An Optimization Algorithm of Stirling Engine Performance for Low-Grade Heat Recovery

This paper deals with the optimization of the performance of Stirling engines for power generation by recovering low-enthalpy sources. A thermodynamic model, together with a Single Objective Genetic Algorithm (SOGA) to optimize the engine performance in terms of efficiency and specific power output, has been employed. The second order Simple Analysis model has been improved by including both heat transfer through the cylinder walls and mechanical losses. The new Improved Simple Analysis (ISA) model has been validated by considering the performance of the GPU-3 engine. The overall accuracy of the simulations is satisfactory with respect to measurements available in the literature. Two cases have been studied with a temperature gap between thermal sources of 80 and 60 K, respectively. The results show that the Gamma configuration is the best choice for the recovery of low-enthalpy energy sources, since it provides, for both cases, a higher specific power output than the other configurations, with a comparable efficiency. The geometry details of the optimal engines are given, thus providing guidance for the engine design. Finally, the engine performance has been analysed by varying the hot source temperature and engine mean pressure.