Compressors and Their Systems: Energy, Flow and Mechanical Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 3373

Special Issue Editor


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Guest Editor
School of Chemical Engineering and Technology, Xi’an Jiaotong University, Xi’an 710049, China
Interests: compressor and refrigeration technology

Special Issue Information

Dear Colleagues,

Compressors play important roles in the fields of refrigeration, heat pump, energy storage, energy recovery, and so on. Flow and energy conversion behaviors in a compressor and its system are very complex as well as closely related to the efficiency and reliability of the system. The mechanical condition of the compressor dominates the vibration and acoustic performance of the system.

In this Special Issue, we invite submissions exploring cutting-edge research and recent advances in the fields of compressors and their systems.

Dr. Xiaoling Yu
Guest Editor

Manuscript Submission Information

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Keywords

  • compressors and their systems
  • energy conversion in compressors and their systems
  • mechanical problems of compressors
  • flow behavior in compressors and their systems

Published Papers (2 papers)

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Research

16 pages, 11631 KiB  
Article
Effects of Surface Roughness on Windage Loss and Flow Characteristics in Shaft-Type Gap with Critical CO2
by Lehao Hu, Qinghua Deng, Zhouyang Liu, Jun Li and Zhenping Feng
Appl. Sci. 2022, 12(24), 12631; https://doi.org/10.3390/app122412631 - 9 Dec 2022
Cited by 3 | Viewed by 1110
Abstract
To investigate the effects of surface roughness on windage loss and flow characteristics in a shaft-type gap, the skin friction coefficient (Cf) and flow versus Reynolds number (Re) at different surface roughness (Ra) and radius ratio [...] Read more.
To investigate the effects of surface roughness on windage loss and flow characteristics in a shaft-type gap, the skin friction coefficient (Cf) and flow versus Reynolds number (Re) at different surface roughness (Ra) and radius ratio (η) values were investigated. The results showed that Cf decreased as Re increased, and the rate of decrease was constant at low Re but reduced at high Re. The growing relative deviations between the coefficients of smooth and rough walls with Ra indicated that Cf was influenced by rough walls when Re > 102. Moreover, Cf and the variation rate increased with η and were easily influenced by Ra for larger η at low Re, since the interaction between wall roughness and fluid influences windage loss. In addition, the flow field implied the flow had transitioned to Taylor-Couette flow, Taylor vortexes occurred when Re > 102, and the number of vortexes increased with increasing Ra and were reduced with increasing η. The velocity was divided into three regions and the pressure rose from the rotational to stationary walls, but decreased with growing η as a whole. This paper improves the research exploring windage loss and will help design smaller supercritical CO2 power devices. Full article
(This article belongs to the Special Issue Compressors and Their Systems: Energy, Flow and Mechanical Systems)
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15 pages, 6019 KiB  
Article
An Automatic Fault Diagnosis Method for the Reciprocating Compressor Based on HMT and ANN
by Qian Lv, Liuxi Cai, Xiaoling Yu, Haihui Ma, Yun Li and Yue Shu
Appl. Sci. 2022, 12(10), 5182; https://doi.org/10.3390/app12105182 - 20 May 2022
Cited by 6 | Viewed by 1524
Abstract
The health management of the reciprocating compressor is crucial for its long term steady operation and safety. Online condition monitoring technology for the reciprocating compressor is almost mature, whereas the fault diagnosis technologies are still insufficient to meet the need. Therefore, in this [...] Read more.
The health management of the reciprocating compressor is crucial for its long term steady operation and safety. Online condition monitoring technology for the reciprocating compressor is almost mature, whereas the fault diagnosis technologies are still insufficient to meet the need. Therefore, in this paper, a novel fault detection method for the reciprocating compressor based on digital image processing and artificial neural network (ANN) was proposed. This method is implemented to the sectionalized pressure–volume (p–V) curves, which are obtained by dividing a working cycle in the cylinder into four thermal processes, including expansion, suction, compression and discharge. Hit-or-miss transform is adopted to extract the comprehensive gradients of expansion and compression curves, and vertical projection transform is applied to extract the vertical projection features. Finally, all of the features are fed to an ANN to do classification. To validate the proposed method, a seeded fault testing was conducted to collect real running data. The results showed that the new approach shows a good performance, with a high classification accuracy of 97.9%. Full article
(This article belongs to the Special Issue Compressors and Their Systems: Energy, Flow and Mechanical Systems)
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