Deformation band and texture of a cast Mg-RE alloy under uniaxial hot compression

Microstructural evolution and texture of a cast Mg-9Gd-4Y-0.6Zr ingot under hot compression were studied in this paper. Post-deforming microstructures were characterized by optical microscopy, scanning electron microscopy and transmission electron microscopy, while crystallographic orientation information was obtained from X-Ray macro-texture measurement and EBSD micro-texture analysis. Dynamic recrystallization (DRX) initiated from the deformation bands (DB) forming on original grain boundaries; the DB became widen with continuously conversion of low-angle-boundary grains into high-angle-boundary grains. The tendency of strain localization increased with Z parameter. The macro-texture analysis indicates that uniaxial compression yielded out the randomized basal texture component. This texture component was found to be strengthened with increasing Z parameter. The micro-texture analysis shows that the deviation from the ideal basal texture arose from orientated growth within DBs. Moreover, the localization deformation promoted dynamic precipitation within DBs, which inhibited the development of DRX.     

Determination of volume fraction of bainite in low carbon steels using artificial neural networks

Artificial neural networks have been used to estimate the volume fraction of bainite in low carbon steels containing various alloying elements. The network predicts the volume fraction for a given composition, isothermal transformation temperature and isothermal transformation time. Additionally, the maximum transformation temperature at which bainite formation takes place is also provided as an input to the neural network. The network was trained using the experimental data from three low carbon steels and it was found to perform quite well in predicting the volume fraction of bainite. The impact of the composition of alloying elements on the volume fraction of bainite was also studied and the results were in agreement with the known metallurgical theory.     

Computational uncertainty analysis in multiresolution materials via stochastic constitutive theory

A stochastic constitutive theory is proposed in this work to propagate microstructure uncertainties in computational multiscale continuum models to bulk multiresolution material behavior. Ubiquitous fine resolution uncertainty sources influencing prediction of material properties based on their structures are categorized in detail, and this research transmits these uncertainties to coarser material resolutions by introducing a stochastic constitutive theory deduced from volume element simulations. To implement the stochastic upscaling process, two advanced uncertainty quantification methods are examined: statistical copula functions and random process polynomial chaos expansion. Both methods confront the mathematical difficulty in randomizing constitutive laws by capturing the marked correlation among constitutive parameters seen in complex materials, thus the results proffer a more accurate probabilistic estimation of constitutive material behavior. The contribution of this work is twofold: uncertainty is propagated from heterogeneous material “structure” to material “property” via the stochastic constitutive theory, and rigorous, data-driven mathematics are formalized to represent complicated dependence structures in multivariate statistical distributions. To the authors’ knowledge, this is the first work in multiresolution mechanics that presents an approach to computationally derive correlation functions from numerical experiments, as opposed, for instance, to assuming one a priori. The method put forth in this research, though quite general, is applied to a mathematical example and plastic, high strength steel alloy for demonstration. Results include stochastic constitutive curve confidence intervals for the material stress–strain response and qualitative comparisons of the two stochastic methods detailed herein.     

AES for multiscale localization modeling in granular media

This work presents a multiscale strong discontinuity approach to tackle key challenges in modeling localization behavior in granular media: accommodation of discontinuities in the kinematic fields, and direct linkage to the underlying grain-scale information. Assumed enhanced strain (AES) concepts are borrowed to enhance elements for post-localization analysis, but are reformulated within a recently-proposed hierarchical multiscale computational framework. Unlike classical AES methods, where material properties are usually constants or assumed to evolve with some arbitrary phenomenological laws, this framework provides a bridge to extract evolutions of key material parameters, such as friction and dilatancy, based on grain scale computational or experimental data. More importantly, the phenomenological softening modulus typically used in AES methods is no longer required. Numerical examples of plane strain compression tests are presented to illustrate the applicability of this method and to analyze its numerical performance.     

An approximation method of origin–destination flow traffic from link load counts

Traffic matrix (TM) is a key input of traffic engineering and network management. However, it is significantly difficult to attain TM directly, and so TM estimation is so far an interesting topic. Though many methods of TM estimation are proposed, TM is generally unavailable in the large-scale IP backbone networks and is difficult to be estimated accurately. This paper proposes a novel method of TM estimation in large-scale IP backbone networks, which is based on the generalized regression neural network (GRNN), called GRNN TM estimation (GRNNTME) method. Firstly, building on top of GRNN, we present a multi-input and multi-output model of large-scale TM estimation. Because of the powerful capability of learning and generalizing of GRNN, the output of our model can sufficiently capture the spatio-temporal correlations of TM. This ensures that the estimation of TM can accurately be attained. And then GRNNTME uses the procedure of data posttreating further to make the output of our model closer to real value. Finally, we use the real data from the Abilene Network to validate GRNNTME. Simulation results show that GRNNTME can perform well the accurate and fast estimation of TM, track its dynamics, and holds the stronger robustness and lower estimation errors.     

Modeling stochastic live load for long-span bridge based on microscopic traffic flow simulation

A general framework of modeling the stochastic live load from traffic for a long-span bridge is developed. The cellular automaton (CA) traffic flow simulation technique is adopted to develop the analytical basis of the framework. Based on the traffic flow simulation results, the live load on a long-span bridge from the stochastic traffic is studied with a focus on the static component. Parametric studies of major variables of the framework, such as the length of the connecting roadways, the speed limit, and the vehicle combination, are conducted.     

Applicability of stress–force–fabric relationship for non-proportional loading

Starting from the micro-structural definition of the stress tensor, Rothenburg and Bathurst (1989) [1] derived a stress–force–fabric relationship for granular materials by approximating the directional distributions of the fabric, more specifically, the contact normal density distribution in this paper and the contact forces with Fourier functions and integrating over directions. This paper aims to assess the validity of the two key assumptions made during their derivation using particle-based numerical simulation in the cases of proportional loading and non-proportional loading. These two assumptions are (i) the 2nd-rank Fourier functions adopted are good enough to approximate the directional distributions of contact normal densities and contact forces and (ii) the principal directions of contact forces and contact normal density are coaxial. Numerical simulations have been carried out to conduct virtual experiments on the behaviour of isotropic specimens to monotonic loading, of isotropic specimens to stress rotation, and of anisotropic specimens to monotonic loading. The first one stands for the case of proportional loading while the latter two are non-proportional loading paths involving rotation of the frame of principal stresses and the frame of fabric, respectively. The directional distributions of contact normal density and contact forces are traced during these three typical loading processes. The simulation results indicate that the 2nd-rank Fourier functions give reasonable approximations, while the coaxial assumption is generally not valid in non-proportional loading. In the case that the principal directions of contact normal density and contact force differ, a more general expression of the stress–force–fabric relationship is required. This research can help to improve our understanding of the stress state and hence shear strength of granular materials based on the particle scale investigation.     

Multiscale simulation of the dislocation emissions of single Ni crystal in nanoindentation

The simulation of nanoindentation into single nickel crystal is performed by using quasi continuum method. The strain energy-displacement and load-displacement curves are presented to study the mechanical behavior of the dislocation nucleation. The characteristics of the stacking fault and dislocation nucleation are determined by calculating the centro-symmetry parameters and analyzing the displacement field of the atoms beneath the indenter. The structure of the stacking fault and the characteristics of dislocation obtained in the simulation by quasicontinuum method are reproduced in the simulation by molecular dynamics.