scholarly journals A Black-Box method for parametric model order reduction based on matrix interpolation with application to simulation and control

2016 ◽  
Vol 64 (9) ◽  
Author(s):  
Matthias Geuß
Keyword(s):  
Model Order Reduction ◽  
Order Reduction ◽  
Black Box ◽  
Design Parameters ◽  
Dimensional Parameter ◽  
Model Order ◽  
Parametric Model Order Reduction ◽  
Box Method ◽  
Matrix Interpolation ◽  
And Control

AbstractThis thesis deals with model order reduction of parameter-dependent systems based on interpolation of locally reduced system matrices. A Black-Box method is proposed that automatically determines the optimal design parameters and delivers a reduced system with desired accuracy. In addition, the method is extended to stability preservation and interpolation for high-dimensional parameter spaces.

scholarly journals A Black-Box Method for Parametric Model Order Reduction

2015 ◽  
Vol 48 (1) ◽  
pp. 168-169 ◽  
Author(s):  
M. Geuss ◽  
B. Lohmann ◽  
B. Peherstorfer ◽  
K. Willcox
Keyword(s):  
Model Order Reduction ◽  
Order Reduction ◽  
Parametric Model ◽  
Black Box ◽  
Model Order ◽  
Parametric Model Order Reduction ◽  
Box Method

scholarly journals Model Order Reduction via a Balanced Truncation-Interpolation Approach for Gas Networks

2016 ◽  
Author(s):  
Yi Ming Lu
Keyword(s):  
Model Order Reduction ◽  
Order Reduction ◽  
Computational Effort ◽  
Balanced Truncation ◽  
Model Order ◽  
Gas Networks ◽  
Optimization And Control ◽  
Interpolation Strategy ◽  
Matrix Interpolation ◽  
And Control

Simulation of large instationary gas networks governed by temperature-(T-)parametric nonlinear PDAEs is computationally expensive and extremely time-demanding. To reduce the computational effort and enable optimization and control, we present a parametric surrogate modeling technique composed of linearization, model order reduction (MOR) via balanced truncation (BT) and matrix interpolation strategy based on the energy modal assurance tracking (EMAT-MIS).

scholarly journals An adaptive sampling procedure for parametric model order reduction by matrix interpolation

2019 ◽  
Vol 39 (4) ◽  
pp. 821-834
Author(s):  
Ying Liu ◽  
Hongguang Li ◽  
Huanyu Du ◽  
Ningke Tong ◽  
Guang Meng
Keyword(s):  
Adaptive Sampling ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Parametric Model ◽  
Sampling Procedure ◽  
Model Order ◽  
Reduced Order ◽  
Parametric Model Order Reduction ◽  
Error Indicator ◽  
Matrix Interpolation

An adaptive sampling approach for parametric model order reduction by matrix interpolation is developed. This approach is based on an efficient exploration of the candidate parameter sets and identification of the points with maximum errors. An error indicator is defined and used for fast evaluation of the parameter points in the configuration space. Furthermore, the exact error of the model with maximum error indicator is calculated to determine whether the adaptive sampling procedure reaches a desired error tolerance. To improve the accuracy, the orthogonal eigenvectors are utilized as the reduced-order basis. The proposed adaptive sampling procedure is then illustrated by application in the moving coil of electrical-dynamic shaker. It is shown that the new method can sample the parameter space adaptively and efficiently with the assurance of the resulting reduced-order models’ accuracy.

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