@inproceedings{0f268bf9c28d47738bb4bf81a41f73f4,
title = "Rapid Identification of Dynamic Characteristics for Squeeze Film Dampers Under Static Eccentricity Conditions*",
abstract = "This study investigates the rapid identification of dynamic characteristics for squeeze film dampers under static eccentricity conditions. Fluid domain models of squeeze film damper with varying static eccentricities and oil film widths were established to analyze their influence on the dynamic coefficients. Four machine learning models were developed and compared for their accuracy in predicting the dampers' dynamic characteristics. The results indicate that an increase in static eccentricity significantly enhances damping and stiffness, while a larger oil film width concurrently increases both damping and stiffness. The Particle Swarm Optimization - Backpropagation Neural Network demonstrated optimal performance in predicting dynamic characteristics under static eccentricity conditions. The findings provide a foundation for the rapid design and intelligent control of dampers.",
keywords = "dynamic characteristics, machine learning, Squeeze film damper, static eccentricity",
author = "Yihao Sun and Qi Jin and Shiming Liu and Runji Yang and Zhongliang Xie",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 6th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2025 ; Conference date: 21-11-2025 Through 23-11-2025",
year = "2025",
doi = "10.1109/ICSMD67131.2025.11365410",
language = "英语",
series = "ICSMD 2025 - International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICSMD 2025 - International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence",
}