This study aims to develop an automated and physically consistent framework for modal identification and reinforcement optimization of large hydroturbine head covers. The objective is to improve the efficiency, objectivity and reliability of modal characterization, while supporting reinforcement design toward prescribed target modal distributions.
A parametric modeling and automated prestressed modal analysis procedure is established for hydroturbine head covers. A weighted least-squares discrete Fourier fitting method is developed to identify nodal diameters from non-uniform circumferential finite-element samples. A coordinated radial-circumferential stiffening scheme is then constructed and its parameter effects on modal characteristics are analyzed. Based on the generated dataset, a modal-order-constrained surrogate model is trained and combined with a global optimization algorithm to determine reinforcement parameters toward prescribed target modal distributions.
The coordinated reinforcement scheme increases the average values of the first ten natural frequencies by 24.51%–54.52%. The proposed nodal diameter identification method enables robust modal feature extraction under non-uniform sampling. The surrogate model achieves high predictive accuracy, with an R2 of 0.9999. For a prescribed target modal distribution, the relative deviations between the optimized and target frequencies of the first ten modes remain below 0.15% after finite element validation.
This study provides an integrated computational framework that combines automated finite element analysis, quantitative nodal diameter identification, physics-constrained machine learning and reinforcement optimization. The proposed method transforms modal interpretation and reinforcement design from experience-based procedures into a quantitative and data-driven workflow. It offers an effective tool for improving the dynamic performance and structural integrity of large stiffened hydroturbine components.
