scholarly journals Automatic selection of basis-adaptive sparse polynomial chaos expansions for engineering applications

2021 ◽  
Author(s):  
Nora Lüthen ◽  
Stefano Marelli ◽  
Bruno Sudret
Keyword(s):  
Polynomial Chaos ◽  
Engineering Applications ◽  
Sparse Polynomial ◽  
Automatic Selection ◽  
Polynomial Chaos Expansions ◽  
Selection Of

Uncertainty Study of Periodic-Grating Wideband Filters With Sparse Polynomial-Chaos Expansions

2019 ◽  
Vol 31 (18) ◽  
pp. 1499-1502
Author(s):  
A. D. Papadopoulos ◽  
T. T. Zygiridis ◽  
E. N. Glytsis ◽  
N. V. Kantartzis ◽  
C. S. Antonopoulos
Keyword(s):  
Polynomial Chaos ◽  
Sparse Polynomial ◽  
Periodic Grating ◽  
Polynomial Chaos Expansions ◽  
Wideband Filters

scholarly journals Stability analysis of a clutch system with uncertain parameters using sparse polynomial chaos expansions

2019 ◽  
Vol 20 (1) ◽  
pp. 104
Author(s):  
Duc Thinh Kieu ◽  
Baptiste Bergeot ◽  
Marie-Laure Gobert ◽  
Sébastien Berger
Keyword(s):  
Stability Analysis ◽  
Polynomial Chaos ◽  
Deterministic Model ◽  
Computational Cost ◽  
Uncertain Parameters ◽  
Sparse Polynomial ◽  
Clutch System ◽  
Frictional Forces ◽  
The Stability ◽  
Polynomial Chaos Expansions

In vehicle transmission systems, frictional forces acting during the sliding phase of the clutch engagement may produce unwanted vibrations. The prediction of the stability of a clutch system remains however a laborious task, as the parameters which have the highest impact on the stability, such as the friction law or the damping, lead to significant dispersions and must be considered as uncertain in such studies. Non-intrusive generalized polynomial chaos (gPC) expansions have already been used in this context. However, the number of deterministic model evaluations (i.e. the computational cost) required to compute the PC coefficients becomes prohibitive for large numbers of uncertain parameters. The sparse polynomial chaos, recently developed by Blatman and Sudret, may overcome this issue. In this paper, the method has been applied to the stability analysis of a clutch system owning up to eight uncertain parameters. Comparisons with the reference Monte Carlo method and classic full PC expansions show that sparse PC expansions allow substantial computational cost reductions while ensuring a high accuracy of the results.

Sign in / Sign up

Export Citation Format

Share Document