City Research Online

Abstract

A significant improvement in the economy-of-scale of small-scale organic Rankine cycle (ORC) systems can arise from the appropriate design of components that can be manufactured in large volumes and implemented flexibly into a wide range of systems and potential applications. This, in turn, requires accurate predictions of component performance that can capture variations in the cycle conditions, parameters or changes to the working fluid. In this paper previous work investigating a modified similitude theory used to predict the performance of subsonic ORC turbines is extended to analyse the supersonic flow of organic fluids within 2D converging-diverging nozzles. Two nozzles are developed using a minimum length method of characteristics design model coupled to REFPROP. These are designed for R245fa and Toluene as working fluids with nozzle exit Mach numbers of 1.4 and 1.7 respectively. First, the nozzle performance is confirmed using CFD simulations, and then further CFD simulations are performed to evaluate the performance of the same nozzles over a range of different inlet conditions and with different working fluids. The CFD simulations are compared to predictions made using the original and modified similitude theories, and also to predictions made by conserving the Prandtl-Meyer function for the different operating conditions. The results indicate that whilst the modified similitude model does not accurately predict nozzle performance, conserving the Prandtl-Meyer function allows to predict the nozzle outlet Mach number to within 2% providing there is not a significant change in the polytropic index. Finally, the effect of working fluid replacement on the ORC system is discussed, and preliminary results demonstrate the possibility of matching a particular turbine to a heat source through optimal working fluid selection.

Publication Type:	Article
Additional Information:	Copyright Bioinfo Publications, 2017.
Subjects:	T Technology > TJ Mechanical engineering and machinery
Departments:	School of Science & Technology > Engineering
SWORD Depositor:	Symplectic Administrator

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