Estimation of Design Parameters and Performance for a State-of-the-Art Turbofan
Paper i proceeding, 2021

The aim of this study is to explore the possibility of matching a cycle performance model to public data on a state-of-the-art commercial aircraft engine (GEnx-1B). The study is focused on obtaining valuable information on figure of merits for the technology level of the low-pressure system and associated uncertainties. It is therefore directed more specifically towards the fan and low-pressure turbine efficiencies, the Mach number at the fan-face, the distribution of power between the core and the bypass stream as well as the fan pressure ratio. Available cycle performance data have been extracted from the engine emission databank provided by the International Civil Aviation Organization (ICAO), type certificate datasheets from the European Union Aviation Safety Agency (EASA) and the Federal Aviation Administration (FAA), as well as publicly available data from engine manufacturer. Uncertainties in the available source data are estimated and randomly sampled to generate inputs for a model matching procedure. The results show that fuel performance can be estimated with some degree of confidence. However, the study also indicates that a high degree of uncertainty is expected in the prediction of key low-pressure system performance metrics, when relying solely on publicly available data. This outcome highlights the importance of statistic-based methods as a support tool for the inverse design procedures. It also provides a better understanding on the limitations of conventional thermodynamic matching procedures, and the need to complement with methods that take into account conceptual design, cost and fuel burn.

performance modelling


latin hypercube sampling


Oliver Sjögren

Chalmers, Mekanik och maritima vetenskaper, Strömningslära

Carlos Xisto

Chalmers, Mekanik och maritima vetenskaper, Strömningslära

Tomas Grönstedt

Chalmers, Mekanik och maritima vetenskaper, Strömningslära

Proceedings of the ASME Turbo Expo

Vol. 1
9780791884898 (ISBN)

ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition, GT 2021
Virtual, Online, ,

FANG - Fläktalternativ för nästa generations motorer

VINNOVA (2019-02747), 2019-10-01 -- 2022-09-30.


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