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Research Article | Open Access

Wear protection assessment of ultralow viscosity lubricants in high-power-density engines: A novel wear prediction algorithm

Javier BLANCO-RODRÍGUEZ1( )Jacobo PORTEIRO1José A. LÓPEZ-CAMPOS1Martí CORTADA-GARCÍA2Silvia FERNÁNDEZ-CASTEJÓN2
CINTECX, Universidade de Vigo, Lagoas-Marcosende s/n, Vigo 36310, Spain
Repsol SA, Madrid, Spain
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Abstract

Durability and reliability have been studied for decades through intensive trial-error experimentation. However, there are numerous fields of application where the costs associated with this approach are not acceptable. In lubricated machines with severe dynamic loads, such as high-power-density engines, simulation tools offer clear advantages over intensive testing. Prototypes and multiple scenarios can be cost-effectively simulated to assess different lubricants and engine configurations. The work presented here details the study of wear based on a validated elastohydrodynamic (EHD) simulation model of the connecting rod journal bearing. This model accounts for elastic deformation through a connecting rod finite element model (FEM). In addition, multiple lubricant rheological and tribological dependences, determined by specific experimental tests, are applied in the model through their interaction with the simulation software. Correspondingly, a novel wear algorithm is proposed to predict wear depth over time evolution along a proposed wear cycle based on the typical working ranges of high-performance engines. A final assessment is presented to compare 4 different ultralow-viscosity lubricants in their protective performance under severe conditions. The results show the evolution of the wear load and wear depth over the wear cycle. This evaluation is key to describing a lubricant selection procedure for high-power-density engines.

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Friction
Pages 1785-1800

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Cite this article:
BLANCO-RODRÍGUEZ J, PORTEIRO J, LÓPEZ-CAMPOS JA, et al. Wear protection assessment of ultralow viscosity lubricants in high-power-density engines: A novel wear prediction algorithm. Friction, 2024, 12(8): 1785-1800. https://doi.org/10.1007/s40544-023-0854-3

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Received: 25 July 2023
Revised: 18 October 2023
Accepted: 28 November 2023
Published: 25 May 2024
© The author(s) 2023.

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