基于多目标遗传算法的上游泵送机械密封优化研究

发布时间：2018-01-04 04:46

  本文关键词：基于多目标遗传算法的上游泵送机械密封优化研究　出处：《江苏大学》2016年硕士论文　论文类型：学位论文

  更多相关文章： 上游泵送机械密封 均匀试验 多元回归 神经网络 多目标遗传算法

【摘要】：上游泵送机械密封作为一种典型的非接触机械密封,因其流体润滑、泄漏量小甚至无泄漏、使用寿命长等优势而得到推广应用。但是,随着科学技术的发展和对密封性能要求的不断提高,如何更加有效地设计出性能优秀的机械密封成为迫切需要解决的课题。机械密封端面造型参数是涉及密封性能优劣的关键,目前大多采用数值计算方法进行单因素单目标优化设计。虽有成效但很难达到高标准要求。为此本文在国家自然科学基金项目(项目编号:51279067)和航空科学基金项目(项目编号:201328R3001)的资助下,以上游泵送机械密封为研究对象,结合神经网络和多目标遗传算法进行优化设计研究,主要工作和结论如下:1.基于空化模型和动网格技术,对上游泵送机械密封微间隙内流场进行了数值计算,分析了密封端面槽型几何参数对密封性能的影响。研究表明:槽深h、螺旋角α、槽径宽径比β和槽区宽度比γ是密封性能的主要影响因素。2.采用均匀试验设计、多元回归分析和人工神经网络相结合的方法,获得了上游泵送机械密封端面形貌参数和密封性能之间的函数关系。研究表明:结合均匀试验设计和神经网络可以获得槽型几何参数与密封性能参数之间的真实函数关系;槽型几何参数间存在交互作用且对密封性能有着重要影响;槽型优化区间为6≤h≤10μm,16°≤α≤20°,0.35≤β≤0.55,0.4≤γ≤0.6。3.基于CFD数值计算和神经网络预测模型,采用多目标遗传算法建立多目标遗传优化策略,对上游泵送机械密封的性能进行优化。研究表明:(1)基于合理的试验设计,应用神经网络建立槽型几何参数和目标函数的数学模型,使得函数关系更加真实准确;(2)优化后的密封性能得到了提高,采用多元回归模型液膜刚度提高了7.5%、泄漏量减小了16.6%,采用神经网络预测模型及遗传算法液膜刚度提高了13.1%、泄漏量减小了18.9%。4.采用MSTS-IV密封试验台对机械密封的端面形貌优化结果进行试验验证。结果表明试验结果与模拟结果基本是一致的,通过遗传算法优化的结果更优。
[Abstract]:As a typical non-contact mechanical seal, the upstream pump mechanical seal has been popularized and applied because of its advantages of fluid lubrication, little or no leakage, long service life and so on. With the development of science and technology and the continuous improvement of sealing performance requirements. How to design the mechanical seal with excellent performance more effectively becomes an urgent problem to be solved. The key to the sealing performance is the modeling parameters of the end face of the mechanical seal. At present, most of the numerical calculation methods are used for single factor and single objective optimization design. Although it is effective, it is difficult to meet the high standard. This paper is based on the National Natural Science Foundation of China (Project No.: 51279067). And the Aviation Science Foundation project (project number: 201328R3001). Taking upstream pumping mechanical seal as the research object, combining neural network and multi-objective genetic algorithm to optimize design, the main work and conclusions are as follows: 1. Based on cavitation model and moving grid technology. The flow field in micro-clearance of upstream pumping mechanical seal is numerically calculated and the effect of geometric parameters of seal end groove on seal performance is analyzed. The results show that the groove depth is h and the spiral angle is 伪. The groove width to diameter ratio 尾 and groove width ratio 纬 are the main influencing factors of sealing performance. 2. The uniform test design, multivariate regression analysis and artificial neural network are adopted. The functional relationship between the surface morphology parameters and sealing performance of upstream pumping mechanical seal is obtained. Combined with uniform test design and neural network, the real function relationship between groove geometry parameters and sealing performance parameters can be obtained. There is interaction between geometric parameters of groove type and it has an important effect on sealing performance. The optimum interval of groove type is 6 鈮，

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