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Journal Articles

Convolutional Neural Network Denoising Auto-Encoders for Intelligent Aircraft Engine Gas Path Health Signal Noise Filtering

Junjie Zhao, Yi-Guang Li, Suresh Sampath

J. Eng. Gas Turbines Power. June 2023, 145(6): 061013.

Paper No: GTP-22-1234

DOI: https://doi.org/10.1115/1.4056128

Published Online: February 6, 2023

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Topics: Artificial neural networks, Filtration, Noise (Sound), Signals, Aircraft engines, Engines, Flight, Time series

Journal Articles

Detection of the Onset of Trip Symptoms Embedded in Gas Turbine Operating Data

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giovanni Bechini

J. Eng. Gas Turbines Power. March 2023, 145(3): 031023.

Paper No: GTP-22-1318

DOI: https://doi.org/10.1115/1.4055904

Published Online: December 8, 2022

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Topics: Artificial neural networks, Gas turbines, Probability, Reliability

Journal Articles

A Gray Target Calculation–Cloud Gravity Center Health Assessment Method for Gas Turbine Engine

Ran Ao, Yunpeng Cao, Junqi Luan, Xiaoyu Han, Shuying Li, Li Yan

J. Eng. Gas Turbines Power. April 2023, 145(4): 041005.

Paper No: GTP-22-1423

DOI: https://doi.org/10.1115/1.4055981

Published Online: December 8, 2022

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Topics: Engines, Entropy, Gas turbines, Gravity (Force), Optimization, Sensors, Service life (Equipment), Weight (Mass), Signal to noise ratio, Algorithms

Journal Articles

An Efficient Quantification Method Based on Feature Selection for High-Dimensional Uncertainties of Multistage Compressors

Junying Wang, Xinqian Zheng, Heli Yang, Zhenzhong Sun, Zhaoyun Song, Yu Fu

J. Eng. Gas Turbines Power. February 2023, 145(2): 021002.

Paper No: GTP-22-1122

DOI: https://doi.org/10.1115/1.4056017

Published Online: November 28, 2022

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Topics: Artificial neural networks, Compressors, Feature selection, Flow (Dynamics), Uncertainty, Uncertainty quantification, Construction

Journal Articles

Ensemble Learning Approach to the Prediction of Gas Turbine Trip

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giovanni Bechini

J. Eng. Gas Turbines Power. February 2023, 145(2): 021009.

Paper No: GTP-22-1319

DOI: https://doi.org/10.1115/1.4055905

Published Online: November 28, 2022

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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

J. Eng. Gas Turbines Power. January 2023, 145(1): 011019.

Paper No: GTP-22-1341

DOI: https://doi.org/10.1115/1.4055634

Published Online: October 21, 2022

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Topics: Artificial neural networks, Blades, Compressors, Computational fluid dynamics, Damping, Design, Machine learning, Mode shapes, Resonance, Modal analysis

Journal Articles

Gas Turbine Combustion Optimization Using Neural Network Model and Wavelet Analysis

Keyu Jia, Suhui Li

J. Eng. Gas Turbines Power. August 2022, 144(8): 081003.

Paper No: GTP-21-1599

DOI: https://doi.org/10.1115/1.4054524

Published Online: June 2, 2022

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Topics: Artificial neural networks, Combustion, Combustion chambers, Emissions, Gas turbines, Nitrogen oxides, Optimization, Pressure, Wavelets, Signals

Journal Articles

Data-Driven Approach for Identifying Mistuning in As-Manufactured Blisks

Sean T. Kelly, Andrea Lupini, Bogdan I. Epureanu

J. Eng. Gas Turbines Power. May 2022, 144(5): 051006.

Paper No: GTP-21-1431

DOI: https://doi.org/10.1115/1.4052503

Published Online: February 21, 2022

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Topics: Blades, Excitation, Noise (Sound), Artificial neural networks

Journal Articles

Shapley Additive Explanations of Multigeometrical Variable Coupling Effect in Transonic Compressor

Junying Wang, Xiao He, Baotong Wang, Xinqian Zheng

J. Eng. Gas Turbines Power. April 2022, 144(4): 041015.

Paper No: GTP-21-1486

DOI: https://doi.org/10.1115/1.4053322

Published Online: February 10, 2022

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Topics: Artificial neural networks, Blades, Compressors, Computational fluid dynamics, Databases, Design, Flow (Dynamics), Optimization, Rotors, Data mining

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

Journal Articles

An Ensemble of Recurrent Neural Networks for Real Time Performance Modeling of Three-Spool Aero-Derivative Gas Turbine Engine

Ibrahem M. A. Ibrahem, Ouassima Akhrif, Hany Moustapha, Martin Staniszewski

J. Eng. Gas Turbines Power. October 2021, 143(10): 101004.

Paper No: GTP-20-1645

DOI: https://doi.org/10.1115/1.4051112

Published Online: July 14, 2021

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Topics: Artificial neural networks, Engines, Gas turbines, Modeling, Testing, Delays, Algorithms

Journal Articles

Best Architecture of an Artificial Neural Network to Model Prefilming Airblast Atomization: Not So Deep Learning

G. Chaussonnet, S. Gepperth, S. Holz, R. Koch, H.-J. Bauer

J. Eng. Gas Turbines Power. July 2021, 143(7): 071006.

Paper No: GTP-20-1754

DOI: https://doi.org/10.1115/1.4049911

Published Online: March 29, 2021

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Topics: Artificial neural networks, Drops, Errors, Surface mount devices, Databases, Sprays, Shapes, Architecture, Deformation

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

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

Journal Articles

Neural Network Prediction of Boundary Layer Flashback

Scott B. Leask, Vincent G. McDonell, Scott Samuelsen

J. Eng. Gas Turbines Power. May 2021, 143(5): 054501.

Paper No: GTP-20-1299

DOI: https://doi.org/10.1115/1.4049987

Published Online: March 11, 2021

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Topics: Artificial neural networks, Boundary layers, Temperature, Flames, Fuels

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

J. Eng. Gas Turbines Power. March 2021, 143(3): 031021.

Paper No: GTP-20-1699

DOI: https://doi.org/10.1115/1.4050020

Published Online: February 24, 2021

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Topics: Artificial neural networks, Combustion, Combustion chambers, Emissions, Fuels, Gas turbines, Nitrogen oxides, Optimization, Machine learning, Stress

Journal Articles

Assessment of a Machine-Learnt Adaptive Wall-Function in a Compressor Cascade With Sinusoidal Leading Edge

Lorenzo Tieghi, Alessandro Corsini, Giovanni Delibra, Gino Angelini

J. Eng. Gas Turbines Power. December 2020, 142(12): 121011.

Paper No: GTP-20-1311

DOI: https://doi.org/10.1115/1.4048568

Published Online: December 1, 2020

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Topics: Airfoils, Algorithms, Artificial neural networks, Turbulence, Cascades (Fluid dynamics), Simulation, Compressors

Journal Articles

Multi-Objective Modeling of Leading-Edge Serrations Applied to Low-Pressure Axial Fans

Till M. Biedermann, M. Reich, C. O. Paschereit

J. Eng. Gas Turbines Power. November 2020, 142(11): 111009.

Paper No: GTP-20-1406

DOI: https://doi.org/10.1115/1.4048599

Published Online: October 26, 2020

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Topics: Acoustics, Artificial neural networks, Blades, Design, Fans, Flow (Dynamics), Modeling, Noise control, Optimization, Pareto optimization

Journal Articles

A Model-Guided Neural Network for the Prediction of Creep Behavior Under in-Service Conditions

Hui Hong, Zhenwei Cai, Han Wang, Weizhe Wang, Yingzheng Liu

J. Eng. Gas Turbines Power. July 2020, 142(7): 071008.

Paper No: GTP-20-1053

DOI: https://doi.org/10.1115/1.4047281

Published Online: June 30, 2020

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Topics: Artificial neural networks, Creep, Physics, Stress

Journal Articles

Implementation of Optimized Artificial Neural Networks for Real-Time Estimation of Low Pressure Cooled Exhaust Gas Recirculation in a Turbocharged Gasoline Direct Injection Engine Using a Model-Based Design Approach

Yuhyeok Jo, Kyunghan Min, Myoungho Sunwoo, Manbae Han

J. Eng. Gas Turbines Power. June 2020, 142(6): 061004.

Paper No: GTP-19-1700

DOI: https://doi.org/10.1115/1.4047125

Published Online: May 28, 2020

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Topics: Artificial neural networks, Combustion, Cylinders, Design, Engines, Exhaust gas recirculation, Pressure, Optimization

Journal Articles

A Multidimensional Extension of Balje Chart for Axial Flow Turbomachinery Using Artificial Intelligence-Based Meta-Models

Gino Angelini, Alessandro Corsini, Giovanni Delibra, Lorenzo Tieghi

J. Eng. Gas Turbines Power. November 2019, 141(11): 111012.

Paper No: GTP-19-1439

DOI: https://doi.org/10.1115/1.4044935

Published Online: October 16, 2019

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Topics: Axial flow, Blades, Cascades (Fluid dynamics), Design, Principal component analysis, Turbomachinery, Fans, Flow (Dynamics), Artificial neural networks, Chords (Trusses)

2 3 4

Journal citation

NARROW

Convolutional Neural Network Denoising Auto-Encoders for Intelligent Aircraft Engine Gas Path Health Signal Noise Filtering

Detection of the Onset of Trip Symptoms Embedded in Gas Turbine Operating Data

A Gray Target Calculation–Cloud Gravity Center Health Assessment Method for Gas Turbine Engine

An Efficient Quantification Method Based on Feature Selection for High-Dimensional Uncertainties of Multistage Compressors

Ensemble Learning Approach to the Prediction of Gas Turbine Trip

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

Gas Turbine Combustion Optimization Using Neural Network Model and Wavelet Analysis

Data-Driven Approach for Identifying Mistuning in As-Manufactured Blisks

Shapley Additive Explanations of Multigeometrical Variable Coupling Effect in Transonic Compressor

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

An Ensemble of Recurrent Neural Networks for Real Time Performance Modeling of Three-Spool Aero-Derivative Gas Turbine Engine

Best Architecture of an Artificial Neural Network to Model Prefilming Airblast Atomization: Not So Deep Learning

Bayesian Machine Learning for the Prognosis of Combustion Instabilities From Noise

Neural Network Prediction of Boundary Layer Flashback

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

Assessment of a Machine-Learnt Adaptive Wall-Function in a Compressor Cascade With Sinusoidal Leading Edge

Multi-Objective Modeling of Leading-Edge Serrations Applied to Low-Pressure Axial Fans

A Model-Guided Neural Network for the Prediction of Creep Behavior Under in-Service Conditions

Implementation of Optimized Artificial Neural Networks for Real-Time Estimation of Low Pressure Cooled Exhaust Gas Recirculation in a Turbocharged Gasoline Direct Injection Engine Using a Model-Based Design Approach

A Multidimensional Extension of Balje Chart for Axial Flow Turbomachinery Using Artificial Intelligence-Based Meta-Models

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