violating the second law of thermodynamics. Moreover, the first order entropy of an infinite number of super-thermal tails stays the same as the entropy of a MDF. The latter demystifies the Maxwell's demon by statistically describing non-isolated systems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Velocity-space Origins of the Pressure–Strain Interaction in Multipopulation Distributions and Its Application to Magnetic Reconnection

A forefront research question is how energy evolves in weakly collisional plasmas for which departures from local thermodynamic equilibrium (LTE) are significant. The standard approach is studying the terms in the non-LTE energy evolution equation derived by taking the second moment of the Boltzmann equation, but the resultant fluid metrics do not retain information about which particles at which velocities drive energy evolution. A widely studied channel for internal energy density evolution is the pressure–strain interaction. Here, we employ the kinetic pressure–strain, a phase-space diagnostic whose velocity-space integral recovers the pressure–strain interaction to disambiguate the contributions to the pressure–strain interaction from disparate particle populations in composite phase-space densities. We develop phase-space analogs of the pressure–strain interaction decompositions to provide the phase-space origins of normal versus sheared flow. We introduce the “kinetic strain-rate” tensor, the phase-space analog of the strain-rate tensor, which we argue is needed to interpret the phase-space origins of the pressure–strain interaction. To demonstrate the utility of these quantities, we investigate them for composite electron distributions near the electron diffusion region in two-dimensional particle-in-cell simulations of antiparallel symmetric magnetic reconnection. We find that the phase-space-based diagnostics isolate the roles of distinct populations. These results contribute to a growing body of work providing new methods for quantifying phase-space energy evolution for a broad array of processes, from magnetic reconnection to collisionless shocks and turbulence, opening new pathways for answering longstanding problems of particle energization in weakly collisional plasmas.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Generative AI in Supply Chain Management: Applications, Challenges, and Future Directions

Supply chain management (SCM) is undergoing rapid transformation due to increasing global complexity, demand volatility, and operational disruptions. Generative Artificial Intelligence (GenAI) has emerged as a powerful paradigm capable of synthesizing data, simulating operational scenarios, and enabling adaptive decision-making across supply chain networks. This paper presents a survey of GenAI’s role in SCM, focusing on its applications in predictive analytics, autonomous logistics, and fraud detection. Unlike traditional AI systems that rely primarily on predictive analytics, GenAI models, including large language models, generative adversarial networks, and diffusion-based architectures, enable the creation of synthetic supply chain scenarios and autonomous optimization strategies. This survey provides (1) a taxonomy of GenAI techniques for supply chain applications, (2) a comparative analysis of generative AI approaches with traditional machine learning, reinforcement learning, and blockchain-based methods, and (3) a discussion of key challenges such as data privacy, interpretability, and integration with legacy enterprise systems. Furthermore, we outline open research problems and propose directions for future research toward autonomous, resilient, and sustainable AI-driven supply chains.

15 - GEOTHERMAL ENERGY↗
DOE OSTI2015

Dynamics of metal-induced crystallization of ultrathin Ge films by rapid thermal annealing

Though Ge crystallization has been widely studied, few works investigate metal-induced crystallization of ultrathin Ge films. For 2 nm Ge films in oxide matrix, crystallization becomes challenging due to easy oxidation and low mobility of Ge atoms. Introducing metal atoms may alleviate these problems, but the functions and the behaviours of metal atoms need to be clarified. This paper investigates the crystallization dynamics of a multilayer structure 1.9 nm Ge/0.5 nm Al/1.5 nm Al{sub 2}O{sub 3} under rapid thermal annealing (RTA). The functions of metal atoms, like effective anti-oxidation, downshifting Raman peaks, and incapability to decrease crystallization temperature, are found and explained. The metal behaviours, such as inter-diffusion and defect generation, are supported with direct evidences, Al-Ge nanobicrystals, and Al cluster in Ge atoms. With these understandings, a two-step RTA process achieves high-quality 2 nm nanocrystal Ge films with Raman peak at 298 cm{sup −1} of FWHM 10.3 cm{sup −1} and atomic smooth interfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Impact of parametric uncertainty on estimation of the energy deposition into an irradiated brain tumor

Predictions of the total energy deposited into a brain tumor through X-ray irradiation are notoriously error-prone. We investigate how this predictive uncertainty is affected by uncertainty in both the location of the region occupied by a dose-enhancing iodinated contrast agent and the agent's concentration. This is done within the probabilistic framework in which these uncertain parameters are modeled as random variables. We employ the stochastic collocation (SC) method to estimate statistical moments of the deposited energy in terms of statistical moments of the random inputs, and the global sensitivity analysis (GSA) to quantify the relative importance of uncertainty in these parameters on the overall predictive uncertainty. A nonlinear radiation–diffusion equation dramatically magnifies the coefficient of variation of the uncertain parameters, yielding a large coefficient of variation for the predicted energy deposition. This demonstrates that accurate prediction of the energy deposition requires a proper treatment of even small parametric uncertainty. Our analysis also reveals that SC outperforms standard Monte Carlo, but its relative efficiency decreases as the number of uncertain parameters increases from one to three. A robust GSA ameliorates this problem by reducing this number.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗