Abstract: In order to achieve the layout and size optimization of the web members of tower crane boom, a truss topology and cross section size optimization method based on continuum is proposed considering three typical working conditions. Firstly, the optimization model is established by replacing web members with web plates. And the web plates are divided into several sub-domains so that periodic soft kill option (SKO) method can be carried out for topology optimization of the slender boom. After getting the optimized topology of web plates, the optimized layout of web members is formed through extracting the principal stress distribution. Finally, using the web member radius as design variable, the boom compliance as objective and the material volume of the boom as constraint, the cross section size optimization mathematical model is established. The size optimization criterion is deduced from the mathematical model by Lagrange multiplier method and Kuhn-Tucker condition. By comparing the original boom with the optimal boom, it is identified that this optimization method can effectively lighten the boom and improve its performance.
Abstract: Numerical design optimization is a powerful tool that
can be used by engineers during any stage of the design process.
There are many different applications for structural optimization. A
specific application that will be discussed in the following paper is
experimental data matching. Data obtained through tests on a physical
structure will be matched with data from a numerical model of that
same structure. The data of interest will be the dynamic characteristics
of an antenna structure focusing on the mode shapes and modal
frequencies. The structure used was a scaled and simplified model of
the Karoo Array Telescope-7 (KAT-7) antenna structure.
This kind of data matching is a complex and difficult task. This
paper discusses how optimization can assist an engineer during the
process of correlating a finite element model with vibration test data.
Abstract: Truss optimization problem has been vastly studied
during the past 30 years and many different methods have been
proposed for this problem. Even though most of these methods
assume that the design variables are continuously valued, in reality,
the design variables of optimization problems such as cross-sectional
areas are discretely valued. In this paper, an improved hill climbing
and an improved simulated annealing algorithm have been proposed
to solve the truss optimization problem with discrete values for crosssectional
areas. Obtained results have been compared to other
methods in the literature and the comparison represents that the
proposed methods can be used more efficiently than other proposed
methods