A Worked-out Example of Surrogate-based Bayesian Parameter and Field Identification Methods

2021 ◽  
pp. 155-203
Author(s):  
Noémi Friedman ◽  
Claudia Zoccarato ◽  
Elmar Zander ◽  
Hermann G. Matthies
Keyword(s):  
Identification Methods ◽  
Field Identification

Current distribution of Nilgiri grass yellow Eurema nilgiriensis Yata (Lepidoptera: Pieridae), with an updated taxonomic key to Eurema of Western Ghats, India

ENTOMON ◽  
2019 ◽  
Vol 44 (1) ◽  
pp. 23-32 ◽  
Author(s):  
P. C. Sujitha ◽  
G. Prasad ◽  
R. Nitin ◽  
Dipendra Nath Basu ◽  
Krushnamegh Kunte ◽  
...  
Keyword(s):  
Current Distribution ◽  
Western Ghats ◽  
Original Description ◽  
High Elevation ◽  
Type Locality ◽  
Ecological Data ◽  
Field Identification ◽  
Wing Patterns ◽  
Southern Western Ghats ◽  
The Nilgiris

Eurema nilgiriensis Yata, 1990, the Nilgiri grass yellow, was described from Nilgiris in southern India. There are not many published records of this species since its original description, and it was presumed to be a high-elevation endemic species restricted to its type locality. Based on the external morphology (wing patterns) as well as the male genitalia, the first confirmed records of the species from Agasthyamalais and Kodagu in the southern Western Ghats, is provided here. This report is a significant range extension for the species outside the Nilgiris, its type locality. Ecological data pertaining to this species as well as the field identification key to all known Eurema of Western Ghats are also presented.

System Identification Methods for Improving Flutter Flight Test Techniques

2004 ◽  
Author(s):  
David Klyde ◽  
Chuck Harris ◽  
Peter M. Thompson ◽  
Edward N. Bachelder
Keyword(s):  
System Identification ◽  
Flight Test ◽  
Identification Methods

Field Identification of Lithologic Discontinuities

Soil Horizons ◽  
1984 ◽  
Vol 25 (3) ◽  
pp. 3
Author(s):  
R. B. Parsons
Keyword(s):  
Field Identification

scholarly journals Comparative Analysis of Identification Methods for Mechanical Dynamics of Large-Scale Wind Turbine

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3429 ◽  
Author(s):  
Chu ◽  
Yuan ◽  
Hu ◽  
Pan ◽  
Pan
Keyword(s):  
Mutual Information ◽  
Parameter Identification ◽  
Wind Turbines ◽  
Closed Loop ◽  
Control Design ◽  
Identification Accuracy ◽  
Mass Model ◽  
Identification Methods ◽  
Advanced Control ◽  
Mechanical Dynamics

With increasing size and flexibility of modern grid-connected wind turbines, advanced control algorithms are urgently needed, especially for multi-degree-of-freedom control of blade pitches and sizable rotor. However, complex dynamics of wind turbines are difficult to be modeled in a simplified state-space form for advanced control design considering stability. In this paper, grey-box parameter identification of critical mechanical models is systematically studied without excitation experiment, and applicabilities of different methods are compared from views of control design. Firstly, through mechanism analysis, the Hammerstein structure is adopted for mechanical-side modeling of wind turbines. Under closed-loop control across the whole wind speed range, structural identifiability of the drive-train model is analyzed in qualitation. Then, mutual information calculation among identified variables is used to quantitatively reveal the relationship between identification accuracy and variables’ relevance. Then, the methods such as subspace identification, recursive least square identification and optimal identification are compared for a two-mass model and tower model. At last, through the high-fidelity simulation demo of a 2 MW wind turbine in the GH Bladed software, multivariable datasets are produced for studying. The results show that the Hammerstein structure is effective for simplify the modeling process where closed-loop identification of a two-mass model without excitation experiment is feasible. Meanwhile, it is found that variables’ relevance has obvious influence on identification accuracy where mutual information is a good indicator. Higher mutual information often yields better accuracy. Additionally, three identification methods have diverse performance levels, showing their application potentials for different control design algorithms. In contrast, grey-box optimal parameter identification is the most promising for advanced control design considering stability, although its simplified representation of complex mechanical dynamics needs additional dynamic compensation which will be studied in future.

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