In this paper a small Organic Rankine Cycle (ORC) plant was tested under different operating conditions and by using refrigerants (R245fa) as working fluids. In particular, attention was posed towards the scroll expander of the power plant in order to identify experimental parameters to use in its predictive semi-empirical model. Experimental results obtained by imposing different operating conditions at the expander inlet section (i.e. temperature, pressure, mass flow rate) and different temperature at the condensation section, were used to validate the mathematical model. An in-house code (MatLab/Scilab based) using CoolProp library for the accurate evaluation of fluid properties, was optimized by using a genetic algorithm implemented in modeFrontier software. Thus, the validated model was used in predictive mode to evaluate the machine performances. İ 2018 The Authors. Published by Elsevier Ltd.

Parameters identification for scroll expander semi-empirical model by using genetic algorithm

Braccio, G.;Cornacchia, G.;Pinto, G.;Fanelli, E.
2018

Abstract

In this paper a small Organic Rankine Cycle (ORC) plant was tested under different operating conditions and by using refrigerants (R245fa) as working fluids. In particular, attention was posed towards the scroll expander of the power plant in order to identify experimental parameters to use in its predictive semi-empirical model. Experimental results obtained by imposing different operating conditions at the expander inlet section (i.e. temperature, pressure, mass flow rate) and different temperature at the condensation section, were used to validate the mathematical model. An in-house code (MatLab/Scilab based) using CoolProp library for the accurate evaluation of fluid properties, was optimized by using a genetic algorithm implemented in modeFrontier software. Thus, the validated model was used in predictive mode to evaluate the machine performances. İ 2018 The Authors. Published by Elsevier Ltd.
Low grade heat recovery;Refrigerant;Scroll expander;Genetic algorithm;ORC
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12079/4424
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