Testing and modeling of compressors for low-lift cooling applications

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009. === Includes bibliographical references (p. 133-136). === In this thesis, an inverter-driven variable speed scroll compressor is tested on a de-superheater test stand to determine its performance in areas of...

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Bibliographic Details
Main Author: Willingham, Ryan Alexander
Other Authors: Leslie K. Norford.
Format: Others
Language:English
Published: Massachusetts Institute of Technology 2010
Subjects:
Online Access:http://hdl.handle.net/1721.1/50560
Description
Summary:Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009. === Includes bibliographical references (p. 133-136). === In this thesis, an inverter-driven variable speed scroll compressor is tested on a de-superheater test stand to determine its performance in areas of low-lift and low compressor speed. The goal is to adapt this test stand so that it could be used to test a reciprocating compressor in this region. A control program is written to maintain a constant saturated suction temperature, suction temperature, and saturated discharge temperature. The program was able to maintain control with errors of ±0.2 °C at most points. At each test point, refrigerant mass flow rate, compressor input power, and discharge temperature is monitored. The amount of heat removed by the condenser was within 7% of the compressor input power and the inverter efficiency was within 5% of the compressor input power for all test points. The inverter efficiency is lowest at low speed. The isentropic efficiency is found to drop off significantly for low pressure ratios. A similar drop off is not expected for reciprocating compressors, so a model for reciprocating compressors is developed. The model is able to predict refrigerant mass flow rate and compressor input power as a function of shaft speed as well as suction and discharge pressures and temperatures. The model is able to accurately predict the mass flow rate with an RMS error within 0.5% and for the power model, the RMS errors are within 3.6%. The mass flow model is found to perform well when extrapolated into lower speed ranges with RMS errors remaining below 0.5%. === by Ryan Alexander Willingham. === S.M.