A reduced-order model for the near wake dynamics of a wind turbine: Model development and uncertainty quantification

2021 ◽  
Author(s):  
Ala' Eyad Qatramez ◽  
Daniel Foti
Keyword(s):  
Wind Turbine ◽  
Uncertainty Quantification ◽  
Model Development ◽  
Reduced Order Model ◽  
Order Model ◽  
Near Wake ◽  
Reduced Order ◽  
Turbine Model

scholarly journals Reduced order model of offshore wind turbine wake by proper orthogonal decomposition

2020 ◽  
Vol 82 ◽  
pp. 108554 ◽  
Author(s):  
M. Salman Siddiqui ◽  
Sidra Tul Muntaha Latif ◽  
Muhammad Saeed ◽  
Muhammad Rahman ◽  
Abdul Waheed Badar ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Proper Orthogonal Decomposition ◽  
Orthogonal Decomposition ◽  
Reduced Order Model ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Order Model ◽  
Reduced Order ◽  
Proper Orthogonal ◽  
Turbine Wake

scholarly journals Data-driven Reduced Order Model for prediction of wind turbine wakes

2015 ◽  
Vol 625 ◽  
pp. 012009 ◽  
Author(s):  
G V Iungo ◽  
C Santoni-Ortiz ◽  
M Abkar ◽  
F Porté-Agel ◽  
M A Rotea ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Reduced Order Model ◽  
Data Driven ◽  
Order Model ◽  
Reduced Order

Development of a reduced order model for nonlinear analysis of the wind turbine blade dynamics

2015 ◽  
Vol 76 ◽  
pp. 264-282 ◽  
Author(s):  
Mohammad M. Rezaei ◽  
Mehdi Behzad ◽  
Hassan Haddadpour ◽  
Hamed Moradi
Keyword(s):  
Wind Turbine ◽  
Nonlinear Analysis ◽  
Turbine Blade ◽  
Wind Turbine Blade ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order

scholarly journals Reduced-Order Model Development for Airfoil Forced Response

2008 ◽  
Vol 2008 ◽  
pp. 1-12 ◽  
Author(s):  
Jeffrey M. Brown ◽  
Ramana V. Grandhi
Keyword(s):  
Model Development ◽  
Principal Component ◽  
Forced Response ◽  
Reduced Order Model ◽  
Response Analysis ◽  
Order Model ◽  
Reduced Order ◽  
Error Quantification ◽  
Modal Force ◽  
Geometry Deviation

Two new reduced-order models are developed to accurately and rapidly predict geometry deviation effects on airfoil forced response. Both models have significant application to improved mistuning analysis. The first developed model integrates a principal component analysis approach to reduce the number of defining geometric parameters, semianalytic eigensensitivity analysis, and first-order Taylor series approximation to allow rapid as-measured airfoil response analysis. A second developed model extends this approach and quantifies both random and bias errors between the reduced and full models. Adjusting for the bias significantly improves reduced-order model accuracy. The error model is developed from a regression analysis of the relationship between airfoil geometry parameters and reduced-order model error, leading to physics-based error quantification. Both models are demonstrated on an advanced fan airfoil's frequency, modal force, and forced response.

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