Search Results | ASME Digital Collection

1-4 of 4

Sort by

Sort Order Select

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

View Article

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

View Paper

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

Journal Articles

Bayesian Machine Learning for the Prognosis of Combustion Instabilities From Noise

Ushnish Sengupta, Carl E. Rasmussen, Matthew P. Juniper

J. Eng. Gas Turbines Power. July 2021, 143(7): 071001.

Paper No: GTP-20-1599

DOI: https://doi.org/10.1115/1.4049762

Published Online: March 29, 2021

View Article

Topics: Acoustics, Artificial neural networks, Combustion, Combustion chambers, Machine learning, Noise (Sound), Turbulence

Proceedings Papers

Bayesian Machine Learning for the Prognosis of Combustion Instabilities From Noise

Ushnish Sengupta, Carl E. Rasmussen, Matthew P. Juniper

Proc. ASME. GT2020, Volume 4A: Combustion, Fuels, and Emissions, V04AT04A047, September 21–25, 2020

Paper No: GT2020-14904

DOI: https://doi.org/10.1115/GT2020-14904

View Paper

Topics: Combustion, Machine learning, Noise (Sound), Acoustics, Flames, Algorithms, Artificial neural networks, Boundary-value problems, Combustion chambers, Fingerprints

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

Bayesian Machine Learning for the Prognosis of Combustion Instabilities From Noise

Bayesian Machine Learning for the Prognosis of Combustion Instabilities From Noise

Journals

Conference Proceedings

eBooks

Resources

Opportunities

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

Bayesian Machine Learning for the Prognosis of Combustion Instabilities From Noise

Bayesian Machine Learning for the Prognosis of Combustion Instabilities From Noise

Journals

Conference Proceedings

eBooks

Resources

Opportunities

This Feature Is Available To Subscribers Only