Journal of Engineering Sciences

Author(s): Verbovyi A.¹*, Khomenko V.¹, Neamtu C.², Pavlenko V.¹, Cherednyk M.¹, Vashyst B.¹, Pavlenko I.¹

Affiliation(s):
¹ Sumy State University, 2 Rymskogo-Korsakova St., 40007 Sumy, Ukraine;
² Technical University of Cluj-Napoca, 28 Memorandumului St., 400114 Cluj-Napoca, Romania

^*Corresponding Author’s Address: [email protected]

Issue: Volume 8, Issue 2 (2021)

Dates:
Submitted: August 12, 2021
Accepted for publication: December 3, 2021
Available online: December 9, 2021

Citation:
Verbovyi A., Khomenko V., Neamtu C., Pavlenko V., Cherednyk M., Vashyst B., Pavlenko I. (2021). Parameter identification of nonlinear bearing stiffness for turbopump units of liquid rocket engines considering initial gaps and axial preloading. Journal of Engineering Sciences, Vol. 8(2), pp. D8-D11, doi: 10.21272/jes.2021.8(2).d2

DOI: 10.21272/jes.2021.8(2).d2

Research Area:  MECHANICAL ENGINEERING: Dynamics and Strength of Machines

Abstract. This article is devoted to developing a mathematical model of nonlinear bearing supports for turbopump units of liquid rocket engines considering initial gaps and axial preloading. In addition to the radial stiffness of the bearing support, this model also considers the stiffness of the bearing cage, the rotational speed of the rotor, axial preloading of the rotor (due to which the inner cage shifts relative to the outer, changing the radial stiffness of the support), as well as radial gaps between contact elements of the bearings. This model makes it possible to calculate the stiffness of the bearing supports more accurately. The proposed model is realized using both the linear regression procedure and artificial neural networks. The model’s reliability is substantiated by the relatively small discrepancy of the obtained evaluation results with the experimental data. As a result, this model will allow determining the critical frequencies of the rotor with greater accuracy. The results have been implemented within the experience of designing turbopump units for State Company “Yuzhnoye Design Office”.

Keywords: bearing support, axial force, radial gap, regression analysis, artificial neural networks.

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