BLISS/S: a new method for two-level structural optimization

Citation
J. Sobieszczanski-sobieski et S. Kodiyalam, BLISS/S: a new method for two-level structural optimization, ST MULT OPT, 21(1), 2001, pp. 1-13
Citations number
13
Categorie Soggetti
Mechanical Engineering
Journal title
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
ISSN journal
1615147X → ACNP
Volume
21
Issue
1
Year of publication
2001
Pages
1 - 13
Database
ISI
SICI code
1615-147X(200103)21:1<1:BANMFT>2.0.ZU;2-A
Abstract
The paper describes a two-level method for structural optimization for a mi nimum weight under the local strength and displacement constraints. The met hod divides the optimization task into separate optimizations of the indivi dual substructures (in the extreme, the individual components) coordinated by the assembled structure optimization. The substructure optimizations use local cross-sections as design variables and satisfy the highly nonlinear local constraints of strength and buckling. The design variables in the ass embled structure optimization govern the structure overall shape and handle the displacement constraints. The assembled structure objective function i s the objective in each of the above optimizations. The substructure optimi zations are linked to the assembled structure optimization by the sensitivi ty derivatives. The method was derived from a previously reported two-level optimization method for engineering systems, e.g. aerospace vehicles, that comprise interacting modules to be optimized independently, coordination p rovided by a system-level optimization. This scheme was adapted to structur al optimization by treating each substructure as a module in a system, and using the standard finite element analysis as the system analysis. A numeri cal example, a hub structure framework, is provided to show the new method agreement with a standard, no-decomposition optimization. The new method ad vantage lies primarily in the autonomy of the individual substructure optim ization that enables concurrency of execution to compress the overall task elapsed time. The advantage increases with the magnitude of that task.