What is it about?
An integrated dynamic model updating method is proposed to address the challenges of mode jumping and mode degeneracy for complex dynamic models. A reduced order proxy model of Neural Network Response Surface (NNRS) was constructed by Modal Matching Reconstruction Strategy (MMRS) and an Improved Vectorial Surrogate Model (IVSM). Among them, the analytical modes are correctly matched with the experimental modes by MMRS, and the order of the mode matching is determined by calculating the Modal Assurance Criterion (MAC), addressing the dynamic changes of the mode matching order during the construction of the NNRS. The fitted NNRS model results are vectorized by IVSM, enabling the rapid extraction of required input and output parameters under multi-parameter conditions, thereby improving efficiency. The model parameters are updated using a multi-objective genetic algorithm, which achieves the simultaneous updating of natural frequency and mode shape. To validate the accuracy and efficiency, an intermediate casing of a gas turbine was updated using the proposed method. With high efficiency, the mean absolute error of natural frequency for the matched order decreased from 24.46% to 3.89%, while the corresponding average MAC value increased from 0.654 to 0.752.
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Why is it important?
1. A neural network response surface model integrating the Modal Matching Reconstruction Strategy (MMRS) is proposed to solve the problems of mode jumping and mode degeneracy of complex structural dynamic model updating. 2. An efficient multi-objective reduced-order modeling method based on the improved vectorized surrogate model (IVSM) is proposed to enhance the efficiency of response prediction for complex dynamic systems. 3. A dynamic model update framework integrating the NNRS surrogate model and multi-objective genetic algorithm is established to achieve high-precision and high-efficiency updates for complex equipment structures.
Perspectives
Writing this article has been a very meaningful experience for me and my collaborators, as it reflects our long-term exploration process of enhancing the reliability and efficiency of dynamic model updates for complex engineering structures. In this research, we attempted to combine artificial intelligence and data-driven methods with the physical knowledge in modal analysis to solve key problems in engineering applications. I hope this work will facilitate further discussions on how intelligent methods can help build more accurate digital models, and provide support for the safe and reliable operation of high-end equipment.
Xiang Li
Beijing University of Chemical Technology
Read the Original
This page is a summary of: A neural network response surface updating method for complex dynamic models based on modal matching reconstruction strategy, Chinese Journal of Aeronautics, May 2026, Tsinghua University Press,
DOI: 10.1016/j.cja.2025.103809.
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