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Proceedings Papers

Reconstruction of Two-Dimensional to Three-Dimensional Flow Transition Fields Using Neural Network-Based Generative Adversarial Networks

Ruiling Xu, Song Gao, Zhiheng Wang, Guang Xi
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT36A022, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-128663
https://doi.org/10.1115/GT2024-128663
View Paper titled, Reconstruction of Two-Dimensional to Three-Dimensional Flow Transition Fields Using Neural Network-Based Generative Adversarial Networks
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Topics: Flow (Dynamics), Generative adversarial networks, Compressors, Generators, Artificial intelligence, Artificial neural networks, Blades, Cascades (Fluid dynamics), Computational methods, Computer simulation
Proceedings Papers

Multidisciplinary Analysis of the Impact of Blade Geometric Deviations on Compressor Performance Based on Fluid-Solid Grid Coordinated Deformation

Zhaoxu Tong, Huiying Zhang, Yan Zhou, Rong Xie, Shengli Xu
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A009, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-122558
https://doi.org/10.1115/GT2024-122558
View Paper titled, Multidisciplinary Analysis of the Impact of Blade Geometric Deviations on Compressor Performance Based on Fluid-Solid Grid Coordinated Deformation
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Topics: Artificial neural networks, Blades, Compressors, Deformation, Errors, Fluids, Machining
Proceedings Papers

Numerical Simulation and Prediction Using Neural Network for Internal Flow Field and Outlet Distortion Contour of S-Shape Inlet Ducts

ShiXuan Wang, Min Zhang, Juan Du, Dun Ba, ChaoQun Nie, YangYang Wang
Proc. ASME. GT2024, Volume 1: Aircraft Engine, V001T01A050, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-128513
https://doi.org/10.1115/GT2024-128513
View Paper titled, Numerical Simulation and Prediction Using Neural Network for Internal Flow Field and Outlet Distortion Contour of S-Shape Inlet Ducts
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Topics: Artificial neural networks, Computer simulation, Ducts, Flow (Dynamics), Internal flow, Pressure, Shapes
Proceedings Papers

A Data-Driven Method for Predicting the Overall Performance of a Three-Rotor Dual-Variable Cycle Engine

Qiyu Yuan, Tian Qiu, Guixian Qu, Peng Liu, Chuankai Liu, Shuiting Ding
Proc. ASME. GT2024, Volume 1: Aircraft Engine, V001T01A033, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-124941
https://doi.org/10.1115/GT2024-124941
View Paper titled, A Data-Driven Method for Predicting the Overall Performance of a Three-Rotor Dual-Variable Cycle Engine
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Topics: Artificial neural networks, Cycles, Design, Engines, Rotors, Flight, Geometry, Performance, Radial basis function networks, Simulation
Proceedings Papers

An Integrated Neural Network and Finite Element Method for Defect Characterization and Strength Prediction of Unidirectional Composites

Bo Zhang, Changqi Liu, Duoqi Shi, Xiaoguang Yang
Proc. ASME. GT2024, Volume 2: Ceramics and Ceramic Composites; Coal, Biomass, Hydrogen, and Alternative Fuels, V002T02A012, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-127092
https://doi.org/10.1115/GT2024-127092
View Paper titled, An Integrated Neural Network and Finite Element Method for Defect Characterization and Strength Prediction of Unidirectional Composites
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Topics: Artificial neural networks, Composite materials, Deep learning, Finite element analysis, Finite element methods, Tensile strength
Proceedings Papers

Generative Design of a Gas Turbine Combustor Using Invertible Neural Networks

Patrick Krüger, Hanno Gottschalk, Bastian Werdelmann, Werner Krebs
Proc. ASME. GT2024, Volume 3A: Combustion, Fuels, and Emissions, V03AT04A035, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-123963
https://doi.org/10.1115/GT2024-123963
View Paper titled, Generative Design of a Gas Turbine Combustor Using Invertible Neural Networks
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Topics: Artificial neural networks, Combustion chambers, Design, Gas turbines, Generative design
Proceedings Papers

A Parallel Online Performance Optimization Method for ATR Engines Based on Ensemble Neural Network

Weidong Cai, Wei Zhao, Binbin Liu, Xuesen Yang, Weiwei Luo, Qingjun Zhao
Proc. ASME. GT2024, Volume 4: Controls, Diagnostics, and Instrumentation, V004T05A032, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-127289
https://doi.org/10.1115/GT2024-127289
View Paper titled, A Parallel Online Performance Optimization Method for ATR Engines Based on Ensemble Neural Network
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Topics: Artificial neural networks, Engines, Optimization
Proceedings Papers

Predicting Gas Turbine NOx Emissions With Machine Learning

Harrison Eller, Sanjana Singh, Sumit Soni
Proc. ASME. GT2024, Volume 3B: Combustion, Fuels, and Emissions, V03BT04A015, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-127627
https://doi.org/10.1115/GT2024-127627
View Paper titled, Predicting Gas Turbine NOx Emissions With Machine Learning
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Topics: Emissions, Gas turbines, Machine learning, Nitrogen oxides, Deep neural networks, Artificial neural networks, Carbon monoxide, Combustion, Errors, Monitoring systems
Proceedings Papers

Prediction of High-Speed Hydraulic Dynamometer Safety Envelope Base on Deep Learning Neural Network

Guo Chen, Hong Xiao, Li Zhou, Rui You
Proc. ASME. GT2024, Volume 4: Controls, Diagnostics, and Instrumentation, V004T05A023, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-124927
https://doi.org/10.1115/GT2024-124927
View Paper titled, Prediction of High-Speed Hydraulic Dynamometer Safety Envelope Base on Deep Learning Neural Network
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Topics: Artificial neural networks, Deep learning, Dynamometers, Safety
Proceedings Papers

IndRNN-Based Data-Driven Modeling Integrated With Physical Knowledge for Engine Performance Monitoring

Dasheng Xiao, Hong Xiao, Zhanxue Wang
Proc. ASME. GT2024, Volume 4: Controls, Diagnostics, and Instrumentation, V004T05A015, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-124076
https://doi.org/10.1115/GT2024-124076
View Paper titled, IndRNN-Based Data-Driven Modeling Integrated With Physical Knowledge for Engine Performance Monitoring
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Topics: Aircraft engines, Engines, Modeling, Thrust, Artificial neural networks, Control systems, Deep learning, Errors, Exhaust systems, Flight
Proceedings Papers

Neural Network Based Digital Twin for Performance Prediction of Water Brake Dynamometer

Shuo Song, Hong Xiao, Leibo Jiang, Yufeng Liang
Proc. ASME. GT2024, Volume 4: Controls, Diagnostics, and Instrumentation, V004T05A028, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-126589
https://doi.org/10.1115/GT2024-126589
View Paper titled, Neural Network Based Digital Twin for Performance Prediction of Water Brake Dynamometer
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Topics: Artificial neural networks, Brakes, Digital twin, Dynamometers, Water
Proceedings Papers

A Comparison of Flat and Hierarchical Structures in Aircraft Engine Fault Classification Algorithms

Igor Loboda, Juan Luis Pérez-Ruiz, Iván González Castillo, Sergiy Yepifanov
Proc. ASME. GT2024, Volume 4: Controls, Diagnostics, and Instrumentation, V004T05A053, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-129597
https://doi.org/10.1115/GT2024-129597
View Paper titled, A Comparison of Flat and Hierarchical Structures in Aircraft Engine Fault Classification Algorithms
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Topics: Aircraft engines, Algorithms, Gas turbines, Engines, Monitoring systems, Artificial neural networks, Construction, Feature extraction, Machine learning, Pattern recognition
Proceedings Papers

Application of Machine Learning Techniques in Calibration and Data Reduction of Multi-Hole Probes

Arman Mirhashemi, Paht Juangphanich, Kenji Miki
Proc. ASME. GT2024, Volume 4: Controls, Diagnostics, and Instrumentation, V004T05A044, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-128955
https://doi.org/10.1115/GT2024-128955
View Paper titled, Application of Machine Learning Techniques in Calibration and Data Reduction of Multi-Hole Probes
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Topics: Calibration, Machine learning, Probes, Artificial neural networks, Flow (Dynamics), Algorithms, Pressure, Pressure measurement, Surface fitting, Uncertainty
Proceedings Papers

The Use of Artificial Neural Networks With a Systems Engineering Approach for Data Enhancement in Humidified Micro-Gas Turbine Application

Ahmad Jamil, Tina Dinh, Ward De Paepe, Homam Nikpey Somehsaraei
Proc. ASME. GT2024, Volume 5: Cycle Innovations, V005T06A009, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-124906
https://doi.org/10.1115/GT2024-124906
View Paper titled, The Use of Artificial Neural Networks With a Systems Engineering Approach for Data Enhancement in Humidified Micro-Gas Turbine Application
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Topics: Artificial neural networks, Cycles, Systems engineering, Turbines
Proceedings Papers

A Physics Informed Neural Network for Solving the Inverse Heat Transfer Problem in Gas Turbine Rotating Cavities

Mark R. Puttock-Brown, Goutham Krishna Mahesh Bindhu, Colin E. Ashby
Proc. ASME. GT2024, Volume 8: Heat Transfer: Internal Air Systems; Heat Transfer: Internal Cooling; Industrial and Cogeneration, V008T14A022, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-128967
https://doi.org/10.1115/GT2024-128967
View Paper titled, A Physics Informed Neural Network for Solving the Inverse Heat Transfer Problem in Gas Turbine Rotating Cavities
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Topics: Artificial neural networks, Cavities, Gas turbines, Heat transfer, Physics, Temperature profiles, Flux (Metallurgy), Heat, Heat conduction, Inverse problems
Proceedings Papers

The Hybrid Pathway to Flexible Power Turbines: Part C, Enabling Live Thermal Boundary Prediction Using Real-Time Plant Measurements and LSTM Neural Networks

Mark Baker, Budimir Rosic
Proc. ASME. GT2024, Volume 9: Manufacturing Materials and Metallurgy; Microturbines, Turbochargers, and Small Turbomachines; Oil & Gas Applications; Steam Turbine, V009T20A016, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-128908
https://doi.org/10.1115/GT2024-128908
View Paper titled, The Hybrid Pathway to Flexible Power Turbines: Part C, Enabling Live Thermal Boundary Prediction Using Real-Time Plant Measurements and LSTM Neural Networks
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Topics: Artificial neural networks, Turbines
Proceedings Papers

Unsupervised Methodology for the Prognostics of Gas Turbine Abrupt Faults

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giovanni Bechini
Proc. ASME. GT2024, Volume 9: Manufacturing Materials and Metallurgy; Microturbines, Turbochargers, and Small Turbomachines; Oil & Gas Applications; Steam Turbine, V009T19A007, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-122163
https://doi.org/10.1115/GT2024-122163
View Paper titled, Unsupervised Methodology for the Prognostics of Gas Turbine Abrupt Faults
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Topics: Artificial neural networks, Gas turbines
Proceedings Papers

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

Md Abir Hossain, Calvin M. Stewart
Proc. ASME. GT2024, Volume 9: Manufacturing Materials and Metallurgy; Microturbines, Turbochargers, and Small Turbomachines; Oil & Gas Applications; Steam Turbine, V009T17A008, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-123727
https://doi.org/10.1115/GT2024-123727
View Paper titled, Creep Lifetime Prediction of Alloy 617 Using Black Box Machine Learning Approach
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Topics: Alloys, Artificial neural networks, Creep, Machine learning, Rupture
Proceedings Papers

Neural Network Application to Rotordynamic Predictions of an Overhung Flexible Rotor

Giulio Deiana, Giuseppe Vannini, Giulia Meazzini, Francesco Cangioli, Enrico Meli
Proc. ASME. GT2024, Volume 10B: Structures and Dynamics — Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V10BT26A003, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-121854
https://doi.org/10.1115/GT2024-121854
View Paper titled, Neural Network Application to Rotordynamic Predictions of an Overhung Flexible Rotor
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Topics: Artificial neural networks, Rotors, Vibration
Proceedings Papers

Possibilities of Applying Machine Learning Techniques for Probabilistic Analysis in a Turbine Blade-Disk Interface

Elmira Emmrich, Md Tarekul Islam, Matthias Voigt, Lukas Bruder, Julian von Lautz, Ronald Mailach
Proc. ASME. GT2024, Volume 10B: Structures and Dynamics — Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V10BT25A002, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-122477
https://doi.org/10.1115/GT2024-122477
View Paper titled, Possibilities of Applying Machine Learning Techniques for Probabilistic Analysis in a Turbine Blade-Disk Interface
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Topics: Blades, Disks, Machine learning, Metamodeling, Turbines, Deep neural networks, Design, Finite element model, Failure, Finite element analysis