.png){kind=icon}
Return
Kriging-based optimization of functionally graded structures
.png){kind=icon}
OA
AI
DOI：10.1007/s00158-021-02949-5.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This work presents an efficient methodology for the optimum design of functionally graded structures using a Kriging-based approach. The method combines an adaptive Kriging framework with a hybrid particle swarm optimization (PSO) algorithm to improve the computational efficiency of the optimization process. In this approach, the surrogate model is used to replace the high-fidelity structural responses obtained by a NURBS-based isogeometric analysis. In addition, the impact of key factors on surrogate modelling, as the correlation function, the infill criterion used to update the surrogate model, and the constraint handling is assessed for accuracy, efficiency, and robustness. The design variables are related to the volume fraction distribution and the thickness. Displacement, fundamental frequency, buckling load, mass, and ceramic volume fraction are used as objective functions or constraints. The effectiveness and accuracy of the proposed algorithm are illustrated through a set of numerical examples. Results show a significant reduction in the computational effort over the conventional approach.
Keywords:
Kriging
Functionally graded materials
Sequential approximate optimization
Isogeometric analysis
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4
Papers:
4.9K
Citations:
1.7W
Organization
Cited Papers
ShareSaveOptimal design of thin-walled functionally graded beams for buckling problems.png)
COMPOSITE STRUCTURES
COMPOSITE STRUCTURESIF7.1
PREAI
ShareSaveA hybrid shared/distributed memory parallel genetic algorithm for optimization of laminate compositesCOMPOSITE STRUCTURESOAAI
COMPOSITE STRUCTURESIF7.1
OAAIShareSaveShareSaveShareSaveEfficient global optimization with ensemble and selection of kernel functions for engineering design
PREAI
ShareSaveComparison study of sampling methods for computer experiments using various performance measures
PREAI
ShareSave
View more