scholarly journals Combining Observational and Computational Uncertainty in Calibration Experiments

2019 ◽  
Author(s):  
Robert Christian Foster ◽  
James R. Gattiker ◽  
Brian Phillip Weaver
Keyword(s):  
Computational Uncertainty

scholarly journals Influence of parallel computational uncertainty on simulations of the Coupled General Climate Model

2012 ◽  
Vol 5 (2) ◽  
pp. 313-319 ◽  
Author(s):  
Z. Song ◽  
F. Qiao ◽  
X. Lei ◽  
C. Wang
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Quality Standard ◽  
Ensemble Member ◽  
Climate System ◽  
Climate System Model ◽  
Climate Simulations ◽  
Computational Uncertainty ◽  
The Impact ◽  
General Climate

Abstract. This paper investigates the impact of the parallel computational uncertainty due to the round-off error on climate simulations using the Community Climate System Model Version 3 (CCSM3). A series of sensitivity experiments have been conducted and the analyses are focused on the Global and Nino3.4 average sea surface temperatures (SST). For the monthly time series, it is shown that the amplitude of the deviation induced by the parallel computational uncertainty is the same order as that of the climate system change. However, the ensemble mean method can reduce the influence and the ensemble member number of 15 is enough to ignore the uncertainty. For climatology, the influence can be ignored when the climatological mean is calculated by using more than 30-yr simulations. It is also found that the parallel computational uncertainty has no distinguishable effect on power spectrum analysis of climate variability such as ENSO. Finally, it is suggested that the influence of the parallel computational uncertainty on Coupled General Climate Models (CGCMs) can be a quality standard or a metric for developing CGCMs.

Review of Uncertainty Quantification of Measurement and Computational Modeling in EMC Part II: Computational Uncertainty

2019 ◽  
Vol 61 (6) ◽  
pp. 1699-1706 ◽  
Author(s):  
Sebastien Lallechere ◽  
Carlo F. M. Carobbi ◽  
Luk R. Arnaut
Keyword(s):  
Computational Modeling ◽  
Uncertainty Quantification ◽  
Computational Uncertainty

scholarly journals Reproducibility of Experiments With Swirling Flow: Numerical Prediction With Polynomial Chaos

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Alouette van Hove ◽  
Lasse N. Skov ◽  
Denis F. Hinz
Keyword(s):  
Fluid Dynamics ◽  
Polynomial Chaos ◽  
Swirling Flow ◽  
Flow Patterns ◽  
Velocity Fields ◽  
Good Reproducibility ◽  
Computational Fluid Dynamics Cfd ◽  
Computational Uncertainty ◽  
Flow Experiments ◽  
Inflow Conditions

Achieving good reproducibility in fluid flow experiments can be challenging, in particular in scenarios where the experimental boundary conditions are obscure. We use computational uncertainty quantification (UQ) to evaluate the influence of uncertain inflow conditions on the reproducibility of experiments with swirling flow. Using a nonintrusive polynomial chaos method in combination with a computational fluid dynamics (CFD) code, we obtain the expectation and variance of the velocity fields downstream from symmetric and asymmetric swirl disturbance generators. Our results suggest that the flow patterns downstream from the asymmetric swirl disturbance generator are more reproducible than the flow patterns downstream from the symmetric swirl disturbance generator. This confirms that the inherent breaking of symmetry eliminates instability mechanisms in the wake of the disturber, thereby creating more stable swirling patterns that make the experiments more reproducible.

Computational uncertainty analysis in multiresolution materials via stochastic constitutive theory

2011 ◽  
Vol 200 (1-4) ◽  
pp. 309-325 ◽  
Author(s):  
M. Steven Greene ◽  
Yu Liu ◽  
Wei Chen ◽  
Wing Kam Liu
Keyword(s):  
Uncertainty Analysis ◽  
Constitutive Theory ◽  
Computational Uncertainty

A note on computational uncertainty

1998 ◽  
Vol 94 (2-3) ◽  
pp. 285-291
Author(s):  
M.H. Hamdan
Keyword(s):  
Computational Uncertainty
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