Optimisation of a Quasi-Steady Model of a Free-Piston Stirling Engine

Ayodeji Sowale, Edward J. Anthony and Athanasios John Kolios
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Ayodeji Sowale: Offshore Renewable Energy Engineering Centre, School of Water, Energy and Environment, Whittle Building 52, Cranfield University, Bedfordshire MK43 0AL, UK
Edward J. Anthony: Power Engineering Centre, School of Water, Energy and Environment, Whittle Building 52, Cranfield University, Bedfordshire MK43 0AL, UK
Athanasios John Kolios: Offshore Renewable Energy Engineering Centre, School of Water, Energy and Environment, Whittle Building 52, Cranfield University, Bedfordshire MK43 0AL, UK

Energies, 2018, vol. 12, issue 1, 1-17

Abstract: Energy from waste heat recovery is receiving considerable attention due to the demand for power systems that are less polluting. This has led to the investigation of external combustion engines such as the free-piston Stirling engine (FPSE) due to its ability to generate power from any source of heat and, especially, waste heat. However, there are still some limitations in the modelling, design and practical utilisation of this type of engine. Modelling of the FPSE has proved to be a difficult task due to the lack of mechanical linkages in its configuration, which poses problems for achieving stability. Also, a number of studies have been reported that attempt to optimise the output performance considering the characteristics of the engine configuration. In this study the optimisation of the second-order quasi-steady model of the gamma-type FPSE is carried out using the genetic algorithm (GA) to maximise the performance in terms of power output, and considering the design parameters of components such as piston and displacer damper, geometry of heat exchangers, and regenerator porosity. This present study shows that the GA optimisation of the RE-1000 FPSE design parameters improved its performance from work done and output power of 33.2 J and 996 W, respectively, with thermal efficiency of 23%, to 44.2 J and 1326 W with thermal efficiency of 27%.

Keywords: free-piston Stirling engine; genetic algorithm; optimisation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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