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Ushnish Sengupta
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Proceedings Papers

Real-Time Parameter Inference of Nonlinear Bluff-Body-Stabilized Flame Models Using Bayesian Neural Network Ensembles

Maximilian L. Croci, Ushnish Sengupta, Ekrem Ekici, Matthew P. Juniper
Proc. ASME. GT2023, Volume 3A: Combustion, Fuels, and Emissions, V03AT04A028, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-101460
https://doi.org/10.1115/GT2023-101460
View Paper titled, Real-Time Parameter Inference of Nonlinear Bluff-Body-Stabilized Flame Models Using Bayesian Neural Network Ensembles
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Topics: Artificial neural networks, Flames
Journal Articles

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

Michael McCartney, Ushnish Sengupta, Matthew Juniper
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. January 2022, 144(1): 011012.
Paper No: GTP-21-1377
https://doi.org/10.1115/1.4052145
Published Online: October 13, 2021
View Article titled, Reducing Uncertainty in the Onset of Combustion Instabilities Using Dynamic Pressure Information and Bayesian Neural Networks
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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
Publisher: American Society of Mechanical Engineers
Paper No: GT2021-60283
https://doi.org/10.1115/GT2021-60283
View Paper titled, Reducing Uncertainty in the Onset of Combustion Instabilities Using Dynamic Pressure Information and Bayesian Neural Networks
Open the PDF for Reducing Uncertainty in the Onset of Combustion Instabilities Using Dynamic Pressure Information and Bayesian Neural Networks in another window
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
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. July 2021, 143(7): 071001.
Paper No: GTP-20-1599
https://doi.org/10.1115/1.4049762
Published Online: March 29, 2021
View Article titled, Bayesian Machine Learning for the Prognosis of Combustion Instabilities From Noise
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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
Publisher: American Society of Mechanical Engineers
Paper No: GT2020-14904
https://doi.org/10.1115/GT2020-14904
View Paper titled, Bayesian Machine Learning for the Prognosis of Combustion Instabilities From Noise
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Topics: Combustion, Machine learning, Noise (Sound), Acoustics, Flames, Algorithms, Artificial neural networks, Boundary-value problems, Combustion chambers, Fingerprints