A Study on the Feature of Several Types of Floating Liner Devices for Piston Friction Measurement
Published April 2, 2019 by SAE International in United States
Downloadable datasets for this paper availableAnnotation of this paper is available
The friction reduction of a piston/piston-ring assembly is effective for fuel economy of an engine, and a friction measurement method is required for developing low friction pistons, piston-rings and lubricants. Most suitable method for friction measurement for piston assemblies is “floating liner method”. It has load sensors between a floating cylinder liner and cylinder block, and the sensors can detect friction force acting on the liner. Many apparatuses using floating liner method are developed. They are roughly divided to two categories. In one of them, floating liner is supported by load-washers which axis is set parallel to the center line of the cylinder liner. In another type, floating liner is supported by three-component force sensors installed on the side face of the cylinder. In this paper, five types of floating liner devices were compared. The natural frequency, an important design factor because it affects measurement accuracy and the highest engine speed, was investigated for each device. It was found that natural frequency was strongly affected by sensor type and its mounting condition. A simple model was shown for estimating the natural frequency. Those results showed that setting appropriate natural frequency of a floating liner device is essential for ensuring measurement accuracy. The transient response of the devices after the firing top dead center was calculated using the model. It was found that the floating liner devices were not suitable for quantitative measurement of friction peak at F.TDC. However, the devices were effective for investigating the trend of the friction peak.
CitationNagano, Y., Ito, A., Okamoto, D., and Yamasaka, K., "A Study on the Feature of Several Types of Floating Liner Devices for Piston Friction Measurement," SAE Technical Paper 2019-01-0177, 2019, https://doi.org/10.4271/2019-01-0177.
Data Sets - Support Documents
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