摘要
A novel approach to identify the crystal orientation of turbine blades with anisotropy materials is proposed. Based on enhanced mode basis, with the main advantages of its efficiency,accuracy and general applicability, the blade vibration mode of each order is linearly constructed by several specified mode shapes, which are obtained from the considered turbine blade with specified crystal orientations correspondingly. Then, a surrogate model based on Kriging method is introduced for constructing the condensed perturbed matrix of stiffness in order to improve the efficiency even further. The constructed surrogate model allows to perform the modal analysis of turbine blades with arbitrary crystal orientations in higher efficiency, due to the fact that the elements of condensed perturbed matrix of stiffness are considered in construction of the surrogate model rather than concerning the perturbation of all the elements of the initial stiffness matrix for the blade. Genetic algorithm is finally employed to optimize the defined fitness functions in order to identify the crystal orientation angles of turbine blades. Several corresponding examples demonstrated the accuracy, efficiency and general applicability of the proposed method.
A novel approach to identify the crystal orientation of turbine blades with anisotropy materials is proposed. Based on enhanced mode basis, with the main advantages of its efficiency,accuracy and general applicability, the blade vibration mode of each order is linearly constructed by several specified mode shapes, which are obtained from the considered turbine blade with specified crystal orientations correspondingly. Then, a surrogate model based on Kriging method is introduced for constructing the condensed perturbed matrix of stiffness in order to improve the efficiency even further. The constructed surrogate model allows to perform the modal analysis of turbine blades with arbitrary crystal orientations in higher efficiency, due to the fact that the elements of condensed perturbed matrix of stiffness are considered in construction of the surrogate model rather than concerning the perturbation of all the elements of the initial stiffness matrix for the blade. Genetic algorithm is finally employed to optimize the defined fitness functions in order to identify the crystal orientation angles of turbine blades. Several corresponding examples demonstrated the accuracy, efficiency and general applicability of the proposed method.
基金
co-supported by the National Natural Science Foundation of China (Nos. 51405460, 11372128 and 51175244)
National Safety Academic Foundation of China (No. U1730129)
the Collaborative Innovation Center of Advanced Aero-Engine, Jiangsu Province Key Laboratory of Aerospace Power System,the Key Laboratory of Aero-Engine Thermal Environment and Structure, Ministry of Industry and Information Technology
