Application and comparison of semi-empirical models for performance prediction of a kW-size reciprocating piston expander

This work describes modeling and performance prediction of a kW-size reciprocating piston expander adopted in micro-Organic Rankine Cycle (ORC) energy systems. Two selected semi-empirical models have been opportunely adapted, calibrated and validated over a full set of experimental data to detect the best method for the simulation of a reciprocating machine. The first modelling approach is based on polynomial correlations of the expander efficiencies and it has been extended to account for the heat losses to ambient. The second one is a lumped parameters model using few key geometrical data and some physical equations to describe the process. Within the calibration range, the considered models show similar performance results of the output variables: the lowest mean relative error value is obtained on the prediction of exhaust temperature (lower than 2%). Maximum relative errors are obtained in the evaluation of rotational speed for the polynomial fitting model (equal to 10%) and in the electric power output for the lumped parameters approach (equal to 8%). The global error function value is close to 5% for both the applied approaches. Conversely, when compared outside of the calibration range, the polynomial fitting functions model proves to be less accurate overestimating the expander rotational speed while underestimating the value of the filling factor at high pressure ratios, and overestimating the isentropic efficiency at low pressure ratios.