Performance characterization and optimization of a gamma Stirling engine

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Insinööritieteiden korkeakoulu | Master's thesis
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Master's Programme in Mechanical Engineering (MEC)
In recent times the Stirling engine has become a subject of interest for different industries and scientific institutions across the world, mainly due to its competitive advantages over the internal combustion engine. The Stirling technology offers multi-fuel capability, high reliability and low emissions. The Stirling engine was invented in 1816 by Robert Stirling and remains a key subject of optimization studies. This indicates that the engine has great potential for improvement. The objective of this thesis is to characterize the engine performance and implement Uncertainty Quantification (UQ) and Sensitivity Analysis (SA) methods to identify the main input variables that contribute the most to the variance of the performance. Understanding the influence of the variations in design and manufacturing inputs on the engine performance variability will allow for an improvement in the consistency of optimization results. For this purpose, a computer-simulated model was developed to predict shaft power and an experimental apparatus for measuring the engine shaft power was built. The computer-simulated model is based on isothermal methods to compute the thermodynamic power generated by the engine, and includes power losses due to mechanical friction and fluid friction, such as viscous friction, piston finite speed and hysteresis losses. Shaft power results from simulation are compared against the shaft power results, which were measured experimentally using the experimental apparatus. The results of the thesis show the input variables that are most likely causing variations on the engine shaft power. Displacer cylinder inner diameter is found to be the highest contributor, followed by the hot side clearance volume, cold side clearance volume, power piston amplitude and in smaller measure, displacer amplitude and tubular path diameter.
Otto, Kevin
Thesis advisor
Otto, Kevin
Stirling engine, uncertainty quantification, sensitivity analysis, friction model, parasitic losses
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