查看更多>>摘要：We study the epitaxial, columnar growth of (multiply oriented) dendrites/cells for a spot melt in a poly-crystalline Al-Cu substrate using two-dimensional, phase-field, direct numerical simulations (DNS) at the full-melt-pool scale. Our main objective is to compare the expensive DNS model to a much cheaper but approximate "line "model in which a single-crystal phase-field simulation is confined to a narrow rectangular geometry. To perform this comparison, we develop algorithms that automatically extract quantities of interest (QoIs) from both DNS and line models. These QoIs allow us to quantitatively assess the assumptions in the line model and help us analyze its discrepancy with the DNS model. We consider four sets of heat source parameters, mimicking welding and additive manufacturing conditions, that create a combination shallow and deep melt pools. Our largest DNS simulation used 16K x 14K grid points in space. Our main findings can be summarized as follows. Under AM conditions, the QoIs of line models are in excellent agreement with the full DNS results for both shallow and deep melt pools. Under welding conditions, the primary spacing of the DNS model is smaller than the prediction of line model. We identify a geometric crowding effect that accounts for the discrepancies between the DNS and line models. We propose two potential mechanisms that determine the response of the microstructure to geometric crowding.
Krasnikov, V. S.Gazizov, M. R.Mayer, A. E.Bezborodova, P. A....
14页
查看更多>>摘要：The effect of precipitate hybridization on macroscopic strengthening in aluminum alloys is investigated on the example of Al-Cu alloy using multiscale approach combining molecular dynamics (MD), continuum modeling and discrete dislocation dynamics (DDD). Non-hybrid and hybrid {100}Al plates are considered to involve theta'-phase and theta'-phase in the core and Guinier-Preston zone (GP-like) structure along the broad interfaces, respectively. MD simulations evidence a complex dislocation-precipitate interaction mechanism involving bypassing of both hybrid and non-hybrid {100}(Al) plates by dislocations at early deformation stages and their shearing by the following dislocations. MD results are used to calibrate a continuum model of dislocation precipitate interactions in 2D DDD. The shear strength of alloy with hybrid precipitates is found to be 20% higher than that for non-hybrid plates at the same Cu content exceeding 2 wt%.
Hiremath, PraveenkumarMelin, SolveigBitzek, ErikOlsson, Par A. T....
18页
查看更多>>摘要：In the present work, we have evaluated the performance of different embedded atom method (EAM) and second-nearest neighbour modified embedded atom method (2NN-MEAM) potentials based on their predictive capabilities for modelling fracture in single-and bicrystalline tungsten. As part of the study, a new 2NN-MEAM was fitted with emphasis on reproducing surface, unstable stacking fault and twinning energies as derived from density functional theory (DFT) modelling. The investigation showed a systematic underestimation of surface energies by most EAM potentials, and a significant variation in unstable stacking and twinning fault energies. Moreover, the EAM potentials in general lack the ability to reproduce the DFT traction-separation (TS) curves. The shorter interaction length and higher peak stress of the EAM TS curves compared to the 2NN-MEAM and DFT TS curves result in one order of magnitude higher lattice trapping than for cracks studied with 2NN-MEAM. These differences in lattice trapping can lead to significant qualitative differences in the fracture behaviour. Overall, the new 2NN-MEAM potential best reproduced fracture-relevant material properties and its results were consistent with fracture experiments. Finally, the results of fracture simulations were compared with analytical predictions based on Griffith and Rice theories, for which emerging discrepancies were discussed.
查看更多>>摘要：Innovations in manufacture of graphene-based nano-devices are principally the outcome of engineering of the nanostructure, while advanced nano-transistors, thermal logic nano-circuits, and thermal nano-diodes are multi-component tailor-made nanomaterials with precise molecular layout. To achieve such delicate graphene-based nanostructures, it is essential to optimize both materials and geometrical parameters. Herein we introduce nozzle-like graphene (G)/boron nitride (BN) nanostructures with very high unidirectional thermal rectification efficiency applying classical molecular dynamics (MD) simulations using Tersoff potential. A series of nozzle-like G/BN and BN/G nanoribbons with variable throat width, L (5-50 angstrom) and convergence angle, theta (20-90 degrees) under average T = 300 K and Delta T = 40 K (temperature differences between the thermal baths) situation were simulated to provide a complete image of thermal rectification for such nozzle-like nanostructures to be intended as thermal nano-diodes. Thermal conductivity and rectification analyses unveiled nozzle-like G/BN devices with a comparatively excellent unidirectional thermal rectification of similar to 25%, at L = 40 angstrom and theta = 60 degrees, which was conceptualized in view of phonon scattering perspective. We believe that the outcome of this survey would open new avenues in manufacturing tailor-made graphene-based nano-diodes.
查看更多>>摘要：A robust, modular, and ab initio high-throughput workflow is presented to automatically match and char-acterize solid-solid interfaces using density functional theory calculations with automatic error corrections. The potential energy surface of the interface is computed in a highly efficient manner, exploiting the high-symmetry points of the two mated surfaces. A database is automatically populated with results to ensure that already available data are not unnecessarily recomputed. Computational parameters and slab thicknesses are converged automatically to minimize computational cost while ensuring accurate results. The surfaces are matched according to user-specified maximal cross-section area and mismatches. Example results are presented as a proof of concept and to show the capabilities of our approach that will serve as the basis for many more interface studies.
查看更多>>摘要：Nanocars have been proposed to transport nanomaterials on the surface. Study of the mechanism of the motion of nanocars has attracted a lot of interests due to the potential ability of these nano-vehicles in the construction of nanostructures with bottom-up approach. Using molecular dynamics simulations, we study the motion of two nano-vehicles named "Nanocar" and "Nanotruck" on hexagonal boron-nitride monolayer. The obtained results reveals that, boron-nitride is an appropriate option to obtain higher mobility of nanocars compared with metal substrates. Considering different temperatures reveals that nanocars start to move on the BN at 200 K, while long range motions are observed at 400 K and higher temperatures. The flexibility of Nanocar chassis wastes a portion of its energy and reduces its displacement range. The anomaly parameters show that C60, Nanocar and Nano truck experience super-diffusive regime at 100 K and higher temperatures. Using diffusion coefficient and activation energy, rotational motion of the nanocars is evaluated. To accurately investigate the mechanism of nanocars motion, we find rotation of the wheels around their axles and the speed of Nanotruck in directions parallel and perpendicular to the chassis. The mentioned parameters indicate that wheel rolling mechanism is the minority mode of the nanocars motion and the translations commonly occur through sliding. At low temperatures, two stable configurations are found for the Nanocar, and reconfiguration is energetically possible at 200 K and higher temperatures. The results presented in this work are expected to facilitate the fabrication of nano structures using molecular machines.
查看更多>>摘要：Convolutional neural networks (CNNs) are implemented to expedite the determination of representative volume elements for microstructurally small cracks (RVEMSC). By definition, RVEMSC is the minimum volume of microstructure required around a microstructurally small crack (MSC) to achieve convergence of crack-front parameters with respect to volume size. In a previous study, RVEMSC was determined using a computationally expensive finite-element (FE) framework involving the simulation of many microstructural instantiations. With the aim of increasing the computational efficiency of determining RVEMSC, CNNs are leveraged herein to reduce the number of FE simulations required to determine RVEMSC. Using data from the previous FE-based RVEMSC study, CNNs are trained to predict RVE(MSC,ip & nbsp;)values, which quantify crack-front parameter convergence with respect to volume size for microstructural instantiation i evaluated at individual crack-front points p, given local microstructural and geometrical information. Predicted RVE(MSC,ip & nbsp;)values are subsequently used to estimate RVEMSC values. Studies are carried out to determine the optimal amount of training data, assess CNN-based RVEMSC estimation performance, and demonstrate the use of CNNs as microstructural-instantiation screening tools by enabling downselection of microstructures that are considered critical in terms of volume requirements. Individual and ensemble CNN predictions are compared. While CNNs are not found to be accurate enough to replace all FE simulations, CNNs are found to be effective as a rapid screening tool for improving the efficiency of the FE-based RVEMSC determination framework and for expediting future RVEMSC studies.
查看更多>>摘要：Variations of constituent fiber and matrix properties and process conditions can cause significant variability in composite parts and affect their performance. The focus of this paper is to establish a new computational framework that can efficiently quantify the uncertainty propagation of parts manufactured through the resin transfer molding (RTM) process. RTM involves a sequence of inter-related processes that span multiple spatial and temporal scales. This calls for a multi-scale analysis for the nominal process, which is computationally complex and intensive. A direct Monte Carlo simulation of uncertainty quantification leads to prohibitive cost. In this research we leverage a sequentially architected multi-response Gaussian process (MRGP) meta-modelling approach to facilitate a hierarchical procedure. This can dramatically reduce the computational cost, and allow us to characterize the process outputs of interest at different scales and at the same time capture the intrinsic correlation amongst these outputs. Moreover, integrating a global sensitivity analysis with the hierar-chical MRGP meta-models yields the importance ranking of uncertainty propagation paths. This computational framework provides a quantitative assessment tool of the uncertainties in composite manufacturing. Case study on curing-induced dimensional variability of a curved composite part is conducted for demonstration and validation.
查看更多>>摘要：Compositional disorder is common in crystal compounds. In these compounds, some atoms are randomly distributed at some crystallographic sites. For such compounds, randomness forms many non-identical independent structures. Thus, calculating the energy of all structures using ordinary quantum ab initio methods can be significantly time-consuming. Machine learning can be a reliable alternative to ab initio methods. We calculate the energy of these compounds with an accuracy close to that of density functional theory calculations in a considerably shorter time using machine learning. In this study, we use kernel ridge regression and neural network to predict energy. In the KRR, we employ sine matrix, Ewald sum matrix, SOAP, ACSF, and MBTR. To implement the neural network, we use two important classes of application of the neural network in material science, including high-dimensional neural network and convolutional neural network based on crystal graph representation. We show that kernel ridge regression using MBTR and neural network using ACSF can provide better accuracy than other methods.
Soper, AnnaShaw, Adam L.Conway, Patrick L. J.Pomrehn, Gregory S....
5页
查看更多>>摘要：ABSTR A C T The disordered Mg-Sc body-centered cubic (bcc) phase is both lightweight and strong; however, the system is impractical for general industrial use due to the high cost of scandium. We propose a computationally efficient metric that assesses ternary rare earth element additions that may stabilize the bcc phase at lower Sc concentrations. We find that the bcc phase is stabilized by the ternary addition of Y or Er, but not by La, Ce, or Nd, and we validate these predictions by experimental production and characterization of Mg-Sc-(Y,Er,Nd) alloys. The results suggest a computationally efficient method to anticipate integration of ternary elements into binary systems using cluster expansions of constituent binaries.
NSTL
Elsevier