Abstract

Unsteady flow distortion is of interest for the development of air-breathing propulsion systems. Stochastic fluctuations can generate incompatibilities between intakes and aero-engines. Observing the extreme flow distortion events during experimental testing is not guaranteed and statistical models such as extreme value theory (EVT) can be used to estimate the occurrence and magnitude of the fluctuations. However, the current industry standard does not provide guidance on how to apply these methods to obtain useful predictions. This work proposes a systematic process to assess the required number of observations for obtaining statistical convergence of the EVT predictions. This is achieved through shuffling of the data samples and relies on the availability of a sufficiently large initial dataset. This can be adopted by gas turbine engineers to evaluate the data recording requirements and to potentially reduce costs associated with experimental programs.

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