Optimal design of an annular thrust air bearing using parametric computational fluid dynamics model and genetic algorithms

The performance of air bearing is highly influenced by the geometrical parameters of its restrictor. This study aims to maximize the load-carrying capacity and stiffness of air bearing, and minimize its volume flow rate by optimizing the geometrical parameters of restrictor. To facilitate the calculation of air bearing performance, a parametric computational fluid dynamics model is developed. Then, it is combined with multiobjective optimization genetic algorithm to search the Pareto optimal solutions. Furthermore, as a case study, the optimal design of an annular thrust air bearing is implemented. The stiffness of air bearing is improved 38.5%, the load-carrying capacity is improved 33.9%, and the volume flow rate is declined 19.6%, which are finally validated by experiments. It proves the reliability of proposed parametric computational fluid dynamics model and genetic optimization algorithm.

[1]  Kai Cheng,et al.  CFD based investigation on influence of orifice chamber shapes for the design of aerostatic thrust bearings at ultra-high speed spindles , 2015 .

[2]  Harish Hirani,et al.  Journal bearing design using multiobjective genetic algorithm and axiomatic design approaches , 2005 .

[3]  Nenzi Wang,et al.  Application of the Genetic Algorithm to the Multi-Objective Optimization of Air Bearings , 2004 .

[4]  Suet To,et al.  Improvement on load performance of externally pressurized gas journal bearings by opening pressure-equalizing grooves , 2014 .

[5]  Nenzi Wang,et al.  Optimum design of externally pressurized air bearing using Cluster OpenMP , 2009 .

[7]  Yeau-Ren Jeng,et al.  A Modified Particle Swarm Optimization Algorithm for the Design of a Double-Pad Aerostatic Bearing With a Pocketed Orifice-Type Restrictor , 2014 .

[8]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[9]  A. Lantada,et al.  Optimising lubricated friction coefficient by surface texturing , 2013 .

[10]  Salim Mohamed Salim,et al.  Wall y + Strategy for Dealing with Wall-bounded Turbulent Flows , 2009 .

[11]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[12]  Nenzi Wang,et al.  Multi-objective optimization of air bearings using hypercube-dividing method , 2010 .

[13]  M. T. Neves,et al.  Discharge coefficient influence on the performance of aerostatic journal bearings , 2010 .

[14]  Vassili Toropov,et al.  A multiscale method for optimising surface topography in elastohydrodynamic lubrication (EHL) using metamodels , 2016 .

[15]  Frank E. Talke,et al.  Optimization of slider air bearing contours using the combined genetic algorithm-subregion approach , 2005 .

[16]  Wang Jiaying,et al.  A modified particle swarm optimization algorithm , 2005 .

[17]  John Wesley Powell.  Design of aerostatic bearings , 1970 .

[18]  Yuan Kang,et al.  Influence of the number of feeding holes on the performances of aerostatic bearings , 2010 .

[19]  Xing Hui,et al.  Bearing Lubrication Optimization for Diesel Engine Based on Orthogonal Design Method , 2011, 2011 Third International Conference on Measuring Technology and Mechatronics Automation.

[20]  Xuedong Chen,et al.  Air Vortices and Nano-Vibration of Aerostatic Bearings , 2011 .

[21]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[22]  Xuedong Chen,et al.  The effect of the recess shape on performance analysis of the gas-lubricated bearing in optical lithography , 2006 .

[23]  Han Ding,et al.  Influences of the geometrical parameters of aerostatic thrust bearing with pocketed orifice -type restrictor on its performance , 2007 .

[24]  Bin Wu,et al.  Optimal design of an aerostatic spindle based on fluid–structure interaction method and its verification , 2016 .

[25]  Homer Rahnejat,et al.  Optimisation of the piston compression ring for improved energy efficiency of high performance race engines , 2017 .

[26]  Yung-Sheng Chen,et al.  Influences of operational conditions and geometric parameters on the stiffness of aerostatic journal bearings , 2010 .

[27]  Kai Cheng,et al.  Multiphysics-based design and analysis of the high-speed aerostatic spindle with application to micro-milling , 2016 .

[28]  Weeratunge Malalasekera,et al.  An introduction to computational fluid dynamics - the finite volume method , 2007 .

[29]  Wanqun Chen,et al.  Investigation on the fluid–structure interaction effect of an aerostatic spindle and the influence of structural dimensions on its performance , 2017 .

[30]  Z-S Liu,et al.  Performance analysis of rotating externally pressurized air bearings , 2009 .

[31]  Simon Barrans,et al.  Design and test of a Pareto optimal flat pad aerostatic bearing , 2008 .

[32]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[33]  Yeau-Ren Jeng,et al.  Comparison between the effects of single-pad and double-pad aerostatic bearings with pocketed orifices on bearing stiffness , 2013 .

[34]  Lothar Thiele,et al.  Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.