| Citation: | JIANG Yuanyuan, JIANG Xianghua, DU Chenhong. Prediction model of nonlinear multimodal damping characteristics of blade shrouds based on IWOA-ELM[J]. Journal of Aerospace Power, 2026, 41（X）：20250461 doi: 10.13224/j.cnki.jasp.20250461
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Due to the strong nonlinearity of shroud contact interfaces and enormous computational cost associated with multimodal damping analysis, conventional methods fail to achieve comprehensive, efficient, and accurate solutions. To address this challenge, an improved whale optimization algorithm-extreme learning machine (IWOA-ELM) model was proposed for multimodal damping characteristic prediction, enabling fast and highly accurate mapping from extremely limited inputs to massive outputs under very short training times and small datasets. A lightweight training dataset was constructed using the energy method combined with finite-element modal analysis. An improved WOA (IWOA) was developed by incorporating a history-memory-based group collaborative strategy and Sobol reverse initialization, and its superior global optimization capability was verified using the CEC2017 benchmark suite. The IWOA was further employed to optimize the weights and biases of the ELM, yielding a powerful network capable of realizing extremely low-input and ultra-high-output prediction. Experimental validation was conducted on a shrouded bladed-disk structure. The input consisted of damping ratios at five stress points under the first bending mode, while the output corresponded to the complete damping characteristics (
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