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Journal Articles

Reducing Uncertainty in the Onset of Combustion Instabilities Using Dynamic Pressure Information and Bayesian Neural Networks

Michael McCartney, Ushnish Sengupta, Matthew Juniper

J. Eng. Gas Turbines Power. January 2022, 144(1): 011012.

Paper No: GTP-21-1377

DOI: https://doi.org/10.1115/1.4052145

Published Online: October 13, 2021

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Topics: Artificial neural networks, Combustion, Combustion chambers, Combustion systems, Engineering prototypes, Errors, Pressure, Probability, Signals, Uncertainty

Proceedings Papers

Reducing Uncertainty in the Onset of Combustion Instabilities Using Dynamic Pressure Information and Bayesian Neural Networks

Michael McCartney, Ushnish Sengupta, Matthew Juniper

Proc. ASME. GT2021, Volume 3B: Combustion, Fuels, and Emissions, V03BT04A042, June 7–11, 2021

Paper No: GT2021-60283

DOI: https://doi.org/10.1115/GT2021-60283

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Topics: Artificial neural networks, Combustion, Pressure, Uncertainty, Hidden variables, Combustion chambers, Combustion systems, Emissions, Errors, Wall temperature

Proceedings Papers

Online Detection of Combustion Instabilities Using Supervised Machine Learning

Michael McCartney, Thomas Indlekofer, Wolfgang Polifke

Proc. ASME. GT2020, Volume 4A: Combustion, Fuels, and Emissions, V04AT04A045, September 21–25, 2020

Paper No: GT2020-14834

DOI: https://doi.org/10.1115/GT2020-14834

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Topics: Combustion, Machine learning, Signals, Pressure, Combustion chambers

Journal Articles

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

Michael McCartney, Matthias Haeringer, Wolfgang Polifke

J. Eng. Gas Turbines Power. June 2020, 142(6): 061009.

Paper No: GTP-19-1608

DOI: https://doi.org/10.1115/1.4045516

Published Online: May 29, 2020

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Topics: Algorithms, Errors, Interpolation, Machine learning, Flames, Splines, Limit cycles

Proceedings Papers

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

Michael McCartney, Matthias Haeringer, Wolfgang Polifke

Proc. ASME. GT2019, Volume 4B: Combustion, Fuels, and Emissions, V04BT04A020, June 17–21, 2019

Paper No: GT2019-91319

DOI: https://doi.org/10.1115/GT2019-91319

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Topics: Algorithms, Flames, Interpolation, Machine learning, Errors, Limit cycles, Simulation, Splines, Uncertainty quantification

Journal citation

NARROW

Reducing Uncertainty in the Onset of Combustion Instabilities Using Dynamic Pressure Information and Bayesian Neural Networks

Reducing Uncertainty in the Onset of Combustion Instabilities Using Dynamic Pressure Information and Bayesian Neural Networks

Online Detection of Combustion Instabilities Using Supervised Machine Learning

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

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

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Reducing Uncertainty in the Onset of Combustion Instabilities Using Dynamic Pressure Information and Bayesian Neural Networks

Reducing Uncertainty in the Onset of Combustion Instabilities Using Dynamic Pressure Information and Bayesian Neural Networks

Online Detection of Combustion Instabilities Using Supervised Machine Learning

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

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

Journals

Conference Proceedings

eBooks

Resources

Opportunities

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