Reduced-order Model Predictions of Wind Turbines via Mode Decomposition and Sparse Sampling

2021 ◽  
Author(s):  
Ala' E. Qatramez ◽  
Daniel Foti
Keyword(s):  
Wind Turbines ◽  
Sparse Sampling ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order ◽  
Mode Decomposition ◽  
Model Predictions

Modal Analysis of Wake behind Stationary and Vibrating Cylinders

2020 ◽  
pp. 1-24
Author(s):  
Marek Janocha ◽  
Guang Yin ◽  
Muk Chen Ong
Keyword(s):  
Reynolds Number ◽  
Modal Analysis ◽  
Reduced Order Model ◽  
Dynamic Mode Decomposition ◽  
Navier Stokes ◽  
Dynamic Mode ◽  
Order Model ◽  
Reduced Order ◽  
Mode Decomposition ◽  
Sst Turbulence Model

Abstract The Dynamic Mode Decomposition (DMD) and Proper Orthogonal Decomposition (POD) are used to analyze the coherent structures of turbulent flow around vibrating isolated and piggyback cylinders configurations subjected to a uniform flow at a laminar Reynolds number (Re=200) and a upper transition Reynolds number (Re=3.6×106). Numerical simulations using two-dimensional URANS (Unsteady Reynolds Averaged Navier-Stokes) approach with the k-omega SST turbulence model are used to obtain the flow fields snapshots for the analysis. The wake flows behind the cylinders are decomposed into energy optimal modes (POD modes) and dynamical relevant modes (DMD modes). A reduced-order model for the flow is built based on the modal analysis. A comparison of POD and DMD is performed to characterize their special features. The present study provides new insights into the flow physics of fluid-structure interaction problem of two coupled cylinders. The characteristic vortex shedding frequencies and their harmonics are identified by DMD modes in all the investigated configurations. It is observed that for single cylinder configurations the most energetic and the most dynamically important mode is associated with the fundamental shedding frequency. For the stationary piggyback configuration, the gap flow between the cylinders appears to be a dominant flow feature as evidenced by leading DMD modes. The cylinder vibration increases significantly number of modes necessary to obtain a reduced order model (ROM) at given level of accuracy compared to respective stationary configurations.

scholarly journals Bolt loosening detection in a jointed beam using empirical mode decomposition–based nonlinear system identification method

2019 ◽  
Vol 15 (9) ◽  
pp. 155014771987565 ◽  
Author(s):  
Chao Xu ◽  
Chen-Chen Huang ◽  
Wei-Dong Zhu
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order ◽  
System Identification Method ◽  
Identification Method ◽  
Mode Decomposition ◽  
Bolt Loosening

In this work, a state-of-art nonlinear system identification method based on empirical mode decomposition is utilized and extended to detect bolt loosening in a jointed beam. This nonlinear system identification method is based on identifying the multi-scale dynamics of the underlying system. Only structural dynamic response signals are needed to construct a reduced-order model to represent the system concerned. It makes the method easy to use in practice. A new bolt loosening identification procedure based on the constructed system nonlinear reduced-order model is proposed. A new damage feature to indicate bolt loosening is presented. Experimental works are carried out to validate the proposed method. The results show that the proposed damage detection method can detect bolt loosening effectively, and the proposed damage feature values increase with the increase of bolt torques. The damage feature calculated from the response solution of the reduced-order model can give robust and sensitive indication of bolt loosening.

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