论文标题
使用自适应多响应高斯过程元模型更新结构模型
Structural Model Updating Using Adaptive Multi-Response Gaussian Process Meta-modeling
论文作者
论文摘要
使用频率响应功能作为输入的有限元模型更新是结构分析，设计和控制中的重要过程。本文提出了一个高效的框架，该框架是基于高斯工艺仿真而建立的，可以通过采样来成型识别模型参数。特别是，制定了多回答高斯过程（MRGP）元模型方法，可以准确构建误差响应表面，即频率响应预测和实际测量之间的差异。为了减少重复有限元模拟的计算成本，建立了自适应采样策略，其中搜索未知参数的搜索由响应表面特征指导。同时，先前采样模型参数和相应误差的信息被用作附加训练数据来完善MRGP元模型。两种随机优化技术，即粒子群和模拟退火，用于训练MRGP元模型进行比较。进行系统的案例研究以检查新的模型更新框架的准确性和鲁棒性。
Finite element model updating utilizing frequency response functions as inputs is an important procedure in structural analysis, design and control. This paper presents a highly efficient framework that is built upon Gaussian process emulation to inversely identify model parameters through sampling. In particular, a multi-response Gaussian process (MRGP) meta-modeling approach is formulated that can accurately construct the error response surface, i.e., the discrepancies between the frequency response predictions and actual measurement. In order to reduce the computational cost of repeated finite element simulations, an adaptive sampling strategy is established, where the search of unknown parameters is guided by the response surface features. Meanwhile, the information of previously sampled model parameters and the corresponding errors is utilized as additional training data to refine the MRGP meta-model. Two stochastic optimization techniques, i.e., particle swarm and simulated annealing, are employed to train the MRGP meta-model for comparison. Systematic case studies are conducted to examine the accuracy and robustness of the new framework of model updating.
