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

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 optim...

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Vydáno v:Structural and multidisciplinary optimization Ročník 66; číslo 1; s. 14
Hlavní autoři: Zhang, Jingjun, Lu, Liming, Zheng, Zhiyi, Tong, Haiyang, Huang, Xuanjun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2023
Springer Nature B.V
Témata:
Computational Mathematics and Numerical Analysis
Engineering
Engineering Design
Errors
Genetic algorithms
Grooves
Journal bearings
Leakage
Model accuracy
Multiple objective analysis
Optimization
Pareto optimization
Pareto optimum
Prediction models
Quadratic programming
Research Paper
Theoretical and Applied Mechanics
Thermohydrodynamics
Thermohydrodynamics
Optimization methods
Inversion technology
Journal bearing
ISSN:1615-147X, 1615-1488
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-022-03470-z