58604 - Uncertainty Quantification of a Jet Engine Performance Model Under Scarce Data Availability 

The development of jet engine components requires a detailed quantification of different uncertainty sources in order to improve the quality and robustness of the design. This paper demonstrates a new approach how to represent the uncertainties in a jet engine performance model caused by the manufacturing and assembly process. In earlier research studies, the manufacturing process was modelled with a probabilistic approach, i.e. by assuming a multivariate normal distribution for the corresponding parameters. Within the scope of this paper, the uncertainty of the components flow capacity and efficiency is quantified based on a limited set of measurement data. Due to the extreme scarcity of the data set, it is proposed to use methods from the field of non-probabilistic uncertainty quantification. In this paper, two different approaches are compared with each other. On the one hand, the uncertainty of the above-mentioned parameters are derived with the help of a so-called hyper-ellipsoid approach. On the other hand, the same parameters are represented with probability-boxes. In contrast to probabilistic methodologies, both approaches are able to represent the lack of measurement data adequately without making any additional assumptions regarding the underlying type of distribution.

Besides the manufacturing process, also the influence of changing ambient conditions is analyzed. Again, the challenge addressed to this paper is to quantify the corresponding parameters of the performance model based on an imprecise measurement set.

It will be shown that the detailed uncertainty quantification of the performance parameters is key in order to get a better understanding of the entire interdisciplinary design process and to improve the quality of the jet engine components. In future research projects, this can be demonstrated by coupling the performance model with the secondary air system.

Keywords: Jet engine performance model, uncertainty quantification, lack-of-data, probability-box