Convergence of shape optimization calculations of mechanical components using adaptive biological growth and iterative finite element methods
来源期刊：中南大学学报(英文版)2013年第1期
论文作者：Mohammad Zehsaz Kaveh E. Torkanpouri Amin Paykani
文章页码：76 - 82
Key words：shape optimization; adaptive biological growth; control points; step factor; optimization rate
Abstract: Shape optimization of mechanical components is one of the issues that have been considered in recent years. Different methods were presented such as adaptive biological for reducing costs and increasing accuracy. The effects of step factor, the number of control points and the definition way of control points coordinates in convergence rate were studied. A code was written using ANSYS Parametric Design Language (APDL) which receives the studied parameters as input and obtains the optimum shape for the components. The results show that for achieving successful optimization, step factor should be in a specific range. It is found that the use of any coordinate system in defining control points coordinates and selection of any direction for stimulus vector of algorithm will also result in optimum shape. Furthermore, by increasing the number of control points, some non-uniformities are created in the studied boundary. Achieving acceptable accuracy seems impossible due to the creation of saw form at the studied boundary which is called “saw position”.
Mohammad Zehsaz, Kaveh E. Torkanpouri, Amin Paykani
(Department of Mechanical Engineering, University of Tabriz, Tabriz, P.O. Box 51666-16471, Iran)
Abstract:Shape optimization of mechanical components is one of the issues that have been considered in recent years. Different methods were presented such as adaptive biological for reducing costs and increasing accuracy. The effects of step factor, the number of control points and the definition way of control points coordinates in convergence rate were studied. A code was written using ANSYS Parametric Design Language (APDL) which receives the studied parameters as input and obtains the optimum shape for the components. The results show that for achieving successful optimization, step factor should be in a specific range. It is found that the use of any coordinate system in defining control points coordinates and selection of any direction for stimulus vector of algorithm will also result in optimum shape. Furthermore, by increasing the number of control points, some non-uniformities are created in the studied boundary. Achieving acceptable accuracy seems impossible due to the creation of saw form at the studied boundary which is called “saw position”.
Key words:shape optimization; adaptive biological growth; control points; step factor; optimization rate
