Surrogate-Based Optimization of Functionally Graded Plates under Thermo- Mechanical Loading

Resumo

Efficient designs for a Functionally Graded Plates (FGP) can be defined via structural optimization.
Usually, bio-inspired algorithms are employed in order to carry out the optimization process. However, when
analyses are very time-demanding, the process may be too costly, since hundreds or even thousands of analyses
may be required. In this work, Sequential Approximate Optimization is employed to provide a more efficient
approach. This paper focuses on the maximization of buckling temperature of a ceramic-metal FGP. B-Splines
are used to define a continuous material gradation along the thickness direction. Effective material properties are
evaluated by the rule of mixtures. The Particle Swarm Optimization (PSO) is applied for structural optimization
and Isogeometric Analysis (IGA) is employed to evaluate the structural responses. Then, Sequential Approximate
Optimization (SAO) is carried out to reduce the computational cost using Kriging to fit an approximate response
surface. A comparison between conventional optimization and SAO is performed, and results show that SAO
achieved the optimum design much earlier the conventional approach, requiring fewer high-fidelity evaluations.