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Using Gaussian Processes to model combustion dynamics

Using Gaussian Processes to model combustion dynamics
Using Gaussian Processes to model combustion dynamics
Modelling the dynamics of combustion is a challenging task due to the non-linear interaction of many processes involved, including chemical kinetics, flame dynamics and acoustic pressure variations inside the chamber. Given that gas turbine engines are the dominant power generation sources, more sophisticated models that can make accurate and reliable predictions regarding the combustion processes and its efficiency, are always in high demand. This paper discusses the development of a data-driven model that is based purely on experimental data, collected from a combustion test rig. The model has been developed using Gaussian Processes, an advanced Bayesian non-parametric machine learning algorithm. The collected data, including pressure in-side the combustion primary zone and structural vibration, were all considered as possible candidates for adapting this algorithm to the dynamical characteristics of the combustion chamber under investigation. Accuracy in prediction using this empirical model was investigated for different combinations of experimental data and fractions of them, using the root mean squared error as performance measure. The covariance function parameters of the Gaussian Process model were optimised using a gradient-based algorithm for the best possible adaptation to the experimental dataset.
Matthaiou, Ioannis
7855a890-8929-4c90-a08c-9672fd7f6fda
Khandelwal, Bhupendra
42b15d72-24f9-42ee-a605-96ad33ad795d
Antoniadou, Ifigeneia
8d9150af-21ec-400f-9174-6ab108138a24
Matthaiou, Ioannis
7855a890-8929-4c90-a08c-9672fd7f6fda
Khandelwal, Bhupendra
42b15d72-24f9-42ee-a605-96ad33ad795d
Antoniadou, Ifigeneia
8d9150af-21ec-400f-9174-6ab108138a24

Matthaiou, Ioannis, Khandelwal, Bhupendra and Antoniadou, Ifigeneia (2017) Using Gaussian Processes to model combustion dynamics. 24th International Congress on Sound and Vibration, Park Plaza Westminster Bridge Hotel, London, United Kingdom. 23 - 27 Jul 2017.

Record type: Conference or Workshop Item (Paper)

Abstract

Modelling the dynamics of combustion is a challenging task due to the non-linear interaction of many processes involved, including chemical kinetics, flame dynamics and acoustic pressure variations inside the chamber. Given that gas turbine engines are the dominant power generation sources, more sophisticated models that can make accurate and reliable predictions regarding the combustion processes and its efficiency, are always in high demand. This paper discusses the development of a data-driven model that is based purely on experimental data, collected from a combustion test rig. The model has been developed using Gaussian Processes, an advanced Bayesian non-parametric machine learning algorithm. The collected data, including pressure in-side the combustion primary zone and structural vibration, were all considered as possible candidates for adapting this algorithm to the dynamical characteristics of the combustion chamber under investigation. Accuracy in prediction using this empirical model was investigated for different combinations of experimental data and fractions of them, using the root mean squared error as performance measure. The covariance function parameters of the Gaussian Process model were optimised using a gradient-based algorithm for the best possible adaptation to the experimental dataset.

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More information

Published date: 2017
Venue - Dates: 24th International Congress on Sound and Vibration, Park Plaza Westminster Bridge Hotel, London, United Kingdom, 2017-07-23 - 2017-07-27

Identifiers

Local EPrints ID: 481793
URI: http://eprints.soton.ac.uk/id/eprint/481793
PURE UUID: c19103e5-8598-43d0-ada1-e19ef0525330
ORCID for Ioannis Matthaiou: ORCID iD orcid.org/0009-0009-3603-2999

Catalogue record

Date deposited: 07 Sep 2023 16:46
Last modified: 18 Mar 2024 04:08

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Contributors

Author: Ioannis Matthaiou ORCID iD
Author: Bhupendra Khandelwal
Author: Ifigeneia Antoniadou

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