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An integrated framework for Bayesian uncertainty quantification and probabilistic multi-criteria decision making in aero-engine preliminary design

An integrated framework for Bayesian uncertainty quantification and probabilistic multi-criteria decision making in aero-engine preliminary design
An integrated framework for Bayesian uncertainty quantification and probabilistic multi-criteria decision making in aero-engine preliminary design
The following paper presents a novel framework that enables making early design decisions based on probabilistic information obtained from fast, deterministic, low-fidelity tools, calibrated against high-fidelity data that is supported by experts’ knowledge. The proposed framework integrates a Probabilistic Multi-Criteria Decision Making technique with Bayesian Uncertainty Quantification concepts supported by the Kennedy and O’Hagan Framework. It allows continuous improvement of low-fidelity design tools as high-fidelity data is gathered and therefore facilitates investigation into the impacts the accumulation of high-fidelity data has on preliminary design process risk. The paper discusses theoretical concepts behind the framework and demonstrates its relevance by application in an illustrative combustor preliminary design case study.
Gramatyka, Jakub
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Eres, Murat Hakki
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Scanlan, James
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Moss, Michael
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Bates, Ron
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Gramatyka, Jakub
a3adf9fd-2c45-4388-ab73-42fe9a791d76
Eres, Murat Hakki
b22e2d66-55c4-46d2-8ec3-46317033de43
Scanlan, James
7ad738f2-d732-423f-a322-31fa4695529d
Moss, Michael
4cc758f8-0e77-4a92-a953-79f76b4e69c0
Bates, Ron
f3439cad-2150-43de-8513-d5fc90317be7

Gramatyka, Jakub, Eres, Murat Hakki, Scanlan, James, Moss, Michael and Bates, Ron (2015) An integrated framework for Bayesian uncertainty quantification and probabilistic multi-criteria decision making in aero-engine preliminary design. 16th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Aviation Forum 2015, Dallas, United States. 22 - 26 Jun 2015. (In Press)

Record type: Conference or Workshop Item (Paper)

Abstract

The following paper presents a novel framework that enables making early design decisions based on probabilistic information obtained from fast, deterministic, low-fidelity tools, calibrated against high-fidelity data that is supported by experts’ knowledge. The proposed framework integrates a Probabilistic Multi-Criteria Decision Making technique with Bayesian Uncertainty Quantification concepts supported by the Kennedy and O’Hagan Framework. It allows continuous improvement of low-fidelity design tools as high-fidelity data is gathered and therefore facilitates investigation into the impacts the accumulation of high-fidelity data has on preliminary design process risk. The paper discusses theoretical concepts behind the framework and demonstrates its relevance by application in an illustrative combustor preliminary design case study.

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More information

Accepted/In Press date: 18 February 2015
Venue - Dates: 16th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Aviation Forum 2015, Dallas, United States, 2015-06-22 - 2015-06-26
Organisations: Computational Engineering & Design Group

Identifiers

Local EPrints ID: 374693
URI: http://eprints.soton.ac.uk/id/eprint/374693
PURE UUID: a513e46f-fc87-4e77-b20d-0de761748a14
ORCID for Murat Hakki Eres: ORCID iD orcid.org/0000-0003-4967-0833

Catalogue record

Date deposited: 26 Feb 2015 14:20
Last modified: 15 Mar 2024 03:14

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Contributors

Author: Jakub Gramatyka
Author: James Scanlan
Author: Michael Moss
Author: Ron Bates

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