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Machine learning for Advanced Gas turbine Injection SysTems to Enhance combustoR performance.

Deliverables

Modelling of acoustically absorbing liners.

Modelling of acoustically absorbing liners.

Comparison of different machine learning algorithms.

Comparison of different machine learning algorithms.

Application of machine learning in CFD.

Application of machine learning in CFD.

Summer school: Thermo-acoustics and combustion dynamics in aero gas turbine engines

Thermo-acoustics and combustion dynamics in aero gas turbine engines

Workshop C

Entrepreneurship, ethics, intellectual property rights and management

Workshop B

CFD for spray flame simulations

Workshop A

Machine Learning, Combustion and Acoustics in aero engine combustors

Data Management Plan (DMP)

Mandatory deliverable as consortium decided not to opt out of the pilot on open research.

Searching for OpenAIRE data...

Publications

Comparison of Machine Learning Algorithms in the Interpolation and Extrapolation of Flame Describing Functions

Author(s): Michael McCartney, Matthias Haeringer, Wolfgang Polifke
Published in: Volume 4B: Combustion, Fuels, and Emissions, Issue GT 2019, Anual, 2019
DOI: 10.1115/gt2019-91319

A model to study spontaneous oscillations in a lean premixed combustor using non-linear analysis

Author(s): Sara Navarro Arredondo, Jim Kok
Published in: Proceedings of the 26th International Congress on Sound and Vibration, Issue 26, 2019

Numerical Study Of A Swirl Atomized Spray Response To Acoustic Perturbations.

Author(s): Alireza Ghasemi, J.B.W. Kok
Published in: Proceedings of the 26th International Congress on Sound and Vibration, Issue 26, 2019

Bayesian machine learning for the prognosis of combustion instabilities from noise

Author(s): Ushnish Sengupta Carl Rasmussen Matthew Juniper
Published in: Proceedings of the ASME 2020 Turbomachinery Technical Conference Exposition, 2020
DOI: 10.31224/osf.io/ysgp4