Multi-Objective Optimization of Active Hybrid Fluid Film Bearings Using Heuristic Algorithms

Purpose of research. The design of sliding bearings, especially for heavily loaded rotary machines, is a laborious task. The implementation of control systems for the movement parameters of the rotor further increases the complexity of a design procedure. The study shows a developed approach to the optimal design of active rotor bearings using heuristic optimization algorithms. The approach allows to obtain a set of optimal Pareto solutions and determine the only configuration of the reference node that best meets the given criteria.

Methods. The problem of optimal parametric synthesis of an active fluid friction bearing was solved using a numerical model coupled with the model of rotor movement in the support. For the given design problem, objective functions were formulated, design variables were determined, and the necessary restrictions were imposed. Using multicriteria versions of the genetic algorithm and the particle swarm algorithm, procedures for the optimal synthesis of reference nodes were carried out. The solutions obtained by different methods are compared and analyzed based on the results of model tests.

Results. As part of the study, algorithmic and software tools were developed for solving problems of optimal parametric synthesis of active hybrid fluid friction bearings. The applied objective functions are conflicting, so the primary result of the solution is a 3D Pareto front. The tested heuristic algorithms showed qualitatively similar solutions, but the genetic algorithm covers a larger range of them. On the whole, the final decisions meet the criteria, but the methods for making final decisions require additional elaboration.

Conclusion. The study presents an approach to the automated design of sliding bearings, which allows you to simultaneously take into account the tribological, dynamic aspects of the behavior of the rotary bearing system, as well as ensure readiness for the use of control systems in bearing nodes. The tested heuristic algorithms give comparable solutions to the optimization problem in comparable time as well. Further improvement of the method of parametric synthesis of such supports should be carried out in the direction of decision-making algorithms, refinement of objective functions, as well as acceleration of the applied calculation models.

1. Dekker M. Inc.: Handbook of turbomachinery. New York, 1995.

2. Chernavskiy S. А. Podshipniki skoljeniya [Sliding bearings]. Мoscow, Mashinostroenie Publ., 1963.

3. Hummel Ch. Kritische Drehzahlen als Folge der Nachgiebigkeit des Schmiermittels im Lager. Forschungsarbeiten VDI, 1926.

4. Yamamoto T., Ishida Y. Linear and nonlinear rotordynamics. A modern treatment with applications. John Willey&Sons, New York, 2001.

5. Chen W. J., Gunter E. J. Introduction to Dynamics of rotor-bearing systems. TRAFFORD PUB, 2005.

6. Singh R., Singh C., Amit H., Amit S. Defect-free optimal synthesis of crank-rocker linkage using nature-inspired optimization algorithms. Mechanism and Machine Theory, 2017, no. 116, pp. 105-122.

7. Panda S., Nanda N., Mishra D. Comparative study on optimum design of rolling element bearing. Tribology International, 2015, no. 92, pp. 595-604.

8. Lu K., Jin Yu., Huang P., Zhang F., Zhang H., Fu Ch., Chen Yu. The applications of POD method in dual rotor-bearing systems with coupling misalignment. Mechanical Systems and Signal Processing, 2021, no. 150, 107236 p.

9. Onunka C., Gobler H., Bright G. A stability optimization model for shaft rotorbearing systems. African Journal of Science, Technology, Innovation and Development. 2016, no. 8, pp. 1-12.

10. Lu K., Jin Yu., Chen Yu., Yang Y., Hou L., Zhang Z., Li Z., Fu Ch. Review for order reduction based on proper orthogonal decomposition and outlooks of applications in mechanical systems. Mechanical Systems and Signal Processing, 2019, no. 123, pp. 264-297.

11. Li S., Babin A., Shutin D., Kazakov Yu., Liu Y., Chen Z., Savin L. Active Hybrid Journal Bearings with Lubrication Control: Towards Machine Learning. Tribology International, 2022, no. 175, 107805 p.

12. Saruhan H. Optimum design of rotor-bearing system stability performance comparing an evolutionary algorithm versus a conventional method. International Journal of Mechanical Sciences, 2006, no. 48, pp. 1341-1351.

13. Zhang J., Talke F. E. Optimization of slider air bearing contours using the combined genetic algorithm-subregion approach. Tribology International - TRIBOL INT, 2005, no. 38. P. 566-573.

14. Güllüce H., Özdemir K. Design and operational condition optimization of a rotary regenerative heat exchanger. Applied Thermal Engineering, 2020, no. 177, 115341 p.

15. Li F., Shen W., Cai X., Gao L., Wang G. A fast surrogate-assisted particle swarm optimization algorithm for computationally expensive problems. Applied Soft Computing, 2020, no. 92, 106303 p.

16. Rodrigues D., Albuquerque V., Papa J. A Multi-Objective Artificial Butterfly Optimization Approach for Feature Selection. Applied Soft Computing, 2020, № 94.

17. Wen X. Modeling and performance evaluation of wind turbine based on ant colony optimization-extreme learning machine. Applied Soft Computing, 2020, no. 94, 106476 p.

18. Javed M., Ma T., Jurasz J., Ahmed S., Mikulik J. Performance comparison of heuristic algorithms for optimization of hybrid off-grid renewable energy systems. Energy, 2020, no. 210, 118599 p.

19. Jensen K., Santos I. Design of actively-controlled oil lubrication to reduce rotorbearing- foundation coupled vibrations - theory & experiment. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 2022, no. 236.

20. Encyclopedia of Tribology. Encycl. Tribol. 2013.

21. Savin L., Polyakov R., Shutin D., Babin A. Peculiarities of reactions control for rotor positioning in an active journal hybrid bearing. International Journal of Mechanics, 2016, no. 10, pp. 62-67.

22. Dmochowski W. M., Dadouche A., Fillon M. Finite Difference Method for Fluid- Film Bearings. Encyclopedia of Tribology, 2013, pp. 1137–1143.

23. San Andres L. Notes 14. Experimental identification of bearing force coefficients. 2009. Available at: https://oaktrust.library.tamu.edu/handle/1969.1/93254 (accessed 09.01.2022).

24. Shutin D., Polyakov R. Adaptive nonlinear controller of rotor position in active hybrid bearings, 2016, pp. 1-6.

25. Mattox D., Wang Q., Chung Y. Encyclopedia of Tribology. Encycl. Tribol, 2013, pp. 2717-2726.

26. Aizerman М. А., Aleskerov F. Т. Vibor variantov. Osnovi teorii [Choise of options. Fundamentals of theory]. Мoscow, Nauka Publ., 1990.

27. Bellman R. E., Zadeh L. A. Decision making in a fuzzy environment. Management Science, 1970, no. 4, pp. 141–164.

28. Hwang C. L., Yoon K. Multiple Attribute Decision Making: Methods and Applications. Berlin, Heidelberg; New York, Springer-Verlag, 1981.

29. Rubiano J., Nucamendi S., Cordero F., Cordero A., Rodríguez M. Alejandro an improved LINMAP for multicriteria decision: designing customized incentive portfolios in an organization. Operational Research, 2022, no. 22.