Advances in Engineering Software - Special issue on large-scale analysis, design and intelligent synthesis environments
Modeling uncertainty in flow simulations via generalized polynomial chaos
Journal of Computational Physics
High-Order Collocation Methods for Differential Equations with Random Inputs
SIAM Journal on Scientific Computing
Journal of Computational Physics - Special issue: Uncertainty quantification in simulation science
SIAM Journal on Numerical Analysis
SIAM Journal on Numerical Analysis
Uncertainty quantification using polynomial chaos expansion with points of monomial cubature rules
Computers and Structures
Hierarchical parallelisation for the solution of stochastic finite element equations
Computers and Structures
Efficient component-wise and solver-based intrusive SFEM analysis of complex structures
Finite Elements in Analysis and Design
Advances in Engineering Software
General purpose software for efficient uncertainty management of large finite element models
Finite Elements in Analysis and Design
On the application of intervening variables for stochastic finite element analysis
Computers and Structures
Uncertainty quantification for algebraic systems of equations
Computers and Structures
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This paper introduces the application of the Guyan reduction within the stochastic finite element (SFE) analysis, which employs a Galerkin-based Polynomial chaos (P-C) expansion formulation. It is shown that by reducing the size of the deterministic FE model, a substantial improvement in the overall computational efficiency can be achieved. An implementation exploiting the features of the proposed formulation is presented. In this regard, especially the interaction with the 3rd party FE solvers has been addressed. The suggested method has been tested on a simple grid structure and also on a large building model, where the accuracy and efficiency of the introduced approach have been quantified.