摘要
采用基于N-S方程的CFD方法模拟了圆凹坑形织构深度和面积率与油膜附加承载力的关系,同时考察表面张力对油膜承载力的影响。通过神经网络非线性映射功能,拟合油膜附加承载力和油膜自身承载力计算结果,再利用粒子群优化算法优化织构深度和面积率并加以试验验证。结果表明：使油膜附加承载力达到最大时,最优织构深度与油膜厚度相关,其比值（织构深度/油膜厚度）约为1.0,最优面积率约为45%。考虑表面张力时,使油膜总承载力达到最大时的最优织构深度和面积率分别为2.5~7.5μm和45%。对比两部分优化结果可知,是否考虑表面张力不影响最优面积率的确定,但影响最优织构深度的确定。从织构深度和面积率的优化结果发现,不同承载力作用下选择不同织构参数,可取得较小的摩擦因数,并通过试验验证,优化结果合理、正确。
The relationship between texture depth and texture area ratio with the additional load-carrying capacity of oil film considered at different oil film thickness is simulated by using the computational fluid dynamics method（CFD） based on N-S equation. Meantime, the influence of surface tension on the load-carrying capacity of oil film is investigated; the additional load-carrying capacity of oil film from surface texture and the own load-carrying capacity of oil film from surface tension are fitted by using the nonlinear mapping function of neural network. The texture depth and the texture area ratio are optimized with optimization algorithm of particle swarm and tested with experiments. The results show that the texture depth is related to the oil film thickness for round dimple texture when the additional load-carrying capacity of oil film got the maximum. The ratio between them is around 1.0 and the optimal area ratio is 45%. With the influence of surface tension is considered, when the total load-carrying capacity for oil film get the maximum, the optimal texture depth and area ratio are 2.5-7.5 μm and 45%, respectively. The comparison of those results show if take the surface tension into consideration or not will not affect the determination of optimal area ratio, but affect the determination of the optimal texture depth. According to the optimal results of texture depth and area ratio, the lower friction coefficient can be obtained by choosing geometric parameters of surface texture at different load-carrying capacity. And through the experiments, good agreement between simulation results and test data is achieved.
作者
陈平
李俊玲
邵天敏
项欣
刘光磊
CHEN Ping LI Junling SHAO Tianmin XIANG Xin LIU Guanglei(School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083 State Key Lab. of Tribology, Tsinghua University, Beijing 100084)
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2016年第19期123-131,共9页
Journal of Mechanical Engineering
基金
中央高校基本科研业务费专项资金资助项目(FRF-BR-15-037A)
关键词
表面织构
表面张力
计算流体动力学(CFD)
神经网络
承载力
摩擦因数
surface texture
surface tension
computational fluid dynamics（CFD）
neural network
load-carrying capacity
friction coefficient
