Domestic scale heat pumps and air conditioners are mainly driven by volumetric compressors. Yet the use of reduced scale centrifugal compressors is reconsidered due to their high efficiency and power density. The design procedure of centrifugal compressors starts with predesign tools based on the Cordier line. However, the optimality of the obtained predesign, which is the starting point of a complex and iterative process, is not guaranteed, especially for small-scale compressors operating with refrigerants. This paper proposes a data-driven predesign tool tailored for small-scale centrifugal compressors used in refrigeration applications. The predesign model is generated using an experimentally validated one-dimensional (1D) code which evaluates the compressor performance as a function of its detailed geometry and operating conditions. Using a symbolic regression tool, a reduced order model that predicts the performance of a given compressor geometry has been built. The proposed predesign model offers an alternative to the existing tools by providing a higher level of detail and flexibility. Particularly, the model includes the effect of the pressure ratio, the blade height ratio, and the shroud to tip radius ratio. The analysis of the centrifugal compressor losses allows identifying the underlying phenomena that shape the new isentropic efficiency contours. Compared to the validated 1D code, the new predesign model yields deviations below 4% on the isentropic efficiency, while running 1500 times faster. The new predesign model is, therefore, of significant interest when the compressor is part of an integrated system design process.
Data-Driven Predesign Tool for Small-Scale Centrifugal Compressor in Refrigeration
Manuscript received June 27, 2018; final manuscript received July 3, 2018; published online October 24, 2018. Editor: Jerzy T. Sawicki.
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Violette, M., Cyril, P., and Jürg, S. (October 24, 2018). "Data-Driven Predesign Tool for Small-Scale Centrifugal Compressor in Refrigeration." ASME. J. Eng. Gas Turbines Power. December 2018; 140(12): 121011. https://doi.org/10.1115/1.4040845
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