Uncertainty quantification by probabilistic analysis of Stirling engine performance

Abstract A Stirling engine thermodynamic cycle was computationally simulated and probabilistically evaluated in view of the several uncertainties in the performance parameters. Cumulative distribution functions and sensitivity factors were computed for the overall thermal efficiency and net specific power output due to the thermodynamic random variables. These results can be used to quickly identify the most critical design variables in order to optimize the design, enhance performance, increase system availability and make it cost effective. The analysis leads to the selection of the appropriate measurements to be used in the Stirling engine health determination and to the identification of both the most critical measurements and parameters. Probabilistic analysis aims at unifying and improving the control and health monitoring of Stirling engine by increasing the quality and quantity of information available about the engine’s health and performance.