Analysis of clustered tensegrity structures using a modified dynamic relaxation algorithm
โ Scribed by Nizar Bel Hadj Ali; Landolf Rhode-Barbarigos; Ian F.C. Smith
- Publisher
- Elsevier Science
- Year
- 2011
- Tongue
- English
- Weight
- 797 KB
- Volume
- 48
- Category
- Article
- ISSN
- 0020-7683
No coin nor oath required. For personal study only.
โฆ Synopsis
Tensegrities are spatial, reticulated and lightweight structures that are increasingly investigated as structural solutions for active and deployable structures. Tensegrity systems are composed only of axially loaded elements and this provides opportunities for actuation and deployment through changing element lengths. In cable-based actuation strategies, the deficiency of having to control too many cable elements can be overcome by connecting several cables. However, clustering active cables significantly changes the mechanics of classical tensegrity structures. Challenges emerge for structural analysis, control and actuation. In this paper, a modified dynamic relaxation (DR) algorithm is presented for static analysis and form-finding. The method is extended to accommodate clustered tensegrity structures. The applicability of the modified DR to this type of structure is demonstrated. Furthermore, the performance of the proposed method is compared with that of a transient stiffness method. Results obtained from two numerical examples show that the values predicted by the DR method are in a good agreement with those generated by the transient stiffness method. Finally it is shown that the DR method scales up to larger structures more efficiently.
๐ SIMILAR VOLUMES
## Abstract A modified genetic algorithm with realโnumber coding, nonโuniform mutation and arithmetical crossover operators was described in this paper. A local minimization was used to improve the final solution obtained by the genetic algorithm. Using the expโ6โ1 interatomic energy function, the
## Abstract The estimation of the parameters (โfictitious densitiesโ) which control the convergence and numerical stability of a nonโlinear Dynamic Relaxation solution is described. The optimal values of these parameters vary during the iterative solution and they are predicted from the Gerschgรถrin
In discussing self-organizing neural networks, to some extent a large-scale network is assumed in order to achieve generality and adaptability. This paper discusses an optimal structurization method for a nonlinear network, based on a self-organizing algorithm with a two-layer structure. The basic s