The aim of this study is to characterize a prototype of reciprocating piston expander integrated into a micro-ORC system test bench (in the kW range of power), installed at the laboratory of the university of Bologna. In order to simulate behavior and performances of the expander in not yet explored operating conditions, two semi-empirical models proposed in the literature have been opportunely adapted to the case of study and calibrated over a full set of available experimental data. One model is based on polynomial correlations on the expander efficiencies, whereas the other one is based on a lumped parameters approach with a more physical sense. Both the models have been evaluated on the error on predict the outputs and compared into performance prediction maps. The preliminary results demonstrate that the polynomial fitting functions model is the most accurate in predicting the outputs in the range of the explored working conditions. However, in order to verify the models extrapolation capability, more experimental points should be collected. The validation of the models outside the calibration range will be object of further investigations.

Performance prediction of a reciprocating piston expander with semi-empirical models

Bianchi M.;Branchini L.;De Pascale A.;Melino F.;Ottaviano S.;Peretto A.;Torricelli N.
2019

Abstract

The aim of this study is to characterize a prototype of reciprocating piston expander integrated into a micro-ORC system test bench (in the kW range of power), installed at the laboratory of the university of Bologna. In order to simulate behavior and performances of the expander in not yet explored operating conditions, two semi-empirical models proposed in the literature have been opportunely adapted to the case of study and calibrated over a full set of available experimental data. One model is based on polynomial correlations on the expander efficiencies, whereas the other one is based on a lumped parameters approach with a more physical sense. Both the models have been evaluated on the error on predict the outputs and compared into performance prediction maps. The preliminary results demonstrate that the polynomial fitting functions model is the most accurate in predicting the outputs in the range of the explored working conditions. However, in order to verify the models extrapolation capability, more experimental points should be collected. The validation of the models outside the calibration range will be object of further investigations.
Bianchi M.; Branchini L.; De Pascale A.; Melino F.; Ottaviano S.; Peretto A.; Torricelli N.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/706115
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