scholarly journals Backward sensitivity analysis and reduced‐order covariance estimation in noninvasive parameter identification for cerebral arteries

2018 ◽  
Vol 35 (4) ◽  
pp. e3170 ◽  
Author(s):  
Robert Rapadamnaba ◽  
Franck Nicoud ◽  
Bijan Mohammadi
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Identification ◽  
Cerebral Arteries ◽  
Covariance Estimation ◽  
Reduced Order

scholarly journals Local improvements to reduced-order models using sensitivity analysis of the proper orthogonal decomposition

2009 ◽  
Vol 629 ◽  
pp. 41-72 ◽  
Author(s):  
ALEXANDER HAY ◽  
JEFFREY T. BORGGAARD ◽  
DOMINIQUE PELLETIER
Keyword(s):  
Sensitivity Analysis ◽  
Proper Orthogonal Decomposition ◽  
Stokes Equations ◽  
Orthogonal Decomposition ◽  
Navier Stokes ◽  
Navier Stokes Equations ◽  
Reduced Order ◽  
Selection Step ◽  
Low Dimensional ◽  
Proper Orthogonal

The proper orthogonal decomposition (POD) is the prevailing method for basis generation in the model reduction of fluids. A serious limitation of this method, however, is that it is empirical. In other words, this basis accurately represents the flow data used to generate it, but may not be accurate when applied ‘off-design’. Thus, the reduced-order model may lose accuracy for flow parameters (e.g. Reynolds number, initial or boundary conditions and forcing parameters) different from those used to generate the POD basis and generally does. This paper investigates the use of sensitivity analysis in the basis selection step to partially address this limitation. We examine two strategies that use the sensitivity of the POD modes with respect to the problem parameters. Numerical experiments performed on the flow past a square cylinder over a range of Reynolds numbers demonstrate the effectiveness of these strategies. The newly derived bases allow for a more accurate representation of the flows when exploring the parameter space. Expanding the POD basis built at one state with its sensitivity leads to low-dimensional dynamical systems having attractors that approximate fairly well the attractor of the full-order Navier–Stokes equations for large parameter changes.

Dimensionality Reduction of High-Fidelity Machine Tool Models by Using Global Sensitivity Analysis

2021 ◽  
pp. 1-13
Author(s):  
Johannes Ellinger ◽  
Thomas Semm ◽  
Michael F. Zäh
Keyword(s):  
Sensitivity Analysis ◽  
Machine Tool ◽  
Parameter Identification ◽  
Machine Tools ◽  
Global Sensitivity Analysis ◽  
Reference Model ◽  
Evaluation Criteria ◽  
Unknown Parameters ◽  
Global Sensitivity ◽  
Process Simulations

Abstract Models that are able to accurately predict the dynamic behavior of machine tools are crucial for a variety of applications ranging from machine tool design to process simulations. However, with increasing accuracy, the models tend to become increasingly complex, which can cause problems identifying the unknown parameters which the models are based on. In this paper, a method is presented that shows how parameter identification can be eased by systematically reducing the dimensionality of a given dynamic machine tool model. The approach presented is based on ranking the model's input parameters by means of a global sensitivity analysis. It is shown that the number of parameters, which need to be identified, can be drastically reduced with only limited impact on the model's fidelity. This is validated by means of model evaluation criteria and frequency response functions which show a mean conformity of 98.9 % with the full-scale reference model. The paper is concluded by a short demonstration on how to use the results from the global sensitivity analysis for parameter identification.

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