A Mathematical Framework for Uncertainty Quantification in Multimodal Image Analysis via Probabilistic Clustering Models.
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Irradiation examination shows that gas bubble swelling kinetics is much faster after irradiation-induced recrystallization than that prior recrystallization in UMo fuels. It implies that gas bubbles in coarse grains and small recrystallized grains have different growth behavior. In this work, a phase-field model of gas bubble evolution integrating microstructure dependent cluster dynamics has been developed, for the first time, to study the gas bubble swelling behavior in the recrystallization zone of UMo fuels. Generation, diffusion, reaction, sink, emission and clustering of vacancies and interstitials are described by the cluster dynamics model while a phase-field model is used to describe the evolution of non-equilibrium gas bubbles including nucleation and growth. With the coupled model, the effect of defect generation rate, clustering rate, interstitial emission and sink rates on grain boundaries on the gas bubble evolution are systematically simulated. A set of model parameters (defect generation rate, clustering rate, interstitial emission and sink rates) is determined by comparing measured and simulated gas bubble swelling kinetics. The results demonstrate that interstitial clustering is one of the important physical mechanisms which results in a fast gas bubble swelling kinetics in the recrystallization zone. The developed model can also be extended to study the associated growth of defect and second phase precipitates often observed in irradiated materials.
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The pattern of erosion of a plateau along an escarpment may be modeled usng cluster growth techniques, recently popularized in models of drainage network evolution. If erosion on the scarp takes place in discrete events at rates subject to local substrate strength, the whole range of behavior is described by a combination of three cluster growth mechanisms: invasion percolation, Eden growth and diffusion-limited aggregation (DLA). These model the relative importance of preexisting substrate strength, background weathering, and seepage weathering and erosion respectively. The rate of seepage processes is determined by the efflux of groundwater at the plateau margin, which in turn is determined by the pressure field in the plateau aquifer. If this process acted alone, it would produce erosion patterns in the form of Laplacian fractals, with groundwater recharge from a distant source, or Poissionian fractals, with groundwater recharge uniform over the plateau. DLA is used to mimic the Laplacian or Poissonian potential field and the corresponding seepage growth process. The scaling structure of clusters grown by pure DLA, invasion percolation, or Eden growth is well known; this study presents a model which combines all three growth mechanisms for the first time. Mixed growth processes create clusters with different scaling properties and morphologies over distinct length scale ranges, and this is demonstrable in natural examples of plateau erosion.
Calculations for n-decane drops evaporating in a spherical cluster surrounded by unvitiated ambient air at atmospheric pressure were performed using two previously proposed cluster models. Both cluster models predict that turbulent transport effects are more important in the case of small clusters. This is due to the smaller volume to surface ratio and thus to the greater transport of hot unvitiated gas to the drops in order to promote evaporation. The results obtained are compared with those of two turbulent models for each one of the 'trapping factors' and similarity models.
Electron beam technique for measuring density and temperature in base region flow field of scale model of clustered rocket engine configurations
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For this work, we perform simulations of intra-granular fission gas bubble evolution in UO 2 using both a relatively simple, computationally inexpensive single-size model and a detailed cluster dynamics model. Simulations encompass 36 experimental cases from 4 different databases, covering various temperature and burnup levels. We systematically compare results from the two models to each other and to post-irradiation experimental data of bubble average size and number density. Overall, the model-to-model comparisons reveal an excellent agreement across the set of simulations. This outcome indicates that, in spite of the underlying assumptions, the single-size model provides a good approximation of the complex physical behavior that is more rigorously described by the cluster dynamics model. Qualitatively, both models reproduce the trends of the experimental data with temperature and burnup correctly. Quantitatively, calculated results are either in good agreement with the data or within errors that appear consistent with the inherent uncertainties. Moreover, for the single-size model, we demonstrate and assess a multiscale approach whereby values for the fission gas atom diffusion coefficient from separate atomistic calculations are used. Systematic comparisons to experimental data point out a credible accuracy of the multiscale model.
To follow up on the unexpectedly good performance of several coupled-cluster models with approximate inclusion of 3-body clusters we performed a more complete assessment of the 3CC method for accurate computational thermochemistry in the standard HEAT framework. New spin-integrated implementation of the 3CC method applicable to closed- and open-shell systems utilizes a new automated toolchain for derivation, optimization, and evaluation of operator algebra in many-body electronic structure. We found that with a double-ζ basis set the 3CC correlation energies and their atomization energy contributions are almost always more accurate (with respect to the CCSDTQ reference) than the CCSDT model as well as the standard CCSD(T) model. The mean absolute errors in cc-pVDZ {3CC, CCSDT, and CCSD(T)} electronic (per valence electron) and atomization energies relative to the CCSDTQ reference for the HEAT data set, were {24, 70, 122} μE h /e and {0.46, 2.00, 2.58} kJ/mol, respectively. The mean absolute errors in the complete-basis-set limit {3CC, CCSDT, and CCSD(T)} atomization energies relative to the HEAT model reference, were {0.52, 2.00, and 1.07} kJ/mol, The significant and systematic reduction of the error by the 3CC method and its lower cost than CCSDT suggests it as a viable candidate for post- CCSD(T) thermochemistry applications, as well as the preferred alternative to CCSDT in general.
Clustering of gaseous sulfuric acid (SA) enhanced by dimethylamine (DMA) is a major mechanism for new particle formation (NPF) in polluted atmospheres. However, uncertainty remains regarding the SA–DMA nucleation parameterization that reasonably represents cluster dynamics and is applicable across various atmospheric conditions. This uncertainty hinders accurate three-dimensional (3-D) modeling of NPF and the subsequent assessment of its environmental and climatic impacts. Here we extensively compare different cluster-dynamics-based parameterizations for SA–DMA nucleation and identify the most reliable one through a combination of box model simulations, 3-D modeling, and in situ observations. Results show that the parameterization derived from Atmospheric Cluster Dynamic Code (ACDC) simulations, incorporating the latest theoretical insights (DLPNO-CCSD(T)/aug-cc-pVTZ//ωB97X-D/6-311++G(3df,3pd) level of theory) and adequate representation of cluster dynamics, exhibits dependable performance in 3-D NPF simulation for both winter and summer conditions in Beijing and shows promise for application in diverse atmospheric conditions. Another ACDC-derived parameterization, replacing the level of theory with RI-CC2/aug-cc-pV(T+d)Z//M06-2X/6–311++G(3df,3pd), also performs well in NPF modeling at relatively low temperatures around 280 K but exhibits limitations at higher temperatures due to inappropriate representation of SA–DMA cluster thermodynamics. Additionally, a previously reported parameterization incorporating simplifications is applicable for simulating NPF in polluted atmospheres but tends to overestimate particle formation rates under conditions of elevated temperature (>∼300 K) and low-condensation sink (<∼3×10 -3 s −1 ). Our findings highlight the applicability of the new ACDC-derived parameterization, which couples the latest SA–DMA nucleation theory and holistic cluster dynamics, in 3-D NPF modeling. The ACDC-derived parameterization framework provides a valuable reference for developing parameterizations for other nucleation systems.
Model low-mass globular-cluster stars were evolved with their helium allowed to diffuse under the influence of gravity, thermal diffusion, and concentration gradient. The evolution tended to speed up. Also, the turnoff point moved toward lower luminosity and slightly lower surface temperature. If the luminosity at turnoff is used as the sole criterion for determining the age of a globular cluster, the inferred ages of such clusters are reduced by about 22% from starting values in the vicinity of 15 billion years.
There is no doubt that a huge gap exists in understanding heterogeneous catalysis between a cluster model of a few atoms and a bulk model of periodic slabs. Nanoparticles, which are crucial in heterogeneous catalysis in industry, lie in the middle of the gap. We present here our work on the computational modelling of molybdenum carbide nanoparticles (MCNPs) as the catalysts for the upgrading of oil sands in the in-situ environment, using benzene hydrogenation as a model reaction. With a cluster model, efforts were first made to understand the mechanism of the reaction with a density functional theory (DFT) study on the adsorption of benzene and its hydrogenation product – cyclohexane, as well as the cyclic hydrogenation reaction intermediates on the Mo{sub 2}C(0001) surface. From the thermodynamic data, along with literature information, it was found that the benzene hydrogenation reaction on molybdenum carbide happens most likely through a Langmuir-Hinshelwood mechanism with the gradual lifting up of the benzene molecule. The electron localization function (ELF) was then used to help understand the nature of the interactions between the MCNPs, identifying strong multi-center interactions between the adsorbates and the MCNPs. To enable the treatment of larger nanoparticles, a fast semi-empirical density functional tight-binding (DFTB) method was parameterized. With this method, the potential energy profiles of benzene hydrogenation reactions on different sizes of MCNPs are calculated. The study was then extended to consider a MCNP embedded in solvent (benzene), using a quantum mechanical (DFTB) / molecular mechanical approach. Calculations on the free energies profiles with the umbrella sampling method show that the entropy of the MCNPs and the solvent are essential in understanding the catalytic activity of the transition metal related nanoparticles for solid/liquid heterogeneous catalysis.
Distribution of black spot defects and small clusters in 1 MeV krypton irradiated 3C-SiC has been investigated using advanced scanning transmission electron microscopy (STEM) and TEM. We find that two thirds of clusters smaller than 1 nm identified in STEM are invisible in TEM images. For clusters that are larger than 1 nm, STEM and TEM results match very well. A cluster dynamics model has been developed for SiC to reveal processes that contribute to evolution of defect clusters and validated against the (S)TEM results. Simulations showed that a model based on established properties of point defects (PDs) generation, reaction, clustering, and cluster dissociation, is unable to predict black spot defects distribution consistent with STEM observations. This failure suggests that additional phenomena not included in a simple point-defect picture may contribute to radiation-induced evolution of defect clusters in SiC and using our model we have determined the effects of a number of these additional phenomena on cluster evolution. Using these additional phenomena it is possible to fit parameters within physically justifiable ranges that yield agreement between cluster distributions predicted by simulations and those measured experimentally.
An accurate and transferable cluster-expansion model for complex quinary alloys is developed. Lattice Monte Carlo simulation enabled by this cluster-expansion model is used to investigate temperature-dependent atomic structure of alnico alloys, which are considered as promising high-performance non-rare-earth permanent-magnet materials for high-temperature applications. The results of the Monte Carlo simulations are consistent with available experimental data and provide useful insights into phase decomposition, selection, and chemical ordering in alnico. The simulations also reveal a previously unrecognized D 0 3 alloy phase. This phase is very rich in Ni and exhibits very weak magnetization. In conclusion, manipulating the size and location of this phase provides a possible route to improve the magnetic properties of alnico, especially coercivity.