Long-term durability of offshore wind turbine composite blades based on nonlinear load behavior due to pitch movement

Jang, Yun-Jung; Jin, Ji-Won; Lee, Jang‐Ho; S, Lee; Kang, Ki-Weon

doi:10.1007/s12206-020-0511-y

Cited by 2 publications

(3 citation statements)

References 21 publications

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“…Under the combined effects of the long-term effect of external load, fatigue effect of vehicle load, erosion effect of the external environment, the aging effect of internal materials, and mutation effect of accidental disasters, various structural damage problems will inevitably occur in the design service life of bridge structure [8]. Especially in recent years, with the rapid development of China's transportation industry and the increasingly prominent characteristics of heavy load and heavy traffic, the load standard of some domestic bridge structures has far exceeded the original design value, and there is the risk of structural damage accumulation damage, resulting in major economic and safety accidents, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Application of Distributed Network in Bridge Structure Safety Inspection

2022

Security and Communication Networks

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In order to solve the problems of the monitoring and health assessment of the main structure of bridges, and to provide technical support for the bridge from the regular inspection to the predictive maintenance mode, a distributed network is proposed for the safety inspection of bridge structures. Through the numerical simulation of the bridge, the finite element model is established and the modal analysis is carried out to obtain the modal data before and after the damage. The damage index of the bridge structure is taken as the input and output variables after the curvature of the modal data, and the nonlinear mapping relationship between the input variables and output variables is established. A large amount of damage modal data is randomly formed into the training set and the test set, and the training set is used to train the neural network. The training accuracy is set to 10−3, and the learning rate is set to 0.01. The test set data is used to identify the damage to the neural network after the training. The experimental results show that the developed program is more accurate in identifying the damage position of the simply supported beam and the continuous beam, and the fitting degree between the predicted value and the real value of the damage degree of the structure can reach 0.97. It is concluded that the damage identification program can intelligently identify and predict two common types of bridge structural damage, namely, the simply supported beam and the continuous beam. And the identification effect is good and has certain feasibility.

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Section: Introductionmentioning

confidence: 99%

Application of Distributed Network in Bridge Structure Safety Inspection

2022

Security and Communication Networks

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“…Particularly, if the wind blade is operated under various environmental conditions, the load applied to the blade leads to combined load conditions. These combined loads have a significant adverse effect on the structural integrity of blades compared to the simple load condition in which only a unidirectional load is applied [14]. Therefore, the debonding damage in the adhesive joints of wind blades should be analyzed under combined load conditions.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, Mixed-mode tests and analyses were performed in accordance with ASTM D6671 [15] based on the fracture toughness calculated through the ASTM D5528 (Mode I) test [16] and D7905 (Mode II) tests [17,18] in a previous study [19]. Moreover, the combined load calculated in the integrated load analysis [14] was applied to the blade model to simulate the effect of the combined load borne by the blade. Through the above process, a prediction model was proposed for the initiation/growth of debonding damage in adhesive joints.…”

Section: Introductionmentioning

confidence: 99%

Progressive Failure Analysis for 5MW-Class Wind Turbine Composite Blades with Debonding Damage based on CZM Method

2022

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Composite wind turbine blades may experience interlaminar damage, including adhesive failure, cracking, and interlaminar fracture failure, from manufacturing or external fatigue load. Among these, the adhesive failure of adhesive joints is critical. Therefore, it is important to identify the failure mechanism in the adhesive joints between the spar cap–shear web and trailing edge of composite blades. We calculated fracture toughness through Mode I, Mode II, and Mixed-mode tests for quantitative analysis of adhesive joints. Then, to select a modeling method for realizing the damage generated in the blade, the method was verified from the specimen level. A damage model was constructed, considering contact conditions of the spar cap–shear web and trailing edge for an NREL 5MW wind turbine blade. Finally, a damage model based on cohesive zone modeling was used to analyze the progressive failure behavior of debonding at adhesive joints according to the external force applied to the blade.

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Long-term durability of offshore wind turbine composite blades based on nonlinear load behavior due to pitch movement

Cited by 2 publications

References 21 publications

Application of Distributed Network in Bridge Structure Safety Inspection

Application of Distributed Network in Bridge Structure Safety Inspection

Progressive Failure Analysis for 5MW-Class Wind Turbine Composite Blades with Debonding Damage based on CZM Method

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