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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

Numerical design of Luenberger observers for nonlinear systems

Author(s): L. C. Ramos, F. D. Meglio, V. Morgenthaler, L. F. Figueira da Silva, P. Bernard
Published in: 2020 IEEE Conference on Decision and Control (CDC), 2020

Online Detection of Combustion Instabilities Using Supervised Machine Learning

Author(s): Michael McCartney, Wolfgang Polifke
Published in: Proceedings of the ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition, 2020
DOI: 10.1115/gt2020-14834

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

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, 2019
DOI: 10.1115/gt2019-91319

Reduced Order Models Applied to Laminar Diffusion Flames

Author(s): N. L. M. B. Junqueira, L. F. Figueira da Silva, L. C. Ramos
Published in: 2020 Brazilian Congress of Thermal Sciences and Engineering, Online., 2020

An Observer for the Electrically Heated Vertical Rijke Tube with Nonlinear Heat Release

Author(s): Wilhelmsen, N.C.A. and Di Meglio, F
Published in: IFAC-Papers OnLine, 2020

Reduced Order Model of Laminar Premixed Inverted Conical Flames

Author(s): Louise da Costa Ramos, Florent Di Meglio, Luis Fernando F. Da Silva, Valery Morgenthaler
Published in: AIAA Scitech 2020 Forum, 2020
DOI: 10.2514/6.2020-0416

Estimating Both Reflection Coefficients of 2 × 2 Linear Hyperbolic Systems with Single Boundary Measurement

Author(s): Wilhelmsen, N.C.A. and Di Meglio, F
Published in: In 2020 59th IEEE Conference on Decision and Control (CDC), 2020

ASSIMILATION OF EXPERIMENTAL DATA TO CREATE A QUANTITATIVELY-ACCURATE REDUCED ORDER THERMOACOUSTIC MODEL

Author(s): Garita, F., Yu, H., & Juniper, M.
Published in: Proceedings of the ASME Turbo Expo 2020: Turbine Technical Conference and Exposition, 2020

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

Author(s): Michael McCartney, Matthias Haeringer, Wolfgang Polifke
Published in: Journal of Engineering for Gas Turbines and Power, Issue 142/6, 2020, ISSN 0742-4795
DOI: 10.1115/1.4045516