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Journal Articles

Creep Lifetime Prediction of Alloy 617 Using Black Box Machine Learning Approach

Md Abir Hossain, Calvin M. Stewart
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. April 2025, 147(4): 041024.
Paper No: GTP-24-1545
https://doi.org/10.1115/1.4066600
Published Online: November 5, 2024
View Article titled, Creep Lifetime Prediction of Alloy 617 Using Black Box Machine Learning Approach
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Topics: Algorithms, Alloys, Artificial neural networks, Creep, Machine learning, Rupture, Chemistry, Testing, Stress
Journal Articles

Combining Machine Learning, Embedded Sensor Networks and Additive Burner Design for Combustor Structural Health Monitoring

Fabrice Giuliani, Nina Paulitsch, Andrea Hofer, Vojislav Petrovic-Filipovic, Benjamin Meier, Werner Bailer, Martin Winter, Roland Unterberger, Alexander Schricker
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. March 2025, 147(3): 031008.
Paper No: GTP-24-1424
https://doi.org/10.1115/1.4066393
Published Online: October 4, 2024
View Article titled, Combining Machine Learning, Embedded Sensor Networks and Additive Burner Design for Combustor Structural Health Monitoring
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Topics: Combustion, Flames, Machine learning, Signals, Sensors
Journal Articles

Prediction of Steam Turbine Blade Erosion Using Computational Fluid Dynamics Simulation Data and Hierarchical Machine Learning

Issei Fukamizu, Kazuhiko Komatsu, Masayuki Sato, Hiroaki Kobayashi
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. November 2024, 146(11): 111008.
Paper No: GTP-23-1519
https://doi.org/10.1115/1.4065815
Published Online: July 20, 2024
View Article titled, Prediction of Steam Turbine Blade Erosion Using Computational Fluid Dynamics Simulation Data and Hierarchical Machine Learning
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Topics: Blades, Erosion, Simulation, Turbines, Pressure, Machine learning
Journal Articles

Assessment of Machine Learning Wall Modeling Approaches for Large Eddy Simulation of Gas Turbine Film Cooling Flows: An a Priori Study

Tadbhagya Kumar, Pinaki Pal, Sicong Wu, A. Cody Nunno, Opeoluwa Owoyele, Michael M. Joly, Dima Tretiak
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. August 2024, 146(8): 081019.
Paper No: GTP-23-1294
https://doi.org/10.1115/1.4064556
Published Online: March 11, 2024
View Article titled, Assessment of Machine Learning Wall Modeling Approaches for Large Eddy Simulation of Gas Turbine Film Cooling Flows: An a Priori Study
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Topics: Delays, Film cooling, Flow (Dynamics), Modeling, Large eddy simulation, Machine learning, Shear stress, Gas turbines, Flat plates, Pressure gradient
Journal Articles

Tuning of Generalized K-Omega Turbulence Model by Using Adjoint Optimization and Machine Learning for Gas Turbine Combustor Applications

George Klavaris, Min Xu, Chris Hill, Florian Menter, Saurabh Patwardhan, Ishan Verma
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. August 2024, 146(8): 081014.
Paper No: GTP-23-1619
https://doi.org/10.1115/1.4064367
Published Online: February 26, 2024
View Article titled, Tuning of Generalized K-Omega Turbulence Model by Using Adjoint Optimization and Machine Learning for Gas Turbine Combustor Applications
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Topics: Artificial neural networks, Flow (Dynamics), Gas turbines, Machine learning, Modeling, Optimization, Turbulence, Combustion chambers, Reynolds-averaged Navier–Stokes equations, Reynolds number
Journal Articles

Prospects for Low-Resolution NDIR Sensors to Discern Ignition Properties of Fuels

Ashish Sutar, Anandvinod Dalmiya, Manaf Sheyyab, Hadis Anahideh, Eric K. Mayhew, Kenneth Brezinsky, Patrick T. Lynch
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. July 2024, 146(7): 071009.
Paper No: GTP-23-1518
https://doi.org/10.1115/1.4064334
Published Online: February 8, 2024
View Article titled, Prospects for Low-Resolution NDIR Sensors to Discern Ignition Properties of Fuels
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Topics: Fourier transform infrared spectroscopy, Fuels, Machine learning, Resolution (Optics), Sensors, Ignition, Jet fuels, Light-emitting diodes, Testing
Journal Articles

A Deep Learning Approach to Predict In-Cylinder Pressure of a Compression Ignition Engine

Accepted Manuscript
Rodrigo Ristow Hadlich, Jason Loprete, Dimitris Assanis
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power.
Paper No: GTP-23-1668
https://doi.org/10.1115/1.4064480
Published Online: January 13, 2024
View Article titled, A Deep Learning Approach to Predict In-Cylinder Pressure of a Compression Ignition Engine
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Topics: Cylinders, Diesel engines, Pressure, Engines, Optimization, Air pollution control, Computational fluid dynamics, Design, Emissions, Errors
Journal Articles

Development of an Artificial Intelligence Model to Predict Combustion Properties, With a Focus on Auto-Ignition Delay

Roda Bounaceur, Romain Heymes, Pierre-Alexandre Glaude, Baptiste Sirjean, René Fournet, Pierre Montagne, Alexandre Auvray, Eric Impellizzeri, Pierre Biehler, Alexandre Picard, Bastien Prieur-Garrouste, Michel Molière
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. June 2024, 146(6): 061011.
Paper No: GTP-23-1436
https://doi.org/10.1115/1.4063774
Published Online: January 4, 2024
View Article titled, Development of an Artificial Intelligence Model to Predict Combustion Properties, With a Focus on Auto-Ignition Delay
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Topics: Algorithms, Artificial intelligence, Artificial neural networks, Combustion, Delays, Ignition, Ignition delay, Natural gas, Pressure, Robustness
Journal Articles

Statistical Rule Extraction for Gas Turbine Trip Prediction

Giovanni Bechini, Enzo Losi, Lucrezia Manservigi, Giovanni Pagliarini, Guido Sciavicco, Ionel Eduard Stan, Mauro Venturini
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. May 2023, 145(5): 051017.
Paper No: GTP-22-1408
https://doi.org/10.1115/1.4056287
Published Online: January 10, 2023
View Article titled, Statistical Rule Extraction for Gas Turbine Trip Prediction
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Topics: Gas turbines, Statistical analysis, Turbines, Machine learning, Time series
Journal Articles

Machine Learning-Enabled Prediction of Transient Injection Map in Automotive Injectors With Uncertainty Quantification

Sudeepta Mondal, Gina M. Magnotti, Bethany Lusch, Romit Maulik, Roberto Torelli
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. April 2023, 145(4): 041015.
Paper No: GTP-22-1451
https://doi.org/10.1115/1.4055777
Published Online: December 13, 2022
View Article titled, Machine Learning-Enabled Prediction of Transient Injection Map in Automotive Injectors With Uncertainty Quantification
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Topics: Design, Ejectors, Needles, Porosity, Simulation, Transients (Dynamics), Uncertainty, Uncertainty quantification, Spatiotemporal phenomena, Internal flow
Journal Articles

Machine-Learning Clustering Methods Applied to Detection of Noise Sources in Low-Speed Axial Fan

Lorenzo Tieghi, Stefan Becker, Alessandro Corsini, Giovanni Delibra, Stefan Schoder, Felix Czwielong
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. March 2023, 145(3): 031020.
Paper No: GTP-22-1291
https://doi.org/10.1115/1.4055417
Published Online: December 8, 2022
View Article titled, Machine-Learning Clustering Methods Applied to Detection of Noise Sources in Low-Speed Axial Fan
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Topics: Algorithms, Blades, Flow (Dynamics), Machine learning, Noise (Sound), Simulation, Optimization, Pressure, Turbulence, Computation
Journal Articles

Ensemble Learning Approach to the Prediction of Gas Turbine Trip

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giovanni Bechini
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. February 2023, 145(2): 021009.
Paper No: GTP-22-1319
https://doi.org/10.1115/1.4055905
Published Online: November 28, 2022
View Article titled, Ensemble Learning Approach to the Prediction of Gas Turbine Trip
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Topics: Gas turbines, Machine learning, Reliability, Support vector machines, Artificial neural networks, Testing, Algorithms, Probability
Journal Articles

Application of Machine Learning to Forced Response Predictions of an Industrial Axial Compressor Rotor Blade

Giuseppe Bruni, Senthil Krishnababu, Simon Jackson
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. January 2023, 145(1): 011019.
Paper No: GTP-22-1341
https://doi.org/10.1115/1.4055634
Published Online: October 21, 2022
View Article titled, Application of Machine Learning to Forced Response Predictions of an Industrial Axial Compressor Rotor Blade
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Topics: Artificial neural networks, Blades, Compressors, Computational fluid dynamics, Damping, Design, Machine learning, Mode shapes, Resonance, Modal analysis
Journal Articles

Prediction of Gas Turbine Trip: A Novel Methodology Based on Random Forest Models

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giuseppe Fabio Ceschini, Giovanni Bechini, Giuseppe Cota, Fabrizio Riguzzi
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. March 2022, 144(3): 031025.
Paper No: GTP-21-1324
https://doi.org/10.1115/1.4053194
Published Online: January 21, 2022
View Article titled, Prediction of Gas Turbine Trip: A Novel Methodology Based on Random Forest Models
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Topics: Gas turbines, Reliability, Machine learning, Time series, Temperature, Compressors, Image segmentation, Natural, scientific and engineering processes and practices, Sensors, Thermocouples
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
Journal Articles

Combustion Tuning for a Gas Turbine Power Plant Using Data-Driven and Machine Learning Approach

Suhui Li, Huaxin Zhu, Min Zhu, Gang Zhao, Xiaofeng Wei
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. March 2021, 143(3): 031021.
Paper No: GTP-20-1699
https://doi.org/10.1115/1.4050020
Published Online: February 24, 2021
View Article titled, Combustion Tuning for a Gas Turbine Power Plant Using Data-Driven and Machine Learning Approach
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Topics: Artificial neural networks, Combustion, Combustion chambers, Emissions, Fuels, Gas turbines, Nitrogen oxides, Optimization, Machine learning, Stress
Journal Articles

Water Droplet Erosion Life Prediction Method for Steam Turbine Blade Materials Based on Image Recognition and Machine Learning

Zheyuan Zhang, Tianyuan Liu, Di Zhang, Yonghui Xie
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. March 2021, 143(3): 031009.
Paper No: GTP-20-1628
https://doi.org/10.1115/1.4049768
Published Online: February 8, 2021
View Article titled, Water Droplet Erosion Life Prediction Method for Steam Turbine Blade Materials Based on Image Recognition and Machine Learning
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Topics: Blades, Drops, Erosion, Machine learning, Steam turbines, Water, Service life (Equipment), Testing, Turbine blades, Data acquisition
Journal Articles

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

Michael McCartney, Matthias Haeringer, Wolfgang Polifke
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. June 2020, 142(6): 061009.
Paper No: GTP-19-1608
https://doi.org/10.1115/1.4045516
Published Online: May 29, 2020
View Article titled, Comparison of Machine Learning Algorithms in the Interpolation and Extrapolation of Flame Describing Functions
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Topics: Algorithms, Errors, Interpolation, Machine learning, Flames, Splines, Limit cycles
Journal Articles

Using Machine Learning to Predict Core Sizes of High-Efficiency Turbofan Engines

Michael T. Tong
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. November 2019, 141(11): 111023.
Paper No: GTP-19-1338
https://doi.org/10.1115/1.4044770
Published Online: October 23, 2019
View Article titled, Using Machine Learning to Predict Core Sizes of High-Efficiency Turbofan Engines
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Topics: Algorithms, Engines, Machine learning, Turbofans, Databases
Journal Articles

Evaluating Optimization Strategies for Engine Simulations Using Machine Learning Emulators

Daniel M. Probst, Mandhapati Raju, Peter K. Senecal, Janardhan Kodavasal, Pinaki Pal, Sibendu Som, Ahmed A. Moiz, Yuanjiang Pei
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. September 2019, 141(9): 091011.
Paper No: GTP-19-1208
https://doi.org/10.1115/1.4043964
Published Online: June 20, 2019
View Article titled, Evaluating Optimization Strategies for Engine Simulations Using Machine Learning Emulators
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Topics: Algorithms, Engines, Optimization, Simulation, Machine learning