Analysis of rolling bearing power loss models for twin screw oil injected compressor
Abdan, S., Stosic, N., Kovacevic, A. ORCID: 0000-0002-8732-2242 , Smith, I. K. ORCID: 0000-0003-1524-9880 & Asati, N. (2019). Analysis of rolling bearing power loss models for twin screw oil injected compressor. In: IOP Conference Series: Materials Science and Engineering. doi: 10.1088/1757-899X/604/1/012013
Abstract
The mechanical losses inside a screw compressor limit the performance of the compressor in terms of efficiency. These losses arise due to relative motion between elements inside the screw compressor. The estimation of mechanical losses predicted in the literature is around 10-15% of the total shaft power. One of the elements which contribute significantly to these losses is rolling element bearings. There are numerous mathematical models available which predict power losses in the rolling bearings. The objective of this paper is to study different models to predict power loss for rolling bearings and to predict the power losses for the bearings used for oil injected, twin screw compressor. A comparison between different power loss models for different operating conditions of compressor is also presented in this paper and results of analysis are compared with available experimental observations. The analysis helps to determine suitable power loss model for different operating conditions and more realistic predictions of the power losses. This allows designers for more accurate estimation of the performance of screw compressors.
Publication Type: | Conference or Workshop Item (UNSPECIFIED) |
---|---|
Additional Information: | Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distributionof this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.Published under licence by IOP Publishing Ltd |
Subjects: | T Technology > TJ Mechanical engineering and machinery |
Departments: | School of Science & Technology > Engineering |
Available under License Creative Commons: Attribution 3.0.
Download (355kB) | Preview
Export
Downloads
Downloads per month over past year