Uncertainty Quantification and Integration in Multi-level Problems

2014 ◽  
pp. 89-98 ◽  
Author(s):  
Chenzhao Li ◽  
Sankaran Mahadevan
Keyword(s):  
Uncertainty Quantification ◽  
Multi Level

Test Resource Allocation for Uncertainty Quantification of Multi-level and Coupled Systems

2011 ◽  
Author(s):  
Shankar Sankararaman ◽  
Sankaran Mahadevan ◽  
Kyle McLemore ◽  
Samuel Bradford ◽  
Chen Liang ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Uncertainty Quantification ◽  
Coupled Systems ◽  
Multi Level

scholarly journals Uncertainty quantification in tsunami modeling using multi-level Monte Carlo finite volume method

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Carlos Sánchez-Linares ◽  
Marc de la Asunción ◽  
Manuel J Castro ◽  
José M González-Vida ◽  
Jorge Macías ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Uncertainty Quantification ◽  
Finite Volume Method ◽  
Finite Volume ◽  
Tsunami Modeling ◽  
Multi Level ◽  
Volume Method

Multi-level parallelism for structural acoustic uncertainty quantification

2021 ◽  
Vol 150 (4) ◽  
pp. A169-A169
Author(s):  
Andrew S. Wixom ◽  
Kyle Myers ◽  
Micah Shepherd
Keyword(s):  
Uncertainty Quantification ◽  
Multi Level ◽  
Level Parallelism

Uncertainty Quantification and Validation of Lattice Structures Fabricated by Selective Laser Melting

2017 ◽  
Author(s):  
Recep M. Gorguluarslan ◽  
Seung-Kyum Choi ◽  
Hae-jin Choi
Keyword(s):  
Additive Manufacturing ◽  
Uncertainty Quantification ◽  
Selective Laser Melting ◽  
Lattice Structure ◽  
Computational Cost ◽  
Elastic Behavior ◽  
Lattice Structures ◽  
Laser Melting ◽  
Structure Level ◽  
Multi Level

A methodology is proposed for uncertainty quantification to accurately predict the mechanical response of lattice structures fabricated by additive manufacturing. Effective structural properties of the lattice structures are characterized using a multi-level stochastic upscaling process that propagates the quantified uncertainties at strut level to the lattice structure level. To obtain realistic simulation models for the stochastic upscaling process, high resolution finite element models of individual struts were reconstructed from the micro-CT scan images of lattice structures which are fabricated by selective laser melting. The upscaling process facilitates obtaining of the homogenized strut properties of the lattice structure to reduce the computational cost of the detailed simulation model for the lattice structure. Bayesian Information Criterion is utilized to quantify the uncertainties with parametric distributions based on the statistical data obtained from the reconstructed strut models. A systematic validation approach that can minimize the experimental cost is also utilized to assess the predictive capability of the stochastic upscaling method used at strut level and lattice structure level. In comparison with physical compression tests, the proposed methodology of linking the uncertainty quantification with multi-level stochastic upscaling method enabled an accurate prediction of the elastic behavior of the lattice structure by accounting for the uncertainties introduced by the additive manufacturing process.

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