A filter-independent model identification technique for turbulent combustion modeling

2012 ◽  
Vol 159 (5) ◽  
pp. 1960-1970 ◽  
Author(s):  
Amir Biglari ◽  
James C. Sutherland
Keyword(s):  
Turbulent Combustion ◽  
Model Identification ◽  
Combustion Modeling ◽  
Identification Technique ◽  
Independent Model

scholarly journals A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Rubén Ibáñez ◽  
Emmanuelle Abisset-Chavanne ◽  
Amine Ammar ◽  
David González ◽  
Elías Cueto ◽  
...  
Keyword(s):  
Dimensional Space ◽  
Model Identification ◽  
Curse Of Dimensionality ◽  
Data Driven ◽  
Multidimensional Data ◽  
High Dimensional ◽  
Proper Generalized Decomposition ◽  
High Dimensional Space ◽  
Sparse Model ◽  
Identification Technique

Sparse model identification by means of data is especially cumbersome if the sought dynamics live in a high dimensional space. This usually involves the need for large amount of data, unfeasible in such a high dimensional settings. This well-known phenomenon, coined as the curse of dimensionality, is here overcome by means of the use of separate representations. We present a technique based on the same principles of the Proper Generalized Decomposition that enables the identification of complex laws in the low-data limit. We provide examples on the performance of the technique in up to ten dimensions.

Dynamic Model Identification of Marine Electric Propulsion System

2012 ◽  
Vol 503-504 ◽  
pp. 1357-1359
Author(s):  
Qiang Wu ◽  
Hao Xiong ◽  
Guang Wei Meng ◽  
Li Bing Zhou
Keyword(s):  
Dynamic Model ◽  
Electric Propulsion ◽  
System Analysis ◽  
Model Identification ◽  
Propulsion System ◽  
Experimental Result ◽  
Experiment Data ◽  
Electric Propulsion System ◽  
Identification Technique ◽  
Propulsion Unit

This paper applies identification technique to the marine electric propulsion system analysis, adopts the recursive extended least squares (RELS) algorithm to estimate the structure and parameters of the model, employs the variable forgetting factors into the algorithm to improve the tracking characteristic of the parameters, establishes the dynamic model of a simulated electric propulsion unit under the excitation control based on the experiment data, and finally verifies the validity of the method through the consistency between simulation result and experimental result.

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