Dynamic Characteristics Analysis and Fatigue Damage Estimation of a Compressor Blade under Fluid-Structure Interaction 2018-01-1206
During the aero-engine operation, the compressor blades are subjected to periodic inertial force and aerodynamic excitation caused by blade rotation and airflow disturbance, respectively. Under the coupling alternating loads, the blade is prone to high cycle fatigue failure. In this paper, a time domain calculation model of fluid-structure interaction (FSI) is established to study the vibration characteristics of the blade and its failure modes are analyzed. Then, the fatigue damage of the blade under multi-level loading is evaluated by the nonlinear damage accumulation model. Considering the coupling effect of the airflow and the blade, computational fluid dynamics (CFD) is applied to calculate the aerodynamic parameters on the blade surface under different working conditions, which is imported to the finite element (FE) model to analyze the dynamic characteristics. According to the stress distribution under fluid-structure interaction loading, the fatigue critical location is determined to compile the loading spectrum and calculate the fatigue life of the blade. The results show that the vibration characteristics of the blade vary with loading conditions. Under the consideration of fluid-structure interaction between the airflow and the blade, a more accurate calculation of the critical speed can be obtained. The trailing edge at the blade root has distinctly stress concentration under the coupling alternating loads and it is the fatigue critical location where is liable to occur fatigue failure. Considering the effect of loading sequence, the multi-level loading damage model reflects the nonlinearity of damage accumulation behaviour of the blade and the calculation is more precise compared with Miner’s rule.
Citation: Fu, X., Zhang, J., Lin, J., Yuan, Y. et al., "Dynamic Characteristics Analysis and Fatigue Damage Estimation of a Compressor Blade under Fluid-Structure Interaction," SAE Technical Paper 2018-01-1206, 2018, https://doi.org/10.4271/2018-01-1206. Download Citation
Xi Fu, Junhong Zhang, Jiewei Lin, Yi Yuan, Zhiyuan Liu