Quantifying Uncertainties during the Early Design Stage of a gas Turbine Disc by Utilizing a Bayesian Framework

Bogdan Profir, Murat Hakki Eres, James Scanlan, Ron Bates, Christos Argyrakis

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

Quantifying uncertainties regarding the grain size of the turbine disk has been identified as a crucial aspect for the preliminary design stage. The reason for that is because the grain size is correlated to the life of the component which should preferably be maximised or at least quantified to the best of the designer’s abilities. In the grand scheme of things, this ultimately translates into a potential competitive advantage for the aero engine company. The prime focus of this paper is the investigation of material properties which was done by combining simulation and experimental data within a Bayesian framework in order to enhance the decision making process during the preliminary design stage. The aim of the case study presented here was to show how the physical processes can be modelled using a Bayesian network which updates prior probability distributions with real data in order to obtain more accurate predictors of reality. The first part of the paper explains the theory behind the framework, while the latter half shows some results as well as some conclusions which can be drawn.
Original languageEnglish
Title of host publicationAIAA Forum and Exposition
PublisherAmerican Institute of Aeronautics and Astronautics
ISBN (Electronic)978-1-62410-556-2
DOIs
Publication statusPublished - 24 Jun 2018
Externally publishedYes
Event18th AIAA Aviation Technology, Integration, and Operations Conference, 2018 - Atlanta, United States
Duration: 25 Jun 201829 Jun 2018

Conference

Conference18th AIAA Aviation Technology, Integration, and Operations Conference, 2018
CountryUnited States
CityAtlanta
Period25/06/1829/06/18

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  • Cite this

    Profir, B., Eres, M. H., Scanlan, J., Bates, R., & Argyrakis, C. (2018). Quantifying Uncertainties during the Early Design Stage of a gas Turbine Disc by Utilizing a Bayesian Framework. In AIAA Forum and Exposition American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2018-3202