A new idea based on the field test data for guided aircraft aerodynamic model parameters modification

2008 ◽  
Author(s):  
Li Xing-Wei ◽  
Pan Xiao
Keyword(s):  
Field Test ◽  
Test Data ◽  
Model Parameters ◽  
Aerodynamic Model

Assessment of DFIG simplified model parameters using field test data

2012 ◽  
Author(s):  
A. Honrubia-Escribano ◽  
E. Gomez-Lazaro ◽  
A. Vigueras-Rodriguez ◽  
A. Molina-Garcia ◽  
J. A. Fuentes ◽  
...  
Keyword(s):  
Field Test ◽  
Test Data ◽  
Simplified Model ◽  
Model Parameters

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

scholarly journals System Identification of Alfred Zampa Memorial Bridge Using Dynamic Field Test Data

2009 ◽  
Vol 135 (1) ◽  
pp. 54-66 ◽  
Author(s):  
Xianfei He ◽  
Babak Moaveni ◽  
Joel P. Conte ◽  
Ahmed Elgamal ◽  
Sami F. Masri
Keyword(s):  
System Identification ◽  
Field Test ◽  
Test Data ◽  
Dynamic Field

Analysis of Pressure Pulsations in Reciprocating Compressor Piping Systems

1966 ◽  
Vol 88 (2) ◽  
pp. 164-168 ◽  
Author(s):  
S. S. Grover
Keyword(s):  
Field Test ◽  
Test Data ◽  
Piping System ◽  
Reciprocating Compressor ◽  
Practical Application ◽  
Pressure Pulsations ◽  
System A ◽  
Piping Systems ◽  
Limiting Condition

This paper deals with pulsations in pressure and flow in the reciprocating compressor and connected piping system. A model is presented that describes the excitation at the compressor and the propagation of the pulsations in the interconnected piping. It has been adapted to digital computations to predict the pulse magnitudes in reciprocating compressor piping systems and to assess measures for their control. Predicted results have been compared with field test data and with simplified limiting condition results. A discussion of its practical application is included.

scholarly journals Flutter Test Data Processing Based on Improved Hilbert-Huang Transform

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Hua Zheng ◽  
Junhao Liu ◽  
Shiqiang Duan
Keyword(s):  
Data Processing ◽  
Test Data ◽  
Model Parameters ◽  
Modal Parameter ◽  
Hilbert Huang Transform ◽  
Modal Density ◽  
Flutter Boundary ◽  
Modal Parameter Estimation ◽  
Nonstationary Conditions ◽  
Typical Mode

Flutter tests are conducted primarily for the purpose of modal parameter estimation and flutter boundary prediction, the accuracy of which is severely affected by the acquired data quality, structural modal density, and nonstationary conditions. An improved Hilbert-Huang Transform (HHT) algorithm is presented in this paper which mitigates the typical mode mixing effect via modulation. The algorithm is validated by theory, by numerical simulation, and per actual flight flutter test data. The results show that the proposed method could extract the flutter model parameters and predict the flutter speed more accurately, which is feasible for the current flutter test data processing.

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