The forced responses of bladed disks are highly sensitive to inevitable random mistuning. Considerable computational efforts are required for the sampling process to assess the statistical vibration properties of mistuned bladed disks. Therefore, efficient surrogate models are preferred to accelerate the process for probabilistic analysis. In this paper, four surrogate models are utilized to construct the relation between random mistuning and forced response amplitudes, which are polynomial chaos expansion (PCE), response surface method (RSM), artificial neural networks (ANN) and Kriging interpolation, respectively. A bladed disk with 2-degrees-of-freedom (2-DOF) each sector is used to validate the effectiveness of the surrogate models. The effects of number of training samples on the surrogate model accuracy are discussed. The responses results of one blade (single output) and maximum response of all blades (multi-output) indicate that PCE and Kriging interpolation could yield accurate and stable predictions of the statistical characteristics of the forced responses. PCE is recommended for the mistuned response predictions due to its accuracy and efficiency.

中文翻译：

---

失谐叶片响应分析替代模型的比较研究

叶片盘的强制响应对不可避免的随机失谐高度敏感。采样过程需要大量的计算工作来评估失谐叶片盘的统计振动特性。因此，首选有效的代理模型来加速概率分析的过程。本文采用多项式混沌扩展（PCE）、响应面法（RSM）、人工神经网络（ANN）和克里金插值法四种替代模型来构建随机失谐与强制响应幅度之间的关系。每个扇区具有 2 个自由度 (2-DOF) 的叶片圆盘用于验证代理模型的有效性。讨论了训练样本数量对代理模型准确性的影响。一个叶片（单输出）的响应结果和所有叶片（多输出）的最大响应表明，PCE和克里金插值可以对受迫响应的统计特性产生准确和稳定的预测。由于其准确性和效率，建议将 PCE 用于失谐响应预测。