Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Predictive chemical kinetic model for lubricant performance: bench testings

Published

Author(s)

Stephen M. Hsu, C I. Chen

Abstract

Bench tests have been used to screen lubricants and additives for industrial fluids in machinery applications for a very long time. As technology becomes more sophisticated in terms of materials, controls, and design, the need for simple, quick, and cheap bench tests increased. At the same time, complexity of the machinery or engine makes it much more difficult to simulate effectively the lubricant performance.For a given machinery operation, oxidation and wear are the two primary performance aspects. Friction characteristics, metal catalysis, contaminants tolerance, deposit control, and oil consumption often need to be considered. Actual field test undoubtedly will sort all these issues out but they are complex, expensive, and time consuming. Can bench tests be used effectively to predict or rank lubricants?This paper describes a new approach to predict lubricant performance by combining specific bench test results and a chemical kinetic model to formulate a finite element computer program to simulate lubricant degradation as a function of time in a given machinery. The method is illustrated using a 1K engine dynamometer test.
Citation
Tribology Letters

Keywords

bench tests, chemical kinetic model, diesel engine simulation, simulation

Citation

Hsu, S. and Chen, C. (2017), Predictive chemical kinetic model for lubricant performance: bench testings, Tribology Letters (Accessed June 13, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created February 19, 2017