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Purpose <p>Squeeze film dampers (SFDs) are critical components in aero-engines for enhancingrotor system dynamics by providing adjustable stiffness and damping, which improves resonance traversaland vibration suppression. However, their dynamic behavior is highly nonlinear and complex, necessitating acoupled analysis that integrates SFD structural parameters, stiffness, damping, and rotor system interactions.This study aims to develop an intelligent model for SFDs that accurately captures their nonlinearcharacteristics and validates its efficacy through rotor system analysis and experimentation.&#xa0;</p> Method <p>The oilfilm pressure distribution and static characteristics of the SFD are first analyzed using the finite differencemethod (FDM) applied to the Reynolds equation, with a focus on the influence of key structural parameterssuch as oil film clearance and eccentricity. A Whale Optimization Algorithm-Backpropagation (WOA-BP)neural network model is then developed, trained on extensive simulation data across varying speeds, topredict the equivalent nonlinear stiffness and damping coefficients. The intelligent model outputs areincorporated into a dynamic model of an SFD-rotor system to analyze unbalanced vibration responses,including time-domain, frequency-domain, and bifurcation characteristics. Finally, an experimental rotor testrig is constructed to validate the vibration reduction performance and nonlinear behavior of the SFD. Results:The FDM results reveal that oil film pressure distribution is highly sensitive to structural parameters, withsignificant nonlinear increases in pressure, stiffness, and damping observed when the oil film clearanceexceeds 0.4 mm or eccentricity surpasses 0.7. The WOA-BP model demonstrates high accuracy in predictingstiffness and damping, generating nonlinear curves that align well with numerical solutions. Simulation of theSFD-rotor system confirms substantial vibration reduction, with chaotic and bifurcation behaviors identified&#xa0;under certain speed conditions. Experimental tests further validate the damping effectiveness, showing&#xa0;notable amplitude reduction near critical speeds when the SFD is active.</p> Conclusion <p>This research establishes a robust data-driven framework for SFD design and analysis, effectively linking structural parameters to dynamic performance through an intelligent neural network model. The proposed approach not only improves the accuracy and efficiency of predicting SFD nonlinear properties but also provides practical insights into vibration control in rotor systems. The integration of numerical modeling, machine learning, and experimental validation offers a comprehensive methodology for optimizing SFD-equipped rotor systems in high-performance machinery.</p>

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Intelligent Model Design of Squeeze Film Dampers Based on Structural Parameters and Nonlinear Vibration Analysis of the SFD-Rotor System

  • 期刊论文

  • 发表日期：

    2025年10月30日

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  • Haobo Wang,
  • Qingkai Han,
  • Zhong Luo,
  • Yulai Zhao

摘要

Purpose

Squeeze film dampers (SFDs) are critical components in aero-engines for enhancingrotor system dynamics by providing adjustable stiffness and damping, which improves resonance traversaland vibration suppression. However, their dynamic behavior is highly nonlinear and complex, necessitating acoupled analysis that integrates SFD structural parameters, stiffness, damping, and rotor system interactions.This study aims to develop an intelligent model for SFDs that accurately captures their nonlinearcharacteristics and validates its efficacy through rotor system analysis and experimentation. 

Method

The oilfilm pressure distribution and static characteristics of the SFD are first analyzed using the finite differencemethod (FDM) applied to the Reynolds equation, with a focus on the influence of key structural parameterssuch as oil film clearance and eccentricity. A Whale Optimization Algorithm-Backpropagation (WOA-BP)neural network model is then developed, trained on extensive simulation data across varying speeds, topredict the equivalent nonlinear stiffness and damping coefficients. The intelligent model outputs areincorporated into a dynamic model of an SFD-rotor system to analyze unbalanced vibration responses,including time-domain, frequency-domain, and bifurcation characteristics. Finally, an experimental rotor testrig is constructed to validate the vibration reduction performance and nonlinear behavior of the SFD. Results:The FDM results reveal that oil film pressure distribution is highly sensitive to structural parameters, withsignificant nonlinear increases in pressure, stiffness, and damping observed when the oil film clearanceexceeds 0.4 mm or eccentricity surpasses 0.7. The WOA-BP model demonstrates high accuracy in predictingstiffness and damping, generating nonlinear curves that align well with numerical solutions. Simulation of theSFD-rotor system confirms substantial vibration reduction, with chaotic and bifurcation behaviors identified under certain speed conditions. Experimental tests further validate the damping effectiveness, showing notable amplitude reduction near critical speeds when the SFD is active.

Conclusion

This research establishes a robust data-driven framework for SFD design and analysis, effectively linking structural parameters to dynamic performance through an intelligent neural network model. The proposed approach not only improves the accuracy and efficiency of predicting SFD nonlinear properties but also provides practical insights into vibration control in rotor systems. The integration of numerical modeling, machine learning, and experimental validation offers a comprehensive methodology for optimizing SFD-equipped rotor systems in high-performance machinery.