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Structural and Multidisciplinary Optimization

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01.01.2023 | Research Paper

Experimental verification: a multi-objective optimization method for inversion technology of hydrodynamic journal bearings

verfasst von: Jingjun Zhang, Liming Lu, Zhiyi Zheng, Haiyang Tong, Xuanjun Huang

Erschienen in: Structural and Multidisciplinary Optimization | Ausgabe 1/2023

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Abstract

Aiming at the key design variables of journal bearings, a novel optimization scheme is proposed to minimize oil leakage and power loss. For the first time, the inversion technology is introduced into the multi-objective optimization genetic algorithm under thermohydrodynamics. Using the hybrid optimization method (sequential quadratic programming and multi-objective optimization genetic algorithm) and the pareto optimal frontier method, the journal bearing model under the oil supply condition of oil groove (Model A) and oil hole (Model B) is optimized. More importantly, the oil leakage (Q_L) formula is exhaustively deduced, and good prediction results are obtained by simulating the data in literature. The optimization test results show that compared with the maximum errors (13% and 25%) of the power loss and leakage flow prediction results in literature, the maximum errors of this prediction model are 8% and 14%, respectively. In addition, compared with hybrid optimization method, the pareto optimal frontier has better advantages under inversion technology. Both methods can give good prediction results. The accuracy of this model is proved by comparing experimental data in the literature.

Vorheriger Artikel Variable functioning and its application to large scale steel frame design optimization

Nächster Artikel Structural topology optimization considering both manufacturability and manufacturing uncertainties

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Titel: Experimental verification: a multi-objective optimization method for inversion technology of hydrodynamic journal bearings
verfasst von: Jingjun Zhang
Liming Lu
Zhiyi Zheng
Haiyang Tong
Xuanjun Huang
Publikationsdatum: 01.01.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: Structural and Multidisciplinary Optimization / Ausgabe 1/2023
Print ISSN: 1615-147X
Elektronische ISSN: 1615-1488
DOI: https://doi.org/10.1007/s00158-022-03470-z

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