Structural damage identification of offshore wind turbines: A two‐step strategy via FE model updating

2021 ◽  
Author(s):  
Zhiming Zhang ◽  
Chao Sun ◽  
Vahid Jahangiri
Keyword(s):  
Wind Turbines ◽  
Structural Damage ◽  
Damage Identification ◽  
Model Updating ◽  
Offshore Wind ◽  
Fe Model ◽  
Offshore Wind Turbines ◽  
Structural Damage Identification ◽  
Fe Model Updating

scholarly journals Comparative Modal Analysis of Monopile and Jacket Supported Offshore Wind Turbines including Soil-Structure Interaction

2020 ◽  
Vol 20 (10) ◽  
pp. 2042016
Author(s):  
A. Abdullahi ◽  
Y. Wang ◽  
S. Bhattacharya
Keyword(s):  
Wind Turbines ◽  
Soil Structure ◽  
Natural Frequencies ◽  
Model Updating ◽  
Offshore Wind ◽  
Fe Model ◽  
Analysis And Design ◽  
Offshore Wind Turbines ◽  
Future Design ◽  
Wide Range

Offshore wind turbines (OWTs) have emerged as a reliable source of renewable energy, witnessing massive deployment across the world. While there is a wide range of support foundations for these structures, the monopile and jacket are most utilized so far; their deployment is largely informed by water depths and turbine ratings. However, the recommended water depth ranges are often violated, leading to cross-deployment of the two foundation types. This study first investigates the dynamic implication of this practice to incorporate the findings into future analysis and design of these structures. Detailed finite element (FE) models of Monopile and Jacket supported OWTs are developed in the commercial software, ANSYS. Nonlinear soil springs are used to simulate the soil-structure interactions (SSI) and the group effects of the jacket piles are considered by using the relevant modification factors. Modal analyzes of the fixed and flexible-base cases are carried out, and natural frequencies are chosen as the comparison parameters throughout the study. Second, this study constructs a few-parameters SSI model for the two FE models developed above, which aims to use fewer variables in the FE model updating process without compromising its simulation quality. Maximum lateral soil resistance and soil depths are related using polynomial equations, this replaces the standard nonlinear soil spring model. The numerical results show that for the same turbine rating and total height, jacket supported OWTs generally have higher first-order natural frequencies than the monopile supported OWTs, while the reverse is true for the second-order vibration modes, for both fixed and flexible foundations. This contributes to future design considerations of OWTs. On the other hand, with only two parameters, the proposed SSI model has achieved the same accuracy as that using the standard model with seven parameters. It has the potential to become a new SSI model, especially for the identification of soil properties through the model updating process.

Review and Prospect on Dynamic FE Model Updating Methods of Footbridge Structures Based on Ambient Excitation

2011 ◽  
Vol 291-294 ◽  
pp. 1572-1577
Author(s):  
Rui Zhao ◽  
Yi Gang Zhang
Keyword(s):  
Structural Damage ◽  
Optimization Design ◽  
Damage Identification ◽  
Model Updating ◽  
Target Function ◽  
Optimization Theory ◽  
Empirical Method ◽  
Fe Model ◽  
Fe Model Updating ◽  
Ambient Excitation

The discrete finite element (FE) model often cannot reflect structure characteristics accurately due to imply more idealistic assumptions and simplifications. Therefore, it is necessary to update FE model for structural damage identification, response calculation, safety evaluation, optimization design, and so on. This article will illustrate respectively three key steps of updating parameters selection, target function selection and optimization method in process of dynamic FE model updating of footbridge structures based on ambient excitation, and put forward a feasible updating method: combine empirical method with sensitivity analysis method to select updating parameters; joint natural frequencies, MAC and modal flexibility as target function; adopt optimization algorithm based on the optimization theory.

Structural Damage Identification Based on Model Updating

2006 ◽  
Author(s):  
T. Yin ◽  
L. Yu ◽  
H. P. Zhu
Keyword(s):  
Objective Function ◽  
Structural Damage ◽  
Damage Identification ◽  
Model Updating ◽  
Frame Structure ◽  
Mode Shapes ◽  
Fe Model ◽  
Portal Frame ◽  
Structural Damage Identification ◽  
Portal Frame Structure

This paper presents a new method for structural damage identification based on the finite element (FE) model updating techniques. First, an objective function is defined as minimizing the sum of differences between the experimental and analytical modal data (natural frequencies and mode shapes), which is set as a nonlinear least-squares problem with bound-constrains. The trust-region approach is then used to solve the minimization problem in order to make this optimization process more robust and reliable. In addition, the expansion and weighting of the original objective function are investigated so that the presented method can be well applied into the damage identification of more real structures. Finally, a numerical simulation model of two-story portal frame structure is adopted to evaluate the efficiency of the proposed technique when both the single and multiple damage cases are set up in the model. Some important issues are also discussed in this paper. The illustrated results show that the single and multiple damages on the two-story portal frame structure can be well identified by the proposed method.

Local Mass Addition and Data Fusion for Structural Damage Identification Using Approximate Models

2020 ◽  
Vol 20 (11) ◽  
pp. 2050124
Author(s):  
Jilin Hou ◽  
Zhenkun Li ◽  
Qingxia Zhang ◽  
Łukasz Jankowski ◽  
Haibin Zhang
Keyword(s):  
Data Fusion ◽  
Objective Function ◽  
Structural Damage ◽  
Damage Identification ◽  
Structural Parameters ◽  
Identification Accuracy ◽  
Fe Model ◽  
Local Damage ◽  
Approximate Models ◽  
Structural Damage Identification

In practical civil engineering, structural damage identification is difficult to implement due to the shortage of measured modal information and the influence of noise. Furthermore, typical damage identification methods generally rely on a precise Finite Element (FE) model of the monitored structure. Pointwise mass alterations of the structure can effectively improve the quantity and sensitivity of the measured data, while the data fusion methods can adequately utilize various kinds of data and identification results. This paper proposes a damage identification method that requires only approximate FE models and combines the advantages of pointwise mass additions and data fusion. First, an additional mass is placed at different positions throughout the structure to collect the dynamic response and obtain the corresponding modal information. The resulting relation between natural frequencies and the position of the added mass is sensitive to local damage, and it is thus utilized to form a new objective function based on the modal assurance criterion (MAC) and [Formula: see text]-based sparsity promotion. The proposed objective function is mostly insensitive to global structural parameters, but remains sensitive to local damage. Several approximate FE models are then established and separately used to identify the damage of the structure, and then the Dempster–Shafer method of data fusion is applied to fuse the results from all the approximate models. Finally, fractional data fusion is proposed to combine the results according to the parametric probability distribution of the approximate FE models, which allows the natural weight of each approximate model to be determined for the fusion process. Such an approach circumvents the need for a precise FE model, which is usually not easy to obtain in real application, and thus enhances the practical applicability of the proposed method, while maintaining the damage identification accuracy. The proposed approach is verified numerically and experimentally. Numerical simulations of a simply supported beam and a long-span bridge confirm that it can be used for damage identification, including a single damage and multiple damages, with a high accuracy. Finally, an experiment of a cantilever beam is successfully performed.

scholarly journals Support condition monitoring of offshore wind turbines using model updating techniques

2019 ◽  
Vol 19 (4) ◽  
pp. 1017-1031 ◽  
Author(s):  
Ying Xu ◽  
George Nikitas ◽  
Tong Zhang ◽  
Qinghua Han ◽  
Marios Chryssanthopoulos ◽  
...  
Keyword(s):  
Finite Element ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Model Updating ◽  
Element Model ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Support Condition ◽  
Offshore Wind Turbines ◽  
Wind Turbine System

The offshore wind turbines are dynamically sensitive, whose fundamental frequency can be very close to the forcing frequencies activated by the environmental and turbine loads. Minor changes of support conditions may lead to the shift of natural frequencies, and this could be disastrous if resonance happens. To monitor the support conditions and thus to enhance the safety of offshore wind turbines, a model updating method is developed in this study. A hybrid sensing system was fabricated and set up in the laboratory to investigate the long-term dynamic behaviour of the offshore wind turbine system with monopile foundation in sandy deposits. A finite element model was constructed to simulate structural behaviours of the offshore wind turbine system. Distributed nonlinear springs and a roller boundary condition are used to model the soil–structure interaction properties. The finite element model and the test results were used to analyse the variation of the support condition of the monopile, through an finite element model updating process using estimation of distribution algorithms. The results show that the fundamental frequency of the test model increases after a period under cyclic loading, which is attributed to the compaction of the surrounding sand instead of local damage of the structure. The hybrid sensing system is reliable to detect both the acceleration and strain responses of the offshore wind turbine model and can be potentially applied to the remote monitoring of real offshore wind turbines. The estimation of distribution algorithm–based model updating technique is demonstrated to be successful for the support condition monitoring of the offshore wind turbine system, which is potentially useful for other model updating and condition monitoring applications.

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