Open AccessTopology Optimization Under Uncertainty by Using the New Collocation Method
Author(s) -
Seyyed Ali Latifi Rostami,
Ali Ghoddosian
Publication year - 2019
Publication title -
periodica polytechnica. civil engineering/periodica polytechnica. civil engineering (online)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.406
H-Index - 19
eISSN - 1587-3773
pISSN - 0553-6626
DOI - 10.3311/ppci.13068
Subject(s) - sparse grid , collocation (remote sensing) , mathematical optimization , topology optimization , benchmark (surveying) , grid , collocation method , computer science , monte carlo method , robust optimization , algorithm , sampling (signal processing) , transformation (genetics) , uncertainty quantification , stochastic optimization , topology (electrical circuits) , mathematics , engineering , finite element method , geometry , machine learning , filter (signal processing) , mathematical analysis , chemistry , structural engineering , ordinary differential equation , biochemistry , geodesy , computer vision , differential equation , statistics , combinatorics , gene , geography
In this paper, a robust topology optimization method presents that insensitive to the uncertainty in geometry. Geometric uncertainty can be introduced in the manufacturing variability. This uncertainty can be modeled as a random field. A memory-less transformation of random fields used to random variation modeling. The Adaptive Sparse Grid Collocation (ASGC) method combined with the geometry uncertainty models provides robust designs by utilizing already developed deterministic solvers. The proposed algorithm provides a computationally cheap alternative to previously introduced stochastic optimization methods based on Monte Carlo sampling by using the adaptive sparse grid method. The method is demonstrated in the design of a minimum compliance Messerschmitt-Bölkow-Blohm (MBB) and cantilever beam as benchmark problems.