Modeling and computing parameters in nonlinear finite element simulations significantly affect simulation accuracy and efficiency even when it is carried out using commercial software, such as ABAQUS, ANSYS, etc. Yet ...
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Modeling and computing parameters in nonlinear finite element simulations significantly affect simulation accuracy and efficiency even when it is carried out using commercial software, such as ABAQUS, ANSYS, etc. Yet comprehensive effects of these parameters on simulation results have seldom been reported. In this article, we explore the effects of several important parameters, such as mass scaling type and value, element type and size, and loading velocity, on the accuracy and efficiency of nonlinear finite element simulation of metallic foams based on three-dimensional Voronoi mesostructures. Analysis indicated that these parameters did affect simulation accuracy and efficiency, and three optimized nondimensional parameters were recommended. Based on the verified model and optimized parameters, effects of cell-wall thickness distribution on the uniaxial properties of metallic foams were also investigated. Simulation results showed that the different distribution of cell-wall thickness in modeling may induce varied elastic moduli and yield stress of metal foams. Our analysis showed that modeling and computing parameters must be paid attention to in the nonlinear FE simulation, and that the recommended parameters constitute a good reference for numerical simulation of metallic foams in predicting mechanical behaviors.
Computation of different parameters using a generalized hardware/software approach leads to save time and resources. Keeping this concept in mind authors are proposed a generalized neural simulator for computing two p...
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ISBN:
(纸本)9781467331166
Computation of different parameters using a generalized hardware/software approach leads to save time and resources. Keeping this concept in mind authors are proposed a generalized neural simulator for computing two parameters each of circular patch (i.e. resonance frequency and radius) and triangular patch (i.e. resonance frequency and side-length) microstrip antennas simultaneously. For the purpose nine different training algorithms are used and Levenberg-Marquardt (LM) backpropagation is proved to be the fastest converging training algorithm and producing the results with least error. The results thus obtained by this simulator are in conventionality and very good in agreement with their measured counterparts.
We consider the problem of computing parameters of player cost functions in discrete-time nonzero-sum noncooperative dynamic games from open-loop Nash equilibria. Although similar inverse problems have been investigat...
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