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

(51 reference statements)

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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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“…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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“…To improve the results of optimization, there is a common practice to combine GA with other methods and techniques, such as artificial bee colony algorithm. 48 Abburi and Dixit 49 have combined GA and linear programming for multi-pass cutting; the goal of this multi-objective optimization was to minimize production time, which provided near-optimal solutions in the form of the Pareto front. To choose the best optimal solution among Pareto optimal solutions, linear programming has been applied.…”

Section: Literature Reviewmentioning

confidence: 99%

Production process parameter optimization with a new model based on a genetic algorithm and ABC classification method

Erić

Stefanović

Đorđević

et al. 2016

Advances in Mechanical Engineering

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The difference between the production cost and selling price of the products may be viewed as a criterion that determines an organization's competitiveness and market success. In such circumstances, it is necessary to impact these criteria in order to maximize this difference. The selling products' price, in modern market conditions, is a category which may not be significantly affected. So organizations have one option, which is the production cost reduction. This is the motive for business organizations and the imperative of each organization. The key parameters that influence the costs of production and therefore influence the competitiveness of organizations are the parameters of production machines and processes used to create products. To define optimal parameter values for production machines and processes that will reduce production costs and increase competitiveness of production organizations, the authors have developed a new mathematical model. The model is based on application of the ABC classification method to classify production line processes based on their costs and an application of a genetic algorithm to find the optimal values of production machine parameters used in these processes. It has been applied in three different modern production line processes; the costs obtained by the model application have been compared with the real production costs.

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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

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

Production process parameter optimization with a new model based on a genetic algorithm and ABC classification method

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