نشریه روش های عددی در مهندسی
سال سی و هفتم شماره 2 (زمستان 1397)

The stored deformation energy in the dislocation structures in a polycrystalline metal can provide a sufficient driving force to move grain boundaries during annealing. In this paper, a thermodynamically-consistent three-dimensional, finite-strain and dislocation density-based crystal viscoplasticity constitutive theory has been developed to describe the distribution of stored energy and dislocation density in a polycrystalline metal. The developed constitutive equations have been numerically implemented into the Abaqus finite element package via writing a user material subroutine. The simulations have been performed using both the simple Taylor model and the full micromechanical finite element model. The theory and its numerical implementation are then verified using the available data in literature regarding the physical experiments of the single crystal aluminum. As an application of the developed constitutive model, the relationship between the stored energy and the strain induced grain boundary migration in aluminum polycrystals has been investigated by the Taylor model and also, the full finite element model. The obtained numerical results indicated that the Taylor model could not precisely simulate the distribution of the stored deformation energy within the polycrystalline microstructure; consequently, the strain induced grain boundary migration. This is due to the fact that the strain induced grain boundary migration in a plastically deformed polycrystalline microstructure is principally dependent on the spatial distribution of the stored deformation energy rather than the overall stored energy value.

In this paper A progressive damage model based on multi-scale modeling has been developed to predict the initiation and propagation of damage in plain weave fabrics. For this purpose, microscopic damage in yarns and resin is calculated by an RVE (Representative Volume Element) FE simulation. By applying suitable boundary conditions of RVE, macro-scale average stresses were derived to extract the components of the equivalent stiffness matrix. Finally, by developing UMAT and USDFLD subroutines in the ABAQUS commercial software, the strength of the woven composite rings is predicted numerically. In order to confirm the numerical predictions, composite rings using the woven glass tapes of 5 cm width and epoxy resin are fabricated according to ASTM D2290 and tested. A good correlation between experimental and numerical results could confirm the accuracy of the finite element simulation.

The computational burdens and more accurate approximations for the estimation of the failure probability are the main concerns in the structural reliability analyses. The Monte Carlo simulation (MCS) method can simply provide an accurate estimation for the failure probability, but it is a time-consuming method for complex reliability engineering problems with a low failure probability and may efficiently approximate the failure probability. In this paper, the efficiency of MCS for the computations of the performance function is improved using a random-weighted method known as the random-weighted MCS (RWMC) method. By using the weighted exponential function, the weights of random data points generated by MCS are adjusted by selecting the random point in the design space. The convergence performances including the computational burdens for evaluating the limit sate function and the accuracy of failure probabilities of RWMC are compared with MCS by using several nonlinear and complex mathematical and structural problems with normal and no-normal random variables. The results indicate that the proposed RWMC method can estimate the accurate results with the less computational burdens, about 100 to 1000 times faster than MCS

In the recent decades, raw materials and resources have been remarkable issues for researchers; in other words, they play an important role in manufacturing industries or service organizations. On the other hand, the population is increasing every day. An increase in the population means the increased demand for goods or services. Therefore, more resources are needed to deliver services or goods. For this reason, government agencies and environmental agencies have developed and enforced stringent laws against producers and service providers who have exceeded the permissible limits for the environment; in some cases, the use of resources has been even restricted. In the meantime, the supply chain has become one of the major issues that can greatly influence this issue. In this research, the supply chain of the closed loop has been modeled due to uncertainty, disturbances and cost of production. The purpose of this problem has been to minimize the cost of the system in question based on the location decisions, and flow rates between levels and sales. The Lagrangian liberation solution method is used to solve this NP-hard problem. In the end, a numerical example has been employed to test the model and the proposed solution method. The results show that the time of implementation of the large-scale problem with GAMS is higher than that of the proposed method.

In this paper, a method is proposed to improve the results of the standard finite element method. L2 norm is used to determine the nodal error. In the next step, the appropriate order of the interpolation cover is seclected to be proportional to the nodal error and the results are corrected. The error computation procedure and the use of covering enrichment functions will continue until the error reaches the specified value. Cover enrichment interpolation functions will consider the effects of the adjacent elements of each node, in addition to the values obtained from the standard interpolation for each element. Computation rules are programmed in the matlab program and considered for the same examples. Comparison of the results of the proposed method with the exact solutions and the results of the methods proposed by the other researchers in the field of linear elasticity indicates the efficiency and accuracy of the proposed method.

Financial crises in banking systems are due to inability to manage credit risks. Credit scoring is one of the risk management techniques that analyze the borrower's risk. In this paper, using the advantages of computational intelligence as well as soft computing methods, a new hybrid approach is proposed in order to improve credit risk management. In the proposed method, for modeling in uncertainty conditions, parameters of the neural network, including weights and errors, are considered in the form of fuzzy numbers. In this method, the underlying system is firstly modeled using neural networks and then, using fuzzy inferences, the optimal decision will be determined with the highest degree of superiority. Empirical results of using the proposed method indicate the efficiency and high accuracy of this method in analyzing credit rating problems.

Diagonal Strap bracing is one of the most applicable lateral bracing systems in light steel framing (LSF). In practice, one or more panels of Gypsum Wall Boards (GWBs) is used for the cladding of strap braced frames. Usually, the effect of these GWBs in modelling and design is neglected by designers, but this effect can affect the seismic performance of the system In this paper, firstly, a simple numerical method is developed to model the monotonic and cyclic behavior of cold-formed strap braced shear walls together with GWBs. Then, the effects of GWB on the lateral characteristics and seismic performance levels of shear walls are evaluated. It is found that neglecting GWB in the lateral design or modeling of LSF is not rational and GWB can increase the dissipation of earthquake energy, lateral strength and stiffness of the walls. Also, the shear wall composed of strap bracing and SWBs reaches a certain performance level in a less drift ratio in comparison to to only strap braced system