Performance and Design Optimization of Solar Powered Stirling Engine Using Genetic Algorithm

Abstract

The aim of this work is to optimize the design and performance of solar powered γ Stirling engine based on genetic algorithm (GA). A second-order mathematical model which includes thermal losses coupled with genetic algorithm GA has been developed and used to find the best values for different design variables. The physical geometry of the γ Stirling engine has been used as an objective variable in the genetic algorithm GA to determine the optimal parameters. The design geometry of the heat exchanger was considered to be the objective variable. The heater slots height, heater effective length, cooler slots height, cooler effective length, re-generator foil unrolled length and re-generator effective length are assumed to be the objective variables. Also, three different types of working fluids have been used in the model simulation to investigate the effect of the different working fluid on the engine performance. The comparison between the results obtained from the simulation by using the original parameters and the results from the optimized parameters when the engine was powered by solar energy; the higher temperature was 923 K applied to the working fluid when the air, helium, and hydrogen were used as working fluid. The engine power increases from 140.58 watts to 228.54 watts, and it is enhanced by approximately 50%, when the heating temperature is 923 K and the air is used as working fluid. The result showed that the working temperature is one of the most important parameters; because the output power increases by increasing of the hot side temperature.