A nonlinear model for top fuel dragster dynamic performance assessment

2012 ◽  
Vol 50 (2) ◽  
pp. 281-297 ◽  
Author(s):  
P. D. Spanos ◽  
D. H. Castillo ◽  
I. A. Kougioumtzoglou ◽  
R. A. Tapia
Keyword(s):  
Performance Assessment ◽  
Nonlinear Model ◽  
Dynamic Performance ◽  
Top Fuel Dragster

Empirical Performance Assessment of Nonlinear Model Selection Techniques

2002 ◽  
pp. 450-459
Author(s):  
Elisa Guerrero Vázquez ◽  
Joaquín Pizarro Junquera ◽  
Andrés Yáñez Escolano ◽  
Pedro L. Riaño Galindo
Keyword(s):  
Model Selection ◽  
Performance Assessment ◽  
Nonlinear Model ◽  
Empirical Performance

A Fitting Method for Establishing an Aeroengine’s State Variable Model Based on Process Identification

2012 ◽  
Vol 236-237 ◽  
pp. 1356-1361
Author(s):  
Xiang Xing Kong ◽  
Xi Wang
Keyword(s):  
Nonlinear Model ◽  
Good Accuracy ◽  
Dynamic Performance ◽  
Variable Model ◽  
Process Identification ◽  
State Variable ◽  
Fitting Method ◽  
Derivative Method ◽  
State Variable Model ◽  
Operating Points

The bias derivative method for aeroengine’s State Variable Model(SVM) doesn’t have a satisfying accuracy. This method usually needs a linear modification to achieve a higher accuracy. In order to obtain a SVM with a good accuracy, this paper proposes a process identification based method. In this method, the coefficient matrices of the SVM are identified based on the input and output of the nonlinear model, according to the principle that the step responses of the SVM and the nonlinear model should be consistent. Then, the SVMs of small perturbation about steady operating points of an engine are established. Simulation results show that the SVMs established by the process identification based method have a good fidelity both in terms of steady and dynamic performance.

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