Ranking Based Model-Form Uncertainty Quantification for a Multi-fidelity Design Approach

2006 ◽  
Author(s):  
Josyula Umakant ◽  
Krishnarao Sudhakar ◽  
Prasanna Mujumdar ◽  
Chillarige Raghavendra Rao
Keyword(s):  
Uncertainty Quantification ◽  
Design Approach ◽  
Model Form

scholarly journals Effect of model-form definition on uncertainty quantification in coupled models of mid-frequency range simulations

2017 ◽  
Vol 93 ◽  
pp. 351-367 ◽  
Author(s):  
Kendra L. Van Buren ◽  
Morvan Ouisse ◽  
Scott Cogan ◽  
Emeline Sadoulet-Reboul ◽  
Laurent Maxit
Keyword(s):  
Uncertainty Quantification ◽  
Frequency Range ◽  
Coupled Models ◽  
Model Form

scholarly journals Model-form and predictive uncertainty quantification in linear aeroelasticity

2017 ◽  
Vol 73 ◽  
pp. 137-161 ◽  
Author(s):  
C.T. Nitschke ◽  
P. Cinnella ◽  
D. Lucor ◽  
J.-C. Chassaing
Keyword(s):  
Uncertainty Quantification ◽  
Predictive Uncertainty ◽  
Model Form

Model-Form and Parameter Uncertainty Quantification in Structural Vibration Simulation Using Fractional Derivatives

2019 ◽  
Vol 14 (5) ◽  
Author(s):  
Baoqiang Zhang ◽  
Qintao Guo ◽  
Yan Wang ◽  
Ming Zhan
Keyword(s):  
Uncertainty Quantification ◽  
Parameter Uncertainty ◽  
Process Model ◽  
Degrees Of Freedom ◽  
Fractional Derivatives ◽  
Calibration Method ◽  
Structural Vibration ◽  
Model Form ◽  
Lack Of Information ◽  
Vibration Simulation

Extensive research has been devoted to engineering analysis in the presence of only parameter uncertainty. However, in modeling process, model-form uncertainty arises inevitably due to the lack of information and knowledge, as well as assumptions and simplifications made in the models. It is undoubted that model-form uncertainty cannot be ignored. To better quantify model-form uncertainty in vibration systems with multiple degrees-of-freedom, in this paper, fractional derivatives as model-form hyperparameters are introduced. A new general model calibration approach is proposed to separate and reduce model-form and parameter uncertainty based on multiple fractional frequency response functions (FFRFs). The new calibration method is verified through a simulated system with two degrees-of-freedom. The studies demonstrate that the new model-form and parameter uncertainty quantification method is robust.

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