Assessment of Uncertainty in Aerospace Propulsion System Design and Simulation
Mavris, Dimitri N.
Roth, Bryce Alexander
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The subject of uncertainty analysis in complex systems design is a broad and bourgeoning field of study. This paper focuses on only three very specific areas of current propulsion research wherein uncertainty plays a pivotal role in the problem formulation. The first is probabilistic approaches to matching engine cycle models to test data. Engine cycle models must have a high confidence of representing the actual engine performance accurately. These models must be matched in the presence of measurement, manufacturing, and other sources of uncertainty. Moreover, the optimal model match tends to change with time such that the problem is stochastic in nature. Current efforts are focusing on using Bayesian statistics to enable a comprehensive (stochastic) treatment of the problem. The second research area of interest is probabilistic analysis methods for estimation of part life in life-limited gas turbine engines. There are many sources of uncertainty in estimating part life, including material properties, material cleanliness/flaw size, part loads, and usage profile. Moreover, life limited parts are subject to accumulated damage over time, and the damage accumulation rate is a strong function of vehicle mission profile and usage. Current efforts are therefore aimed at linking detailed part analysis (finite element and materials models) with higher-level system and mission-level parameters to enable rapid and accurate analysis with the least possible effort. Finally, the role of uncertainty in engine materials selection and insertion is discussed. The materials development process for critical turbine engine parts is very lengthy and subject to considerable uncertainty with regards to the optimal balance of materials properties required for a given application. This is an area of research that will benefit from the development of materials selection methods designed to yield robust materials applicable to the greatest possible number of engines.