Composite structures have been widely used in wind turbine equipment for their high stiffness to mass ratio and high strength. A major concern in the use of composite materials is their susceptibility to various micro damage, such as fiber breakage and matrix crack, which will lead to macroscopic structural fracture. In this paper, a multi-scale modeling strategy is proposed to investigate failure mechanisms and damage evolution of composite blades with initial defects from microscopic damage (including fiber fractures and matrix cracks) to macroscopic fracture. At the microscopic scale, an isoparametric micromechanical model is developed to calculate microscopic stresses and simulate microscopic damage. At the laminar scale, the classic laminate theory is employed to evaluate the laminate stiffness. At the structural scale, a reverse modeling technology is proposed to accurately acquire structural dimensions of a wind turbine blade, and a macroscopic 3D model is implemented into ANSYS/LS-DYNA software. By comparing with the experimental data, it is demonstrated that the proposed multi-scale method is suitable to predict mechanical properties of complex composite structures effectively.
Establishment of damage accumulation models for reflecting the combined damage mechanism on the fatigue behavior of aero-engine turbine blades is crucial for their safety. In this work, a novel combined high and low cycle fatigue (CCF) life prediction methodology is presented as a basis of that to consider the interaction between low and high cycle fatigues. Accordingly, a dynamic reliability model is proposed to study the operational reliability of turbine blades under CCF loadings. Moreover, experimental data of materials along with the collected field data from the actual turbine blades are applied to validate the CCF life prediction model and the dynamic reliability model. The validation of the results is conducted by a comparison analysis, which indicates that the proposed life prediction method yields better accuracy, while the dynamic reliability model is proved to be more in line with the outcomes derived by the Monte Carlo simulation.
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