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Quantifying uncertainties during the early design stage of a gas turbine disc by utilizing a bayesian framework

Quantifying uncertainties during the early design stage of a gas turbine disc by utilizing a bayesian framework
Quantifying uncertainties during the early design stage of a gas turbine disc by utilizing a bayesian framework

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 maximized 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.

American Institute of Aeronautics and Astronautics
Profir, Bogdan
6db80893-c830-4dbc-87bc-4c0a15077d06
Eres, Murat Hakki
b22e2d66-55c4-46d2-8ec3-46317033de43
Scanlan, James P.
7ad738f2-d732-423f-a322-31fa4695529d
Bates, Ron
1a02ebfa-30e4-4570-a7b8-21006d37b01c
Argyrakis, Christos
482b3a6c-d435-4042-9f6a-81d16b4075b2
Profir, Bogdan
6db80893-c830-4dbc-87bc-4c0a15077d06
Eres, Murat Hakki
b22e2d66-55c4-46d2-8ec3-46317033de43
Scanlan, James P.
7ad738f2-d732-423f-a322-31fa4695529d
Bates, Ron
1a02ebfa-30e4-4570-a7b8-21006d37b01c
Argyrakis, Christos
482b3a6c-d435-4042-9f6a-81d16b4075b2

Profir, Bogdan, Eres, Murat Hakki, Scanlan, James P., Bates, Ron and Argyrakis, Christos (2018) Quantifying uncertainties during the early design stage of a gas turbine disc by utilizing a bayesian framework. In 2018 Aviation Technology, Integration, and Operations Conference. American Institute of Aeronautics and Astronautics.. (doi:10.2514/6.2018-3202).

Record type: Conference or Workshop Item (Paper)

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 maximized 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.

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More information

e-pub ahead of print date: 24 June 2018
Published date: 25 June 2018
Venue - Dates: 18th AIAA Aviation Technology, Integration, and Operations Conference, 2018, , Atlanta, United States, 2018-06-25 - 2018-06-29

Identifiers

Local EPrints ID: 424371
URI: http://eprints.soton.ac.uk/id/eprint/424371
PURE UUID: 90fcfda2-384c-4b2b-aa8a-748f2ef1f160
ORCID for Murat Hakki Eres: ORCID iD orcid.org/0000-0003-4967-0833

Catalogue record

Date deposited: 05 Oct 2018 11:36
Last modified: 06 Jun 2024 01:41

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Contributors

Author: Bogdan Profir
Author: Ron Bates
Author: Christos Argyrakis

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