Study on Static Characteristics Prediction of Porous Hydrostatic Bearing

YAN Ruzhong; SHI Junwei; MA Xiaojian; AN Xingyu

doi:10.13433/j.cnki.1003-8728.20220214

Volume 43 Issue 3

Mar. 2024

Turn off MathJax

Article Contents

Article Navigation > Mechanical Science and Technology for Aerospace Engineering > 2024 > 43(3): 490-496

YAN Ruzhong, SHI Junwei, MA Xiaojian, AN Xingyu. Study on Static Characteristics Prediction of Porous Hydrostatic Bearing[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(3): 490-496. doi: 10.13433/j.cnki.1003-8728.20220214

Citation:

YAN Ruzhong, SHI Junwei, MA Xiaojian, AN Xingyu. Study on Static Characteristics Prediction of Porous Hydrostatic Bearing[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(3): 490-496. doi: 10.13433/j.cnki.1003-8728.20220214

Citation:

PDF( 1942 KB)

Study on Static Characteristics Prediction of Porous Hydrostatic Bearing

doi: 10.13433/j.cnki.1003-8728.20220214

YAN Ruzhong^{1, 2
,},
SHI Junwei^{1, 2},
MA Xiaojian^{1, 2},
AN Xingyu^{1, 2}

1.
College of Mechanical Engineering, Donghua University, Shanghai 201620, China
2.
Engineering Research Center of Advanced Textile Machinery Ministry of Education, Shanghai 201620, China

Received Date: 2021-12-09
Publish Date: 2024-03-25

Abstract

Abstract

In the design of porous hydrostatic bearings, bearing's design parameter is one of the key factors affecting its static and dynamic characteristics. Normally, several repetitions of modeling and simulation are required to obtain suitable bearing design parameters, and due to the complex bearing structure makes modeling difficult and simulation time long, which seriously affects the efficiency of bearing design. In this paper, a bearing static characteristic prediction model based on genetic algorithm (GA) optimized BP (Back Propagation) neural network is constructed, Latin hypercube sampling method is used to sample the bearing parameter design space, and perform Fluent fluid simulation. The data is used for the training and testing of the GA-BP neural network model to realize the prediction of static characteristics of porous hydrostatic bearing under any design parameters in the design space. The research results show that the trained GA-BP neural network model can accurately predict the static characteristics of porous hydrostatic bearings with a prediction accuracy of over 95%, which is great significance for the rapid design and parameter optimization of porous hydrostatic bearings.
- porous hydrostatic bearing,
- static characteristic,
- GA-BP neural network,
- latin hypercube sampling,
- prediction

FullText(HTML)

References(15)

References

[1]	伍奕桦. 多孔质可倾瓦轴承支承转子系统动力学分析及其实验研究[D]. 长沙: 湖南大学, 2019. WU Y H. Theoretical analysis and experimental investigation on rotordynamic performance of a rigid rotor supported on porous tiliting pad bearings[D]. Changsha: Hunan University, 2019. (in Chinese)
[2]	段柳颖, 张晓峰, 刘玚, 等. 多孔质气体静压径向轴承倾斜状态特性研究[J]. 润滑与密封, 2021, 46(6): 10-14. DUAN L Y, ZHANG X F, LIU Y, et al. Inclination performance study of porous aerostatic journal bearing[J]. Lubrication Engineering, 2021, 46(6): 10-14. (in Chinese)
[3]	QIN P, SHEN Y, ZHU J, et al. Dynamic analysis of hydrodynamic bearing–rotor system based on neural network[J]. International Journal of Engineering Science, 2005, 43(5-6): 520-531. doi: 10.1016/j.ijengsci.2003.10.008
[4]	ZHU X X, LUO X Z, ZHAO J H, et al. Research on deep feature learning and condition recognition method for bearing vibration[J]. Applied Acoustics, 2020, 168: 107435. doi: 10.1016/j.apacoust.2020.107435
[5]	TANG J, HUANG X D, FANG C G. BP neural network in prediction of the constant-current hydrostatic bearing static stiffness[J]. Advanced Materials Research, 2011, 199-200: 271-274. doi: 10.4028/www.scientific.net/AMR.199-200.271
[6]	李寅平, 陈国定, 李盼, 等. 基于GA-LM-BP算法的圆柱轴承动特性预测[J]. 机械制造, 2013, 51(1): 24-27. doi: 10.3969/j.issn.1000-4998.2013.01.009 LI Y P, CHEN G D, LI P, et al. Prediction of dynamic characteristics of cylindrical bearing based on GA-LM-BP algorithm[J]. Machinery, 2013, 51(1): 24-27. (in Chinese) doi: 10.3969/j.issn.1000-4998.2013.01.009
[7]	石莹, 王学智, 刘金营, 等. 基于Fluent-BP神经网络的液体动静压轴承热特性分析[J]. 润滑与密封, 2016, 41(1): 37-42. doi: 10.3969/j.issn.0254-0150.2016.01.007 SHI Y, WANG X Z, LIU J Y, et al. Thermal characteristics analysis of liquid hybrid bearing based on Fluent-BP neural networks[J]. Lubrication Engineering, 2016, 41(1): 37-42. (in Chinese) doi: 10.3969/j.issn.0254-0150.2016.01.007
[8]	林桂强, 毕超, 江波, 等. 基于BP神经网络的套筒式串联推力轴承组均载性能及承载能力细化研究[J]. 工程塑料应用, 2012, 40(7): 50-53. doi: 10.3969/j.issn.1001-3539.2012.07.012 LIN G Q, BI C, JIANG B, et al. Detailed research on load uniformity and load-bearing capacity of sleeve type tandem thrust bearings based on BP neural network[J]. Engineering Plastics Application, 2012, 40(7): 50-53. (in Chinese) doi: 10.3969/j.issn.1001-3539.2012.07.012
[9]	顾延东. 多孔质气体径向轴承静动特性研究及优化设计方法[D]. 镇江: 江苏大学, 2019. GU Y D. Investigation on the static and dynamic characteristics and optimization design of aerostatic radial bearing with porous restrictor[D]. Zhenjiang: Jiangsu University, 2019. (in Chinese)
[10]	顾延东, MARTIN B, ARTUR S, 等. 多孔质气体静压轴承研究现状及发展趋势[J]. 排灌机械工程学报, 2021, 39(8): 818-825. GU Y D, MARTIN B, ARTUR S, et al. Aerostatic bearing with porous restrictor: research status and future perspectives[J]. Journal of Drainage and Irrigation Machinery Engineering, 2021, 39(8): 818-825. (in Chinese)
[11]	雷明远. 基于遗传算法与BP神经网络的RV减速器结构优化设计[D]. 兰州: 兰州理工大学, 2021. LEI M Y. Structure optimization design of RV reducer based on genetic algorithm and BP neural network[D]. Lanzhou: Lanzhou University of Technology, 2021. (in Chinese)
[12]	马高, 刘康. 基于BP神经网络CFRP约束混凝土抗压强度预测[J]. 湖南大学学报(自然科学版), 2021, 48(9): 88-97. doi: 10.16339/j.cnki.hdxbzkb.2021.09.010 MA G, LIU K. Prediction of compressive strength of CFRP-confined concrete columns based on BP neural network[J]. Journal of Hunan University (Natural Sciences), 2021, 48(9): 88-97. (in Chinese) doi: 10.16339/j.cnki.hdxbzkb.2021.09.010
[13]	胡新荣, 刘嘉文, 刘军平, 等. 基于改进GA-BP-MC神经网络的人体三维尺寸预测[J]. 计算机工程与科学, 2021, 43(8): 1443-1453. doi: 10.3969/j.issn.1007-130X.2021.08.013 HU X R, LIU J W, LIU J P, et al. 3D human dimension prediction based on improved GA-BP-MC neural network[J]. Computer Engineering and Science, 2021, 43(8): 1443-1453. (in Chinese) doi: 10.3969/j.issn.1007-130X.2021.08.013
[14]	李静, 姜峰, 牛彩云. 实验设计和神经网络法对柴油机性能的优化研究[J]. 机械设计与制造, 2020(6): 100-104. doi: 10.3969/j.issn.1001-3997.2020.06.025 LI J, JIANG F, NIU C Y. Research on performance optimization of diesel engine based on DOE and ANN method[J]. Machinery Design & Manufacture, 2020(6): 100-104. (in Chinese) doi: 10.3969/j.issn.1001-3997.2020.06.025
[15]	CHEN H, WANG Y, ZUO M S, et al. A new prediction model of CO₂ diffusion coefficient in crude oil under reservoir conditions based on BP neural network[J]. Energy, 2022, 239: 122286. doi: 10.1016/j.energy.2021.122286