Bridging the Gap Between Deterministic and Probabilistic Uncertainty Quantification Using Advanced Proxy Based Methods

2015 ◽  
Author(s):  
N. Goodwin
Keyword(s):  
Uncertainty Quantification ◽  
Probabilistic Uncertainty

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

2021 ◽  
Author(s):  
Norbert Ludwig ◽  
Giulia Antinori ◽  
Marco Daub ◽  
Fabian Duddeck
Keyword(s):  
Uncertainty Quantification ◽  
Probabilistic Approach ◽  
Engine Performance ◽  
Theoretical Background ◽  
Performance Model ◽  
Data Availability ◽  
Data Set ◽  
Jet Engine ◽  
Probabilistic Uncertainty ◽  
Flow Capacity

Abstract The development of jet engine components requires a detailed quantification of different uncertainty sources to improve the quality and robustness of the design. In order to get a better understanding of the entire interdisciplinary jet engine design process, the detailed uncertainty quantification of the performance parameters is of vital importance. 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 modeled 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 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, three different approaches to derive the variation of the components’ flow capacity and efficiency are compared with each other. In contrast to probabilistic methodologies, all approaches are able to represent the lack of data without making any additional assumptions regarding the underlying type of distribution. As a result, each of the methodologies describes the uncertain parameters by probability-boxes. After clarifying the theoretical background, the results obtained from the different approaches are discussed in detail. It figured out that the propagation method for probability-boxes plays a crucial role for the uncertainty quantification.

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