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This paper presents an analysis on the elastic characteristics of luffa and palm natural fiber composites (NFC) with epoxy and ecopoxy matrixes, taking into account the impact of fiber volume fractions. Furthermore, longitudinal modulus, transverse modulus, shear modulus, and Poisson's ratio were predicted using representative volume elements (RVEs) with chopped random and unidirectional fiber arrangements. However, analytical approaches such as rule of mixture, Chamis, Halpin–Tsai, and Nielsen were considered for validating and comparing the findings of finite element analyses. Hence, it was found that increasing fiber volume fraction increased the elastic properties of palm/epoxy, palm/ecopoxy, and luffa/epoxy NFCs, but decreased that of luffa/ecopoxy NFC. Addition of palm fibers in ecopoxy and epoxy had stronger effect than luffa on enhancing the elastic properties of the final structure. However, greatest elastic characteristics observed through analytical and numerical models were obtained for ecopoxy matrix with 0.5 palm fibers. A strong agreement was observed between the results obtained from analytical approaches and RVE unidirectional model. Chamis model exhibited higher outcomes compared to the considered analytical techniques, while Halpin–Tsai model showed the least values. [ABSTRACT FROM AUTHOR]

In this paper, a novel three-dimensional (3D) cellular structure with negative Poisson's ratio was designed by alternating cuboid surface indents on the vertical ribs of the unit cells. The Poisson's ratio and Young's modulus of structures with different geometric parameters were determined using the finite element method (FEM) as a function of these parameters. Samples with identical geometric variables were fabricated via 3D printing, and their through-thickness direction Poisson's ratios were measured and compared with simulation results. Results showed that the Poisson's ratio of the 3D cellular structures can be tuned from positive to negative and can reach a minimal value of −0.958. Good agreement was found between the experimental results and the simulation. This lattice structure is considerably stiffer than re-entrant negative Poisson's ratio foam with the same solid phase. The design concept developed here can be optimized for specific applications via geometric parameters manipulation. [ABSTRACT FROM AUTHOR]

We propose in this paper analytical and numerical models that describe the in-plane uniaxial elastic properties (Young's moduli and Poisson's ratios) of a honeycomb structure with curved walls. We perform a parametric analysis of the mechanical performance of this honeycomb also by taking into account the different types of deformations acting inside the cell walls. The curved wall honeycomb possesses higher magnitudes of the Poisson's ratio ν12 in the auxetic configuration compared to classical center symmetric configuration with straight cell wall. The presence of the curvature also allows creating configurations with positive Poisson's ratio even for negative internal cell angles, and makes this honeycomb design attractive for mechanical tailoring. [ABSTRACT FROM AUTHOR]

Based on the auxiliary space method, a preconditioner is studied in this paper for linear systems of equations arising from higher order finite element (FEM) discretizations of linear elasticity equations. The main idea, which is proposed by Xu ( Computing 1996; 56:215-235) for the scalar PDE, is to construct the preconditioner as a combination of a smoother and a coarse level solver, where the systems of equations arising from lower order FEM discretizations are used in the coarse level solver. It is theoretically shown that the condition number of the preconditioned systems is uniformly bounded with respect to both the problem size and moderate Poisson's ratio. When the Poisson's ratio is near the limit of 0.5, we have presented some numerical tests for the case of fourth-order FEM discretization in a combination with quadratic conforming FEM as a coarse space. The results are almost robust when Poisson's ratio is near the limit of 0.5. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Most of the electromagnetic devices, especially electrical machines, have the disadvantage to be exposed to high vibrations caused by magnetic forces. The aim of this study is to propose a methodology to optimize the cylindrical stators generally used in electrical machines regarding the vibration phenomena. Techniques for vibration reduction require knowledge of the proper frequencies, which depend on mechanical shapes and dimensions as well as material properties such as mass density, Young's modulus and Poisson's ratio. This paper proposes a new approach which is based on the identification of mass density (lamination stacking factor) and Young's modulus in the goal to minimize the vibratory behavior of electrical machines. In this goal, we have used artificial intelligent and finite element method (FEM) analysis to solve the magneto-mechanical inverse problem (IP). In the proposed approach, a Multilayer Perceptron Neural Network (MLPNN) is used as a forward model in order to decrease the FEM time consuming. Thus, a Genetic Algorithm (GA) is used to solve the IP in a reasonable time of running. An example study of an induction machine proves that the developed approach may be applied in both design and identification applications. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]