Active Subspace Variation and Modeling Uncertainty in a Supersonic Flame Simulation

AIAA Journal ◽  
2021 ◽  
pp. 1-10
Author(s):  
Nana Wang ◽  
Tianwei Yang ◽  
Zhuyin Ren
Keyword(s):  
Modeling Uncertainty ◽  
Flame Simulation ◽  
Active Subspace

scholarly journals Active-subspace analysis of exceedance probability for shallow-water waves

2021 ◽  
Vol 126 (1) ◽  
Author(s):  
Kenan Šehić ◽  
Henrik Bredmose ◽  
John D. Sørensen ◽  
Mirza Karamehmedović
Keyword(s):  
Shallow Water ◽  
Water Waves ◽  
Exceedance Probability ◽  
Subspace Analysis ◽  
Shallow Water Waves ◽  
Active Subspace

Injection of LOX spray in Methane cross-flow RANS modeling uncertainty quantification

2021 ◽  
Author(s):  
Pietro Paolo Ciottoli ◽  
Jacopo Liberatori ◽  
Riccardo Malpica Galassi ◽  
Mauro Valorani
Keyword(s):  
Uncertainty Quantification ◽  
Cross Flow ◽  
Modeling Uncertainty ◽  
Rans Modeling

scholarly journals Global Sensitivity Analysis and Adaptive Stochastic Sampling of a Subsurface-Flow Model Using Active Subspaces

2019 ◽  
Author(s):  
Daniel Erdal ◽  
Olaf A. Cirpka
Keyword(s):  
Sensitivity Analysis ◽  
Flow Model ◽  
Global Sensitivity Analysis ◽  
Subsurface Flow ◽  
Full Model ◽  
Stochastic Sampling ◽  
Global Sensitivity ◽  
Parameter Combination ◽  
Active Subspaces ◽  
Active Subspace

Abstract. Integrated hydrological modelling of domains with complex subsurface features requires many highly uncertain parameters. Performing a global uncertainty analysis using an ensemble of model runs can help bring clarity which of these parameters really influence system behavior, and for which high parameter uncertainty does not result in similarly high uncertainty of model predictions. However, already creating a sufficiently large ensemble of model simulation for the global sensitivity analysis can be challenging, as many combinations of model parameters can lead to unrealistic model behavior. In this work we use the method of active subspaces to perform a global sensitivity analysis. While building-up the ensemble, we use the already existing ensemble members to construct low-order meta-models based on the first two active subspace dimensions. The meta-models are used to pre-determine whether a random parameter combination in the stochastic sampling is likely to result in unrealistic behavior, so that such a parameter combination is excluded without running the computationally expensive full model. An important reason for choosing the active subspace method is that both the activity score of the global sensitivity analysis and the meta-models can easily be understood and visualized. We test the approach on a subsurface flow model including uncertain hydraulic parameter, uncertain boundary conditions, and uncertain geological structure. We show that sufficiently detailed active subspaces exist for most observations of interest. The pre-selection by the meta-model significantly reduces the number of full model runs that must be rejected due to unrealistic behavior. An essential but difficult part in active subspace sampling using complex models is approximating the gradient of the simulated observation with respect to all parameters. We show that this can effectively and meaningful be done with second-order polynomials.

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