TY - GEN
T1 - A multi-mesh strategy for continuum topology optimization under correlated uncertainties
AU - Guest, James K.
AU - Asadpoure, Alireza
AU - Igusa, Takeru
N1 - Funding Information:
This work was supported by the National Science Foundation under Grant No. Bloebaumersnvsgipm rafoicoerfh.gTsusirpasigptefully arocknowarletdged.
Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - A multi-mesh strategy is employed to reduce the computational costs associated with incorporating correlated uncertainties into structural topology optimization problems. Uncertainty quantification is achieved through an existing perturbation-based method. This method transforms uncertainties in stiffness, such as geometric and material property randomness, into an augmented deterministic topology optimization problem. Although the approach has proven quite efficient in general, the uncertainty sources are tied to the finite element analysis mesh. Computational costs then rise significantly when refining the finite element mesh in the presence of correlated random variables. We therefore propose separating the analysis and uncertainty mesh. The technique is evaluated on problems of expected stiffness with uncertainties in elastic modulus and compared to solutions found using the standard perturbation and Monte Carlo topology optimization algorithms.
AB - A multi-mesh strategy is employed to reduce the computational costs associated with incorporating correlated uncertainties into structural topology optimization problems. Uncertainty quantification is achieved through an existing perturbation-based method. This method transforms uncertainties in stiffness, such as geometric and material property randomness, into an augmented deterministic topology optimization problem. Although the approach has proven quite efficient in general, the uncertainty sources are tied to the finite element analysis mesh. Computational costs then rise significantly when refining the finite element mesh in the presence of correlated random variables. We therefore propose separating the analysis and uncertainty mesh. The technique is evaluated on problems of expected stiffness with uncertainties in elastic modulus and compared to solutions found using the standard perturbation and Monte Carlo topology optimization algorithms.
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U2 - 10.2514/6.2010-9328
DO - 10.2514/6.2010-9328
M3 - Conference contribution
AN - SCOPUS:84880779492
SN - 9781600869549
T3 - 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010
BT - 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference 2010
T2 - 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, MAO 2010
Y2 - 13 September 2010 through 15 September 2010
ER -