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Статті в журналах з теми "Crystal size prediction"
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Zhang, Yongchun, and Michael F. Doherty. "Simultaneous prediction of crystal shape and size for solution crystallization." AIChE Journal 50, no. 9 (2004): 2101–12. http://dx.doi.org/10.1002/aic.10182.
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Соколовский, А. С., М. Н. Лубов, Н. А. Беседина, Ю. В. Трушин та М. В. Дубина. "Кинетическая модель формирования кристаллов белка в капиллярах методом контрдиффузии". Письма в журнал технической физики 44, № 11 (2018): 105. http://dx.doi.org/10.21883/pjtf.2018.11.46203.17068.
Повний текст джерелаАнотація:
AbstractA kinetic model of crystallization of lysozyme is proposed, and computer calculations of this process are carried out. The conditions for the formation of such crystals are determined. Under these conditions, individual crystals were grown that were suitable for X-ray examination. The developed model enables prediction of the quantity, size, and place of crystal nucleation inside the capillary.
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Mahmoud, Hanan Ahmed Hosni. "Transfer Learning in Inorganic Compounds’ Crystal Structure Classification." Crystals 13, no. 1 (January 2, 2023): 87. http://dx.doi.org/10.3390/cryst13010087.
Повний текст джерелаАнотація:
Deep learning consists of deep convolutional layers and an unsupervised feature selection phase. The feature selection of deep learning on a large size dataset can be employed in correlated prediction models with small size datasets. This methodology is titled deep transfer learning model and enhances prediction model generalization. In this research, we proposed a prediction model for the crystal structure classification of inorganic compounds. Deep learning models in structure classification are usually trained using a large size dataset of 300 K compounds from different quantum compounds dataset (DS1). The feature selection of the deep learning models is reused for selecting features in a small size dataset (with 30 K inorganic compounds and containing 150 different crystal structures) and three alloy classes. The selected features are then fed into a random decision forest prediction model as input. The proposed convolutional neural network (CNN) with transfer learning realizes an accuracy of 98.5%. The experiment results display the CPU time consumed by our model, comparing the time required by similar models. The CPU classification time of the proposed model is 21 s on average.
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Hashino, Tempei, and Gregory J. Tripoli. "The Spectral Ice Habit Prediction System (SHIPS). Part IV: Box Model Simulations of the Habit-Dependent Aggregation Process." Journal of the Atmospheric Sciences 68, no. 6 (June 1, 2011): 1142–61. http://dx.doi.org/10.1175/2011jas3667.1.
Повний текст джерелаАнотація:
Abstract The purpose of this paper is to assess the prediction of particle properties of aggregates and particle size distributions with the Spectral Ice Habit Prediction System (SHIPS) and to investigate the effects of crystal habits on aggregation process. Aggregation processes of ice particles are critical to the understanding of precipitation and the radiative signatures of cloud systems. Conventional approaches taken in cloud-resolving models (CRMs) are not ideal to study the effects of crystal habits on aggregation processes because the properties of aggregates have to be assumed beforehand. As described in Part III, SHIPS solves the stochastic collection equation along with particle property variables that contain information about crystal habits and maximum dimensions of aggregates. This approach makes it possible to simulate properties of aggregates explicitly and continuously in CRMs according to the crystal habits. The aggregation simulations were implemented in a simple model setup, assuming seven crystal habits and several initial particle size distributions (PSDs). The predicted PSDs showed good agreement with observations after rescaling except for the large-size end. The ice particle properties predicted by the model, such as the mass–dimensional (m-D) relationship and the relationship between diameter of aggregates and number of component crystals in an aggregate, were found to be quantitatively similar to those observed. Furthermore, these predictions were dependent on the initial PSDs and habits. A simple model for the growth of a particle’s maximum dimension was able to simulate the typically observed fractal dimension of aggregates when an observed value of the separation ratio of two particles was used. A detailed analysis of the collection kernel indicates that the m-D relationship unique to each crystal habit has a large impact on the growth rate of aggregates through the cross-sectional area or terminal velocity difference, depending on the initial equivalent particle distribution. A significant decrease in terminal velocity differences was found in the inertial flow regime for all the habits but the constant-density sphere. It led to formation of a local maximum in the collection kernel and, in turn, formed an identifiable mode in the PSDs. Remaining issues that must be addressed in order to improve the aggregation simulation with the quasi-stochastic model are discussed.
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Chen, Dongju, Shupei Li, and Jinwei Fan. "Effect of KDP-Crystal Material Properties on Surface Morphology in Ultra-Precision Fly Cutting." Micromachines 11, no. 9 (August 25, 2020): 802. http://dx.doi.org/10.3390/mi11090802.
Повний текст джерелаАнотація:
To study the effect of material properties on the surface morphology of potassium dihydrogen phosphate (KDP) crystals, an ultra-precision fly cutting machine tool with a single-point diamond tool was used to perform a cutting experiment on (100) crystal plane of the KDP crystal. The elastic modulus, shear modulus, hardness, and dislocation of KDP crystals are taken into the cutting force model by introducing the strain gradient plasticity theory. Since the size effect and dynamic response will affect the surface roughness during ultra-precision machining, the surface roughness of workpieces in ultra-precision fly cutting is hard to predict. Based on the previously established strain gradient plasticity theoretical model, cutting force model, and the dynamic characteristics of the ultra-precision fly cutting system, a surface morphology prediction model under the influence of KDP crystal material properties was established. Finally, the accuracy of the surface morphology prediction model was verified by ultra-precision fly cutting experiments, and identified the frequency range of the characteristic signal caused by the anisotropy of the KDP crystal from the frequency, thereby verifying the KDP crystal material properties has a significant effect on the surface of the machined workpiece roughness.
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Dixon, Anthony G., and Robert W. Thompson. "Prediction of the zeolite crystal size distribution in batchwise hydrothermal synthesis." Zeolites 6, no. 3 (May 1986): 154–60. http://dx.doi.org/10.1016/0144-2449(86)90041-2.
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Arsiccio, Andrea, Antonello A. Barresi, and Roberto Pisano. "Prediction of Ice Crystal Size Distribution after Freezing of Pharmaceutical Solutions." Crystal Growth & Design 17, no. 9 (August 15, 2017): 4573–81. http://dx.doi.org/10.1021/acs.cgd.7b00319.
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McDonald, Matthew A., Andreas S. Bommarius, Martha A. Grover, and Ronald W. Rousseau. "Direct Observation of Growth Rate Dispersion in the Enzymatic Reactive Crystallization of Ampicillin." Processes 7, no. 6 (June 22, 2019): 390. http://dx.doi.org/10.3390/pr7060390.
Повний текст джерелаАнотація:
Prediction and control of crystal size distributions, a prerequisite for production of consistent crystalline material in the pharmaceutical industry, requires knowledge of potential non-idealities of crystal growth. Ampicillin is one such medicine consumed in crystal form (ampicillin trihydrate). Typically it is assumed that all crystals of the same chemical and geometric type grow at the same rate, however a distribution of growth rates is often observed experimentally. In this study, ampicillin produced enzymatically is crystallized and a distribution of growth rates is observed as individual crystals are monitored by microscopy. Most studies of growth rate dispersion use complex flow apparatuses to maintain a constant supersaturation or imprecise measurements of size distributions to reconstruct growth rate dispersions. In this study, the controllable enzyme reaction enables the same information to be gathered from fewer, less complicated experiments. The growth rates of individual ampicillin trihydrate crystals were found to be normally distributed, with each crystal having an intrinsic growth rate that is constant in time. Differences in the individual crystals, such as different number and arrangement of dislocations and surface morphology, best explain the observed growth rates. There is a critical supersaturation below which growth is not observed, thought to be caused by reactants adsorbing to the crystal surface and pinning advancing growth steps. The distribution of critical supersaturation also suggests that individual crystals’ surface morphologies cause a distribution of growth rates.
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Xia, Deyu, Ning Li, Pengju Ren, and Xiaodong Wen. "Prediction Of Material Properties By Neural Network Fusing The Atomic Local Environment And Global Description: Applied To Organic Molecules And Crystals." E3S Web of Conferences 267 (2021): 02059. http://dx.doi.org/10.1051/e3sconf/202126702059.
Повний текст джерелаАнотація:
Machine learning has brought great convenience to material property prediction. However, most existing models can only predict properties of molecules or crystals with specific size, and usually only local atomic environment or molecular global descriptor representation be used as the characteristics of the model, resulting in poor model versatility and cannot be applied to multiple systems. We propose a method that combines the description of the local atomic environment and the overall structure of the molecule, a fusion model consisting of a graph convolutional neural network and a fully connected neural network is used to predict the properties of molecules or crystals, and successfully applied to QM9 organic molecules and semiconductor crystal materials. Our method is not limited to a specific size of a molecule or a crystal structure. According to the calculation principle of the properties of the material molecules, the influences of the local atomic environment and the overall structure of the molecules on the properties are respectively considered, an appropriate weighting ratio is selected to predict the properties. As a result, the prediction performance has been greatly improved. In fact, the proposed method is not limited to organic molecules and crystals and is also applicable to other structures, such as clusters.
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Tu, Yuhui, Seán B. Leen, and Noel M. Harrison. "A high-fidelity crystal-plasticity finite element methodology for low-cycle fatigue using automatic electron backscatter diffraction scan conversion: Application to hot-rolled cobalt–chromium alloy." Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 235, no. 8 (May 11, 2021): 1901–24. http://dx.doi.org/10.1177/14644207211010836.
Повний текст джерелаАнотація:
The common approach to crystal-plasticity finite element modeling for load-bearing prediction of metallic structures involves the simulation of simplified grain morphology and substructure detail. This paper details a methodology for predicting the structure–property effect of as-manufactured microstructure, including true grain morphology and orientation, on cyclic plasticity, and fatigue crack initiation in biomedical-grade CoCr alloy. The methodology generates high-fidelity crystal-plasticity finite element models, by directly converting measured electron backscatter diffraction metal microstructure grain maps into finite element microstructural models, and thus captures essential grain definition for improved microstructure–property analyses. This electron backscatter diffraction-based method for crystal-plasticity finite element model generation is shown to give approximately 10% improved agreement for fatigue life prediction, compared with the more commonly used Voronoi tessellation method. However, the added microstructural detail available in electron backscatter diffraction–crystal-plasticity finite element did not significantly alter the bulk stress–strain response prediction, compared to Voronoi tessellation–crystal-plasticity finite element. The new electron backscatter diffraction-based method within a strain-gradient crystal-plasticity finite element model is also applied to predict measured grain size effects for cyclic plasticity and fatigue crack initiation, and shows the concentration of geometrically necessary dislocations around true grain boundaries, with smaller grain samples exhibiting higher overall geometrically necessary dislocations concentrations. In addition, minimum model sizes for Voronoi tessellation–crystal-plasticity finite element and electron backscatter diffraction–crystal-plasticity finite element models are proposed for cyclic hysteresis and fatigue crack initiation prediction.
Дисертації з теми "Crystal size prediction"
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HARGUINDEGUY, MAITE. "Infrared thermography for freeze-drying applications: from ice crystal size prediction to primary drying process monitoring and design space determination." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2959955.
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Conn, Brian E. "Revealing the Magic in Silver Magic Number Clusters: The Development of Size-Evolutionary Patterns for Monolayer Coated Silver-Thiolate Nanoclusters." University of Toledo / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1481294367098454.
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Skyner, Rachael Elaine. "Hydrate crystal structures, radial distribution functions, and computing solubility." Thesis, University of St Andrews, 2017. http://hdl.handle.net/10023/11746.
Повний текст джерелаАнотація:
Solubility prediction usually refers to prediction of the intrinsic aqueous solubility, which is the concentration of an unionised molecule in a saturated aqueous solution at thermodynamic equilibrium at a given temperature. Solubility is determined by structural and energetic components emanating from solid-phase structure and packing interactions, solute–solvent interactions, and structural reorganisation in solution. An overview of the most commonly used methods for solubility prediction is given in Chapter 1. In this thesis, we investigate various approaches to solubility prediction and solvation model development, based on informatics and incorporation of empirical and experimental data. These are of a knowledge-based nature, and specifically incorporate information from the Cambridge Structural Database (CSD). A common problem for solubility prediction is the computational cost associated with accurate models. This issue is usually addressed by use of machine learning and regression models, such as the General Solubility Equation (GSE). These types of models are investigated and discussed in Chapter 3, where we evaluate the reliability of the GSE for a set of structures covering a large area of chemical space. We find that molecular descriptors relating to specific atom or functional group counts in the solute molecule almost always appear in improved regression models. In accordance with the findings of Chapter 3, in Chapter 4 we investigate whether radial distribution functions (RDFs) calculated for atoms (defined according to their immediate chemical environment) with water from organic hydrate crystal structures may give a good indication of interactions applicable to the solution phase, and justify this by comparison of our own RDFs to neutron diffraction data for water and ice. We then apply our RDFs to the theory of the Reference Interaction Site Model (RISM) in Chapter 5, and produce novel models for the calculation of Hydration Free Energies (HFEs).
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Afsi, Nawel. "Contrôle des procédés représentés par des équations aux dérivées partielles." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSE1033.
Повний текст джерелаАнотація:
L’objectif de ce travail est le contrôle des procédés représentés par des équations aux dérivées partielles. Deux procédés ont été considérés. Le premier procédé est un procédé de cristallisation en batch. L’objectif du contrôle est la génération d’une distribution des tailles de cristaux (DTC) ayant une taille moyenne adéquate. Tout d’abord, nous avons utilisé observateur à grand gain en cascade pour estimer cette taille moyenne en utilisant que la température du cristallisoir et la concentration du soluté. Ensuite, différents scénarios ont été testés afin de comparer les performances des différentes structures de la commande sans modèle. Le deuxième procédétraité est un procédé de polymérisation par ouverture de cycle du lactide. Cette réaction est très sensible aux impuretés. Alors, deux stratégies de contrôle ont été proposé afin de rétablir les conditions nominales en cas de dérive qui sont l’optimisation dynamique et la commande prédictiveThis work aims to control the processes represented by partial differential equations. Two processes were considered. The first process is a batch crystallization process. The aim of the control is to generate a crystal size distribution (CSD) with an appropriate mean size. First, we used a high gain cascade observer to estimate this average size using only the crystallizer temperature and solute concentration. Then, different scenarios were tested to compare the performance of the different structures of the control system without a model. The second process treated is a lactide polymerization process. This reaction is very sensitive to impurities. So, two control strategies were proposed to restore the nominal conditions in case of drift, which are the dynamic optimization and predictive control
Частини книг з теми "Crystal size prediction"
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Stephens, Graeme. "Cirrus, Climate, and Global Change." In Cirrus. Oxford University Press, 2002. http://dx.doi.org/10.1093/oso/9780195130720.003.0024.
Повний текст джерелаАнотація:
Understanding the climate of Earth and the way climate varies in time requires a quantitative understanding of the way water cycles back and forth between the atmosphere and at the Earth's surface. The exchanges of water between the surface and atmosphere establish the hydrological cycle, and it is the influence of this cycle on the energy budget of Earth that is central not only to understanding present climate but also to the prediction of climate change. Processes relating to the smallest of the reservoirs of water—namely, the atmospheric branch of the hydrological cycle—play an especially critical role in climate change. Water in vapor phase is the critical greenhouse gas (e.g., Chahine 1992) providing much studied feedbacks on climate forcing (Lindzen 1990; Rind et al. 1991; Stephens and Greenwald 1991; Inamdar and Ramanathan 1998; Hall and Manabe 1999). Water in the form of condensed, precipitation-sized particles is an important source of energy fueling circulation systems and is the fundamental supply of fresh water to life on Earth. Liquid water cloud droplets significantly modulate the radiative budget of the planet (e.g., Wielicki et al. 1995). Water that exists as ice particles suspended in the atmosphere is perhaps the smallest of the water reservoirs of the atmosphere, yet these ice crystals when distributed as part of large-scale cirrus clouds exert a disproportionate influence on the energy and water budgets of the planet. This chapter briefly speculates on the important ways cirrus clouds affect the Earth's climate. The topics discussed are central to what is referred to as the cloud-climate problem, which might be schematically represented in terms of the coupled processes represented in figure 20.1. The two most critical scientific questions associated with the cloud-climate problem are also stated in figure 20.1. Answers to these questions require a clearer understanding of how the large-scale circulation of the atmosphere governs cloud formation and evolution, how these clouds heat and moisten the atmosphere, and how this heating and moistening effect in turn feeds back to influence the dynamical and thermodynamical properties of the atmosphere.
Тези доповідей конференцій з теми "Crystal size prediction"
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Castelluccio, Gustavo M., and David L. McDowell. "Fatigue Life Prediction of Microstructures." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-85710.
Повний текст джерелаАнотація:
The formation and early growth of fatigue cracks in the high cycle fatigue regime is influenced by microstructuctural features such as grain size and morphological and crystallographic texture. However, most fatigue models do not predict the influence of the microstructure on early stages of crack formation, or they employ parameters that should be calibrated with experimental data from specimens with microstructures of interest. These post facto strategies are adequate to characterize materials, but they are not fully appropriate to aid in the design of fatigue-resistant engineering alloys. This paper presents a modeling framework that facilitates relative assessment of fatigue resistance among different microstructures. The scheme employs finite element simulations that explicitly render the microstructure and a methodology that estimates transgranular fatigue growth for microstructurally small cracks on a grain-by-grain basis, including consideration of growth within grains (embedded analytically) and stress redistribution as the cracks extend. The methodology is implemented using a crystal plasticity algorithm in ABAQUS and calibrated to study fatigue crack initiation of a bimodal grain size distribution found in RR1000 powder processed Ni-base superalloys for turbine disk applications.
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Ruilan Liu and Yang Xu. "Soft sensor based on least square support vector machine with limited memory for crystal particle size prediction in PTA purification process." In 2008 7th World Congress on Intelligent Control and Automation. IEEE, 2008. http://dx.doi.org/10.1109/wcica.2008.4594479.
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Chen, Hongqiang, Jeffrey W. Kysar, Y. Lawrence Yao, and Youneng Wang. "Experimental Characterization and Simulation of Three Dimensional Plastic Deformation Induced by Microscale Laser Shock Peening." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59661.
Повний текст джерелаАнотація:
Different experimental techniques and 3D FEM simulations are employed to characterize and analyze the three dimensional plastic deformation and residual strain/stress distribution for single crystal Aluminum under microscale laser shock peening assuming finite geometry. Single pulse shock peening at individual locations was studied. X-ray micro-diffraction techniques based on a synchrotron light source affords micron scale spatial resolution and is used to measure the residual stress spatial distribution along different crystalline directions on the shocked surface. Crystal lattice rotation due to plastic deformation is also measured with electron backscatter diffraction (EBSD). The result is experimentally quantified and compared with the simulation result obtained from FEM analysis. The influence of the finite size effect, crystalline orientation are investigated using single crystal plasticity in FEM analysis. The result of the 3D simulations of a single shock peened indentation are compared with the FEM results for a shocked line under 2D plain strain deformation assumption. The prediction of overall character of the deformation and lattice rotation fields in three dimensions will lay the ground work for practical application of μLSP.
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Wang, Jian, and Caizhi Zhou. "A Meso-Scale Fretting Fatigue Simulation Method Based on Submodelling Technique." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-68754.
Повний текст джерелаАнотація:
Abstract This work developed a macro-microscopic coupled simulation method for fretting fatigue simulations. The crystal plasticity finite element (CPFE) mode was used to predict the hotspot of crack nucleation in fretting fatigue loading. Our model has considered the microstructure features of metals, such as the grain size, grain orientations and dislocation slips, in the fretting fatigue simulations, most of which have been ignored in previous work. And the submodel approach has been adopted in our work to overcome the size limitation and minimize the effect of boundary constrains. After calibration of material parameters, the model has been validated by the Hertz’s analytic solution based on the contact mechanics. From the results of CPFE simulations, we can accurately identify the hotspot of crack nucleation in fretting fatigue loading based on the local the equivalent plastic strain. The global model-submodel coupling method proposed in this work provides a solution for prediction of crack initiation and the crack initiation life of fretting fatigue in metals.
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Owolabi, Gbadebo M., and David L. McDowell. "Microstructure-Sensitive Fatigue Design for Notched Components." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-10860.
Повний текст джерелаАнотація:
The current drive for the discovery and use of advanced materials in safety-critical components necessitates the development of reliable fatigue design tools. However, most of the current tools require a number of approximations based on heuristics and phenomenological data rather than solid theoretical underpinning and still yield unsatisfactory and inconsistent results when applied to complex components under in service loads. Microstructural inhomogeneities in the materials induce notch size effects, but are not explicitly accounted for in phenomenological methods. Accordingly, notch sensitivity remains a highly empirical subject in spite of significant advances in microstructure-sensitive modeling. More robust fatigue design tools should capture the cause and effect relation of microstructure to distribution of slip and driving forces for formation and early growth of small cracks in the notch root field. Recent developments in computational crystal plasticity and microstructure-scale modeling have provided deeper understanding of the complex correlations between properties and structures and further indicate the limitations of conventional fatigue life prediction approaches. These modeling approaches have the potential to substantially reduce the need for costly large scale experimental programs to determine scatter in fatigue, for example. At present, however, there is a lack of simulation-based methodologies for considering interactive effects of stress and strain field gradients at the notch-root and microstructure-scale behavior in predicting notch-root fatigue crack initiation. In this paper results from simulations within a well-defined notch root damage process zone are used along with a probabilistic mesomechanics approach to develop a framework for new microstructure-sensitive fatigue notch factor. In addition, probability distributions of fatigue strength and fatigue life to form small cracks are estimated from simulations, extending notch sensitivity to explicitly incorporate microstructure sensitivity and attendant size effects via probabilistic arguments.
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Zhao, Xinglong, Joa˜o Quinta da Fonseca, Andrew Sherry, and David Lidbury. "Grain-Scale Heterogeneity Effect on Mechanistic Modelling of Cleavage Fracture of a Ferritic RPV Steel Forging Material." In ASME 2008 Pressure Vessels and Piping Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/pvp2008-61569.
Повний текст джерелаАнотація:
Improving brittle fracture prediction is crucial for structural integrity assessment. In current safety assessments, fracture mechanics treats polycrystalline steels as homogeneous continua. In reality, deformation of structural steels is heterogeneous. Part of this heterogeneity is due to the elastic and plastic anisotropy of their constituent (often randomly orientated) grains. This paper will compare the predicted failure stresses from tensile tests performed on a ferritic pressure vessel steel using the crystal plasticity finite element approach alongside measured carbide distribution and classical Beremin cleavage model. Available tensile data of 22NiMoCr37 steel at low temperature (−91°C and −154°C) were analysed using Bridgman solutions to account for the necking effect on the stress state at the centre of necking where brittle cracking initiates. This stress state imposed on representative volume element (RVE) made up of 10×10×10 randomly orientated grains, whose deformation is simulated using crystal plasticity finite element modelling (CPFEM). Randomly distributed carbides were produced based on the measured carbide size distribution and density for this steel. By assuming carbides as Griffith microcracks, the cleavage fracture stress in each grain can be assessed based the maximum principal stress on the cleavage crystal plane and an assumed surface energy. By repeating the random carbide distribution 1,000 times, brittle fracture probability can be calculated. Detailed examination shows that the above approach is actually a verification of the BEREMIN local approach model for cleavage fracture. The modelling results were compared with the available ductility data at −91°C and the interpolated ductility data at −154°C at the centre of necking. It is foreseen that this approach will lead to improvements in brittle fracture modelling in heterogeneous ferritic steels by introducing realistic surface energies and real defect distributions in specific materials, when used alongside the CPFEM submodelling approach.
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Zhang, T., G. X. Wang, F. Ladeinde, and V. Prasad. "Thermo-Solutal Issues in Very Large Diameter Silicon Crystal Growth." In ASME 1998 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/imece1998-1094.
Повний текст джерелаАнотація:
Abstract Although 200 mm diameter wafers presently dominate the semiconductor market, 300 mm wafers are expected to be commonly used at the turn of the century. Larger wafer diameter is needed in order to increase the chip yield due to increase in chip size due to higher integration and performance of both memory (DRAM) and microprocessor (MPU) devices. mm diameter single crystals. As the crystal diameter is increased from 200 mm to 300 mm or larger, the size of the melt increases significantly leading to much more complex melt dynamics. Turbulent melt flow phenomena make the predictions and control of growth conditions very difficult. Understanding of the heat and mass transport in large melt systems is therefore critical to the growth and control of high-quality, large-diameter silicon single crystals. This paper examines various thermal-solutal issues related to Czochralski growth of 300 mm silicon crystals such as turbulence in the melt, heat transfer, oxygen transport and control, and the recent progress is reviewed.
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Ma, Binjian, and Debjyoti Banerjee. "Predicting Particle Size Distribution in Nanofluid Synthesis." In ASME 2017 Heat Transfer Summer Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/ht2017-5048.
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Wet chemistry approaches have been widely used to synthesize nanoparticle suspensions with different size and shape. Controlling particle size is crucial for tailoring the properties of the nanofluid. In this study, we simulated the particle size growth during a thermal-chemical nanofluid synthesis routine. The simulation was based on the population balance model for aggregation kinetics, which is coupled with thermal decomposition, nucleation and crystal growth kinetics. The simulation result revealed a typical burst nucleation mechanism towards self-assembly of supersaturated monomers in the nanoparticle formation process and the shift from monodispersed particles to polydispersed particles by the particle-particle coagulation.
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Bardetsky, Alexander, Helmi Attia, and Mohamed Elbestawi. "A Fracture Mechanics Approach to the Prediction of Tool Wear in Dry High Speed Machining of Aluminum Cast Alloys: Part 1 — Model Development." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-80620.
Повний текст джерелаАнотація:
The analysis of the mechanism of cutting tool wear in high speed machining of cast aluminum alloys is conducted in this research work. The result of analysis indicates that the interaction between the hard silicon constituencies of the alloy and the surface of the cutting tool is the most detrimental to tool life. The wear of the cutting tool in such interactions, governed by fatigue wear mechanism, is directly proportional to silicon content of the alloy, silicon grain size and to the tool’s loading conditions. In order to predict the tool wear in machining aluminum cast alloys, a new wear model is developed. The fracture mechanics approach in wear rate estimation is implemented in this model. As an input data for the tool wear modeling, the normal and tangential stresses, acting on the flank of cutting tool are used. The fracture mechanics analysis of the subsurface crack propagation in the cobalt binder of cemented carbide cutting tool material is performed using a finite element (FE) model of the tool-workpiece sliding contact. The real microstructure of cemented carbide is incorporated in the FE model of tool-workpiece contact, and elastic-plastic properties of cobalt, defined by continuum theory of crystal plasticity are introduced in the model by UMAT subroutine of the ABAQUS® FE software. The crack propagation rate, determined from FE modeling, is used then in the model of cutting tool wear, developed in this work. This model is capable to predict the wear rate of cutting tool, base on the microstructural characteristics of the cutting tool and workpiece material and the tool’s loading conditions. The model can be used for cutting tool life assessment and management in high speed machining of Al-Si alloys in an industrial setting.
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Thomas, Sarah A., Robert S. Hixson, M. Cameron Hawkins, and Oliver T. Strand. "Wave speeds in single-crystal and polycrystalline copper." In 2019 15th Hypervelocity Impact Symposium. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/hvis2019-007.
Повний текст джерелаАнотація:
Abstract While the equation of state for copper has been fairly well studied, wave speeds at low stress are not as well known. Systematic errors may be present in the lowest stress data presented in the Marsh [1] compendium due to the use of the flash gap method to collect the data. Additionally, little data has been gathered on the wave speeds in single-crystal copper, which may vary from polycrystalline due to the different longitudinal and shear sound speeds. Hugoniot information at low pressures is useful in constraining and improving predictive hydrodynamic codes. Knowledge of single-crystal behavior provides input for mesoscale computer models that use tens-of-micron-sized grains of single crystals to build a model of polycrystalline systems. We undertook experiments to measure wave speeds in polycrystalline and single-crystal copper at low pressures using a novel technique to limit error, and to determine if single-crystal shock velocities are systematically different than polycrystalline shock velocities at the same stress. The best previous research on this topic is from Chau et al. [2] at relatively high shock stress; they reported no observed difference between orientations. It is of interest to do careful measurements at low stress, and that is the principal goal of this work.