基于粒子群算法和投影追踪分析的干气密封动态特性优化

干气密封是轴类密封中重要的密封方式之一，尤其适用于对于密封可靠性要求较高的装置中。由于密封气膜与密封环的位置设计关系，浮动环的振动关系着密封气膜刚度值的变化，因此浮动环系统的动态特性影响着干气密封的可靠性。作者提出了一种基于粒子群优化（particle swarm optimization）与投影追踪分析（projection pursuit）相结合的动态特性优化方法；依据工况条件，建立浮动环系统预应力模态分析模型，对实验测试结果进行频谱分析验证模态分析的准确性；实验结果与分析结果证明，为提高干气密封可靠性，需对浮动环系统进行动态特性优化，选取浮动环系统中的轴向设计参数为优化参数，将响应面方法（response surface methodology）与Box-Behnken试验设计相结合分别获得优化目标和约束条件关于优化参数的完整2次多项式响应面模型，实现隐性关系显性化，采用粒子群优化算法以浮动环系统固有频率为优化目标函数，系统静变形为约束进行快速优化，在系统静变形量小于要求值的条件下，使得系统固有频率值增大到142 Hz，与原始固有频率值相比提高了20%，并获得了优化参数与固有频率的正反比关系，最后，通过投影追踪分析得到优化参数对系统固有频率的影响程度；基于粒子群优化算法与投影追踪分析相结合的优化方法将浮动环系统固有频率提高到高于所给工况最高转速8 000 r/min（133 Hz）并理论性获得结构参数对于系统动态特性的影响程度。

Dynamic Feature Optimization of Dry Gas Seal Based on Particle Swarm Optimization and Projection Pursuit

Dry gas seal is one of the important sealing methods in shaft seal, especially for the devices with high sealing reliability requirements. Due to the designed relationship between the sealing gas film and the seal rings, the vibration of the floating ring is related to the change of the sealing gas film stiffness value. Therefore, the dynamic feature of floating ring system affects the reliability of the dry gas seal. A dynamic feature optimization method based on the particle swarm optimization and the projection pursuit was proposed. According to the working conditions, the pre-stressed modal analysis of the floating ring system was completed, and the accuracy of the modal analysis was verified by the spectrum analysis from experiment. The results of the experiment and analysis proved that it was necessary to optimize the dynamic feature of the floating ring system to improve the reliability of dry gas seal. The axial design parameters of the floating ring system were selected as the optimization parameter. Combining the response surface methodology with the Box-Behnken experiment design, the complete quadratic polynomial response surface models of optimization objective and constraint about optimization parameters were presented, respectively, which made the implicit relations explicit. With the natural frequency of the floating ring system as the objective function and the system static deformation as the constraint, the natural frequency reached to 142 Hz efficiently through the particle swarm optimization algorithm, increased 20% compared to the original natural frequency. Furthermore, the positive and negative relationship between the natural frequency and the optimization parameters was defined. Finally, the influence degree of optimization parameters on the natural frequency was obtained through the projection pursuit analysis. Based on the combination of the particle swarm optimization and the projection pursuit, the natural frequency of the floating ring system was increased above the given highest operating rotational speed 8 000 r/min (133 Hz).