Journal Bearing Optimization Using Nonsorted Genetic Algorithm and Artificial Bee Colony Algorithm

Gorasso, Luca; Wang, L.

doi:10.1155/2014/213548

Cited by 15 publications

(6 citation statements)

References 25 publications

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“…Furthermore, it was also verified that the training algorithm and the sample size affect the prediction accuracy significantly. Gorasso and Wang [30] proposed a journal bearing optimization process, in which the performance functions were an ANN trained with a dataset obtained from numerical solutions of the Reynolds equation and Computational Fluid Dynamics (CFD) simulations. The optimization strategies adopted for the calculations were non-sorted genetic algorithm and artificial bee colony algorithm.…”

Section: Lubrication and Fluid Film Formationmentioning

confidence: 99%

The Use of Artificial Intelligence in Tribology—A Perspective

et al. 2020

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Artificial intelligence and, in particular, machine learning methods have gained notable attention in the tribological community due to their ability to predict tribologically relevant parameters such as, for instance, the coefficient of friction or the oil film thickness. This perspective aims at highlighting some of the recent advances achieved by implementing artificial intelligence, specifically artificial neutral networks, towards tribological research. The presentation and discussion of successful case studies using these approaches in a tribological context clearly demonstrates their ability to accurately and efficiently predict these tribological characteristics. Regarding future research directions and trends, we emphasis on the extended use of artificial intelligence and machine learning concepts in the field of tribology including the characterization of the resulting surface topography and the design of lubricated systems.

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Section: Lubrication and Fluid Film Formationmentioning

confidence: 99%

The Use of Artificial Intelligence in Tribology—A Perspective

et al. 2020

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“…In order to solve this problem, the mutation rate in the genetic algorithm can be used to improve the basic ACA. 23 Compared with the basic ACA, the genetic ACA can perform global optimization without stagnation and has strong robustness. Through the research of genetic algorithm and ACA, Xiong et al 24 found that the two algorithms generally show the speed-time curve in Figure 4 on the solution problem.…”

Section: Genetic Acamentioning

confidence: 99%

A genetic ant colony algorithm-based driving cycle generation approach for testing driving range of battery electric vehicle

Shi

Liu

Guan

et al. 2020

Advances in Mechanical Engineering

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In this article, an approach of driving cycle generation for battery electric vehicle is proposed based on genetic ant colony algorithm. The real-world traffic information is utilized to build up a local driving cycle database, in which definitions of the short trip and kinematic characteristic parameters are discussed to describe the driving cycle. A method of principal component analysis is taken as a preprocessor for reducing the dimension of driving cycle data. And then, genetic ant colony algorithm is used to classify the type of short trips and generate the driving cycle. The experimental results on board indicate that, compared with the Economic Commission for Europe driving cycle, the error of driving range and characteristic parameters tested by genetic ant colony driving cycle are reduced by 18.1% and 18.3%, respectively. Therefore, genetic ant colony driving cycle is a good candidate to test driving range of battery electric vehicle.

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“…The basic trend in rotor dynamics is parametric optimization of rotor attributes, such as geometry of bearings and seals, masses and supports distribution by shaft length, etc, respect to forced oscillations caused by the centrifugal forces (unbalance), that problems a big number of papers lately are dedicated [13][14][15][16][17][18][19][20]. The main controversial problem of parametric optimization is the determination of the quality criteria for a specific rotor system.…”

Section: Energy Efficiency Of Rotor Systemmentioning

confidence: 99%

Estimation of Energy Efficiency of Oscillations of Rotors on Radial Fluid Film Bearings

Majorov

Савин

Babin

2018

Proceedings of the 4th International Conference on Industrial Engineering

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The following parameters have the most influence on the dynamics of a high-speed turbomachine's rotor system: mass and stiffness distribution of a rotor; damping properties of the materials of rotor's elements; location of bearings, sealings, and dampers; type, design, and operational conditions of bearings, sealings, and dampers; mass and inertia characteristics of machine's housing; and operational conditions of a turbomachine itself. It should be noted that the presence of fluid film bearing complicates the dynamic analysis. It is thus a quite urgent task to search for some rational parameters when a rotor system is under design and preliminary calculation, which in many cases leads to the problems of an optimal parametric design. One of the main yet controversial problems of parametric design in the field of rotor system dynamics is the determination of quality indicators for a particular case of a rotor system. The present paper proposed a criterion based on the efficiency of rotor system's input energy. The analysis of a number of parameters', e.g., values of constant external forces (rotor's mass), varying external forces (imbalance), and types of bearings, influence on this criterion has also been carried out.

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Journal Bearing Optimization Using Nonsorted Genetic Algorithm and Artificial Bee Colony Algorithm

Cited by 15 publications

References 25 publications

The Use of Artificial Intelligence in Tribology—A Perspective

The Use of Artificial Intelligence in Tribology—A Perspective

A genetic ant colony algorithm-based driving cycle generation approach for testing driving range of battery electric vehicle

Estimation of Energy Efficiency of Oscillations of Rotors on Radial Fluid Film Bearings

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