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- W2766526110 abstract "Modelling the dynamics of combustion is a challenging task due to the non-linear interactionof many processes involved, including chemical kinetics, flame dynamics and acoustic pressurevariations inside the chamber. Given that gas turbine engines are the dominant power generationsources, more sophisticated models that can make accurate and reliable predictions regarding thecombustion processes and its efficiency, are always in high demand. This paper discusses thedevelopment of a data-driven model that is based purely on experimental data, collected froma combustion test rig. The model has been developed using Gaussian Processes, an advancedBayesian non-parametric machine learning algorithm. The collected data, including pressure insidethe combustion primary zone and structural vibration, were all considered as possible candidatesfor adapting this algorithm to the dynamical characteristics of the combustion chamberunder investigation. Accuracy in prediction using this empirical model was investigated for differentcombinations of experimental data and fractions of them, using the root mean squared erroras performance measure. The covariance function parameters of the Gaussian Process model wereoptimised using a gradient-based algorithm for the best possible adaptation to the experimentaldataset." @default.
- W2766526110 created "2017-11-10" @default.
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- W2766526110 date "2017-07-24" @default.
- W2766526110 modified "2023-09-24" @default.
- W2766526110 title "Using Gaussian Processes to model combustion dynamics" @default.
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- W2766526110 hasPublicationYear "2017" @default.
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