Stirling engines are close cycle motors which can output mechanical work following a difference in temperature of two whatever thermal sources. This paper presents the preliminary design and the optimization of a system composed by a Low Differential Temperature Stirling Engine moving a simple single effect reciprocating water pump. The heat source is solar radiation, so that the engine can be installed in developing countries or in remote installation, without the need for fossil fuels. According to literature, the design of a Stirling engine is a complex task, since a lot of parameters should be considered at the same time; a mathematical model of the whole system, based on the second order theory for Stirling engine, has been implemented. In the following, it has been exploited to perform the optimization of the engine/pump mechanical system: maximum water flow for given maximum engine dimensions is the design goal of this problem. The Genetic Algorithms, Particle Swarm, Monte Carlo, Differential Evolution, Imperialist Comptetitive, and Simulated Annealing heuristic algorithms have been applied to solve the problem and to compare each other. The results obtained confirm the usefulness of the optimization in supporting the designer in such a complex task, in which a very accurate design is necessary to increase the efficiency of the system.

### DESIGN AND HEURISTIC OPTIMIZATION OF LOW TEMPERATURE DIFFERENTIAL STIRLING ENGINE FOR WATER PUMPING

#### Abstract

Stirling engines are close cycle motors which can output mechanical work following a difference in temperature of two whatever thermal sources. This paper presents the preliminary design and the optimization of a system composed by a Low Differential Temperature Stirling Engine moving a simple single effect reciprocating water pump. The heat source is solar radiation, so that the engine can be installed in developing countries or in remote installation, without the need for fossil fuels. According to literature, the design of a Stirling engine is a complex task, since a lot of parameters should be considered at the same time; a mathematical model of the whole system, based on the second order theory for Stirling engine, has been implemented. In the following, it has been exploited to perform the optimization of the engine/pump mechanical system: maximum water flow for given maximum engine dimensions is the design goal of this problem. The Genetic Algorithms, Particle Swarm, Monte Carlo, Differential Evolution, Imperialist Comptetitive, and Simulated Annealing heuristic algorithms have been applied to solve the problem and to compare each other. The results obtained confirm the usefulness of the optimization in supporting the designer in such a complex task, in which a very accurate design is necessary to increase the efficiency of the system.
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Utilizza questo identificativo per citare o creare un link a questo documento: `http://hdl.handle.net/11585/164249`
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