Parallel Optimum Design of Foil Bearing Using Particle Swarm Optimization Method

Wang, Nenzi; Huang, Hsin‐Hua; Hsu, Chi-Rou

doi:10.1080/10402004.2012.758334

Cited by 19 publications

(15 citation statements)

References 13 publications

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“…In view of this algorithm, the individual members establish a social network and can benefit from previous experiences and discoveries of the other members. PSO algorithm is easier to implement because the swarm are updated only by updating the particle velocity and position vectors, which shows this approach has great potentials for use in the designs for air foil bearing [24], rolling element bearing [25] and magnetorheological (MR) bearing [26].…”

Section: Introductionmentioning

confidence: 99%

An optimization research on groove textures of a journal bearing using particle swarm optimization algorithm

Zhang

Liu

Zhao

2021

Mechanics & Industry

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This study aims to optimize the distributions of groove textures in a journal bearing to reduce its friction coefficient. Firstly, A lubrication model of a groove textured journal bearing is established, and the finite difference and overrelaxation iterative methods are used to numerically solve the model. Then, the friction coefficient is adopted as the fitness function and the groove lengths are optimized by particle swarm optimization (PSO) algorithm to evolve the optimal distributions. Furthermore, the effects of eccentricity ratios and rotary speeds on optimal distributions of groove textures are also discussed. The numerical results show the optimal distributions of groove textures are like trapeziums under different eccentricity ratios and rotary speeds, and the trapeziums become slenderer with increasing of eccentricity ratios. It is also found that the reductions of friction coefficients by optimal groove textures are more significant under lower eccentricity ratios. Briefly, this study may provide guidance on surface texture design to improve the tribological performance of journal bearings.

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Section: Introductionmentioning

confidence: 99%

An optimization research on groove textures of a journal bearing using particle swarm optimization algorithm

Zhang

Liu

Zhao

2021

Mechanics & Industry

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“…Finally, few examples of application of Particle Swarm Optimization [272], [273] and Ant Colony Optimization have been found in wear analysis [274]. …”

Section: Applications Of Computational Intelligence To Tribologymentioning

confidence: 99%

Applications of computational intelligence to mechanical engineering

Belfiore

Rudas

2014

2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI)

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Most of the methods and tools of Computational Intelligence (CI), such as Artificial Neural Networks, Evolutionary Computation, Fuzzy logic, Computational Swarm Intelligence and Artificial Immune Systems, have been developed for decades. However, their applications are not disseminated as exhaustively as they could be, for many reasons. In this work, a survey on some applications of Computational Intelligence to Mechanical Engineering will be presented. This review has been based on statistical analysis of a large amount of data extracted from major bibliographic databases. This analysis provides a quantitative map of the variety of CI methods and tools and their applications to some fields of Mechanical Engineering. The results will suggest to researchers some important topics of Mechanical Engineering where CI has great potential and may improve its impact

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“…It was noted that the execution time could be further deceased using more processor cores. Several recent studies [10][11][12][13][14][15][16] also show a few bearing analyses and optimum designs using OpenMP in Fortran coding. It was found that the execution time of the numerical simulations can be significantly reduced with a small amount of time and effort spent in code revision for parallel computing.…”

Section: Introductionmentioning

confidence: 99%

Fluid-film lubrication computing with many-core processors and graphics processing units

Wang

Chen

2018

Advances in Mechanical Engineering

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The advancement of modern processors with many-core and large-cache may have little computational advantages if only serial computing is employed. In this study, several parallel computing approaches, using devices with multiple or many processor cores, and graphics processing units are applied and compared to illustrate the potential applications in fluid-film lubrication study. Two Reynolds equations and an air bearing optimum design are solved using three parallel computing paradigms, OpenMP, Compute Unified Device Architecture, and OpenACC, on standalone shared-memory computers. The newly developed processors with many-integrated-core are also using OpenMP to release the computing potential. The results show that the OpenACC computing can have a better performance than the OpenMP computing for the discretized Reynolds equation with a large gridwork. This is mainly due to larger sizes of available cache in the tested graphics processing units. The bearing design can benefit most when the system with many-integrated-core processor is being used. This is due to the many-integrated-core system can perform computation in the optimization-algorithm-level and using the many processor cores effectively. A proper combination of parallel computing devices and programming models can complement efficient numerical methods or optimization algorithms to accelerate many tribological simulations or engineering designs.

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Parallel Optimum Design of Foil Bearing Using Particle Swarm Optimization Method

Cited by 19 publications

References 13 publications

An optimization research on groove textures of a journal bearing using particle swarm optimization algorithm

An optimization research on groove textures of a journal bearing using particle swarm optimization algorithm

Applications of computational intelligence to mechanical engineering

Fluid-film lubrication computing with many-core processors and graphics processing units

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