Advances in Computational Design

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Sensitivity analysis based on complex variables in FEM for linear structures

  • Azqandi, Mojtaba Sheikhi (Department of Mechanical Engineering, Bozorgmehr University of Qaenat) ;
  • Hassanzadeh, Mahdi (Department of Mechanical Engineering, Islamic Azad University, Gorgan Branch, Kordkuy Center) ;
  • Arjmand, Mohammad (Department of Civil Engineering, Bozorgmehr University of Qaenat)
  • Received : 2017.09.21
  • Accepted : 2018.09.06
  • Published : 2019.01.25
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Abstract

One of the efficient and useful tools to achieve the optimal design of structures is employing the sensitivity analysis in the finite element model. In the numerical optimization process, often the semi-analytical method is used for estimation of derivatives of the objective function with respect to design variables. Numerical methods for calculation of sensitivities are susceptible to the step size in design parameters perturbation and this is one of the great disadvantages of these methods. This article uses complex variables method to calculate the sensitivity analysis and combine it with discrete sensitivity analysis. Finally, it provides a new method to obtain the sensitivity analysis for linear structures. The use of complex variables method for sensitivity analysis has several advantages compared to other numerical methods. Implementing the finite element to calculate first derivatives of sensitivity using this method has no complexity and only requires the change in finite element meshing in the imaginary axis. This means that the real value of coordinates does not change. Second, this method has the lower dependency on the step size. In this research, the process of sensitivity analysis calculation using a finite element model based on complex variables is explained for linear problems, and some examples that have known analytical solution are solved. Results obtained by using the presented method in comparison with exact solution and also finite difference method indicate the excellent efficiency of the proposed method, and it can predict the sustainable and accurate results with the several different step sizes, despite low dependence on step size.

Keywords

References

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