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Research Papers

Supercritical CO2 Radial Turbine Design Performance as a Function of Turbine Size Parameters

[+] Author and Article Information
Jianhui Qi

Queensland Geothermal Energy
Centre of Excellence,
The University of Queensland,
Brisbane 4072, Australia
e-mail: j.qi@uq.edu.au

Thomas Reddell

Queensland Geothermal Energy
Centre of Excellence,
The University of Queensland,
Brisbane 4072, Australia
e-mail: t.reddell@uq.edu.au

Kan Qin

Queensland Geothermal Energy
Centre of Excellence,
The University of Queensland,
Brisbane 4072, Australia
e-mail: k.qin1@uq.edu.au

Kamel Hooman

Queensland Geothermal Energy
Centre of Excellence,
The University of Queensland,
Brisbane 4072, Australia
e-mail: k.hooman@uq.edu.au

Ingo H. J. Jahn

Centre for Hypersonics,
The University of Queensland,
Brisbane 4072, Australia
e-mail: i.jahn@uq.edu.au

1Corresponding author.

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received August 10, 2016; final manuscript received January 20, 2017; published online March 28, 2017. Assoc. Editor: Anestis I. Kalfas.

J. Turbomach 139(8), 081008 (Mar 28, 2017) (11 pages) Paper No: TURBO-16-1191; doi: 10.1115/1.4035920 History: Received August 10, 2016; Revised January 20, 2017

Supercritical CO2 (sCO2) cycles are considered as a promising technology for next generation concentrated solar thermal, waste heat recovery, and nuclear applications. Particularly at small scale, where radial inflow turbines can be employed, using sCO2 results in both system advantages and simplifications of the turbine design, leading to improved performance and cost reductions. This paper aims to provide new insight toward the design of radial turbines for operation with sCO2 in the 100–200 kW range. The quasi-one-dimensional mean-line design code topgen is enhanced to explore and map the radial turbine design space. This mapping process over a state space defined by head and flow coefficients allows the selection of an optimum turbine design, while balancing performance and geometrical constraints. By considering three operating points with varying power levels and rotor speeds, the effect of these on feasible design space and performance is explored. This provides new insight toward the key geometric features and operational constraints that limit the design space as well as scaling effects. Finally, review of the loss break-down of the designs elucidates the importance of the respective loss mechanisms. Similarly, it allows the identification of design directions that lead to improved performance. Overall, this work has shown that turbine design with efficiencies in the range of 78–82% is possible in this power range and provides insight into the design space that allows the selection of optimum designs.

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Figures

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Fig. 1

An overview of topgen calculation process [19]

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Fig. 2

The schematic of the rotor blade plate thickness and trailing edge thickness

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Fig. 3

Radial turbine rotor blade geometric parameters [13]

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Fig. 4

Bearing selection criteria calculation for radial turbines [29]

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Fig. 5

Turbine design map for 100 kW, 160 kRPM operating point

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Fig. 6

Turbine efficiency contour for 100 kW, 160 kRPM operating point

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Fig. 7

Turbine design map at 200 kW and 113 kRPM

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Fig. 8

A 200 kW 113 kRPM turbine efficiency contour

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Fig. 9

Turbine design map at 100 kW and 120 kRPM

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Fig. 10

A 100 kW 120 kRPM turbine efficiency contour

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Fig. 11

Turbine profiles for three turbine cases

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Fig. 12

Turbine losses decomposition for three turbine cases

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