Overview of Challenges in Performing Uncertainty Quantification for Fluids Engineering Problems

2022 ◽  
Author(s):  
Andrew W. Cary ◽  
John Schaefer ◽  
Earl P. Duque ◽  
Manas Khurana ◽  
Erin C. DeCarlo
Keyword(s):  
Uncertainty Quantification ◽  
Engineering Problems

scholarly journals An Efficient Polynomial Chaos Expansion Method for Uncertainty Quantification in Dynamic Systems

2021 ◽  
Vol 2 (3) ◽  
pp. 460-481
Author(s):  
Jeongeun Son ◽  
Yuncheng Du
Keyword(s):  
Uncertainty Quantification ◽  
Chemical Engineering ◽  
Polynomial Chaos ◽  
Expansion Method ◽  
Polynomial Chaos Expansion ◽  
Parametric Uncertainties ◽  
Chaos Expansion ◽  
Generalized Dimension ◽  
Model Predictions ◽  
Engineering Problems

Uncertainty is a common feature in first-principles models that are widely used in various engineering problems. Uncertainty quantification (UQ) has become an essential procedure to improve the accuracy and reliability of model predictions. Polynomial chaos expansion (PCE) has been used as an efficient approach for UQ by approximating uncertainty with orthogonal polynomial basis functions of standard distributions (e.g., normal) chosen from the Askey scheme. However, uncertainty in practice may not be represented well by standard distributions. In this case, the convergence rate and accuracy of the PCE-based UQ cannot be guaranteed. Further, when models involve non-polynomial forms, the PCE-based UQ can be computationally impractical in the presence of many parametric uncertainties. To address these issues, the Gram–Schmidt (GS) orthogonalization and generalized dimension reduction method (gDRM) are integrated with the PCE in this work to deal with many parametric uncertainties that follow arbitrary distributions. The performance of the proposed method is demonstrated with three benchmark cases including two chemical engineering problems in terms of UQ accuracy and computational efficiency by comparison with available algorithms (e.g., non-intrusive PCE).

INVERSE UNCERTAINTY QUANTIFICATION OF A CELL MODEL USING A GAUSSIAN PROCESS METAMODEL

2020 ◽  
Vol 10 (4) ◽  
pp. 333-349
Author(s):  
Kevin de Vries ◽  
Anna Nikishova ◽  
Benjamin Czaja ◽  
Gábor Závodszky ◽  
Alfons G. Hoekstra
Keyword(s):  
Gaussian Process ◽  
Uncertainty Quantification ◽  
Cell Model ◽  
A Cell
Sign in / Sign up

Export Citation Format

Share Document