Research Papers

Data-Driven Predesign Tool for Small-Scale Centrifugal Compressor in Refrigeration

[+] Author and Article Information
Mounier Violette

Laboratory for Applied Mechanical Design,
École polytechnique fédérale de Lausanne,
Neuchâtel 2000, Switzerland
e-mail: violette.mounier@epfl.ch

Picard Cyril

Laboratory for Applied Mechanical Design,
École polytechnique fédérale de Lausanne,
Neuchâtel 2000, Switzerland
e-mail: cyril.picard@epfl.ch

Schiffmann Jürg

Laboratory for Applied Mechanical Design,
École polytechnique fédérale de Lausanne,
Neuchâtel 2000, Switzerland
e-mail: jurg.schiffmann@epfl.ch

1Corresponding author.

Manuscript received June 27, 2018; final manuscript received July 3, 2018; published online October 24, 2018. Editor: Jerzy T. Sawicki.

J. Eng. Gas Turbines Power 140(12), 121011 (Oct 24, 2018) (8 pages) Paper No: GTP-18-1385; doi: 10.1115/1.4040845 History: Received June 27, 2018; Revised July 03, 2018

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.

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Fig. 1

Schematic layout of a centrifugal compressor and its main dimensions for a 1D-model

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Fig. 2

Repartition of the feasible compressor designs in a NsDs diagram

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Fig. 3

Comparison of the predicted optimum Ds with respect to the original Cordier line [16] (left) and to the predicted best efficiency with respect to Balje's prediction [15] (right)

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Fig. 4

Evolution of Ds, b4¯, r2s¯, and ηis for the best efficiency along Ns for a pressure ratio of 3.2

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Fig. 5

The 77% efficiency contours in the compressor Ns–b4¯ diagram generated at fixed pressure ratios and at r2s¯=0.56

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Fig. 6

The 77% efficiency contours in the compressor Ns–b4¯ diagram generated with PR = 2.6 and varying r2s¯

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Fig. 7

Evolution of the compressor skin friction (left) and tip clearance loss (right) ratios in the Ns–b4¯ diagram with PR = 2.6 and r2s¯=0.56

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Fig. 8

Evolution of the relative Mach number at the impeller outlet (left) and inlet (right) in the Ns–b4¯ diagram with PR = 2.6 and r2s¯=0.56

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Fig. 9

Evolution of the compressor blade loading (left) and recirculation loss (right) ratios in the Ns–b4¯ diagram with PR = 2.6 and r2s¯=0.56

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Fig. 10

Evolution of the compressor incidence (left) and disk loss ratio (right) in the Ns–b4¯ diagram with PR = 2.6 and r2s¯=0.56

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Fig. 11

Underlying phenomena shaping the efficiency contours of the new predesign model in the Ns–b4¯ diagram

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Fig. 12

Comparison of the optimum predicted efficiencies versus the 1D code

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Fig. 13

Comparison of the predicted optimal design variables versus the optima found using the mean line model



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