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Proceedings Papers

Image-Based Flashback Detection in a Hydrogen-Fired Gas Turbine Using a Convolutional Autoencoder

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Paul Porath, Vikas Yadav, Lukasz Panek, Abdulla Ghani
Proc. ASME. GT2024, Volume 3B: Combustion, Fuels, and Emissions, V03BT04A044, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-128722
https://doi.org/10.1115/GT2024-128722
View Paper titled, Image-Based Flashback Detection in a Hydrogen-Fired Gas Turbine Using a Convolutional Autoencoder
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Topics: Gas turbines, Hydrogen, Flames, Computer-aided engineering, Design, Combustion chambers, Errors, Fuels, Test facilities, Trains
Proceedings Papers

Exploiting a Transformer Architecture for Simultaneous Development of Transition and Turbulence Models for Turbine Flow Predictions

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Yuan Fang, Maximilian Reissmann, Roberto Pacciani, Yaomin Zhao, Andrew S. H. Ooi, Michele Marconcini, Harshal D. Akolekar, Richard D. Sandberg
Proc. ASME. GT2024, Volume 12C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, V12CT32A023, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-125550
https://doi.org/10.1115/GT2024-125550
View Paper titled, Exploiting a Transformer Architecture for Simultaneous Development of Transition and Turbulence Models for Turbine Flow Predictions
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Topics: Flow (Dynamics), Turbines, Turbulence, Computational fluid dynamics, Errors, Reynolds-averaged Navier–Stokes equations, Computer programming, Machine learning, Natural language processing, Physics
Proceedings Papers

Feasibility Study of Decentralized Lubrication System Design Through Branch and Bound and Genetic Algorithm for Turbomachinery Trains

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Yuzheng Chen, Bill Maier, Andy Buckenberger, Yaoyao Fiona Zhao
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A013, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-123728
https://doi.org/10.1115/GT2024-123728
View Paper titled, Feasibility Study of Decentralized Lubrication System Design Through Branch and Bound and Genetic Algorithm for Turbomachinery Trains
Open the PDF for Feasibility Study of Decentralized Lubrication System Design Through Branch and Bound and Genetic Algorithm for Turbomachinery Trains in another window
Topics: Design, Genetic algorithms, Industrial lubrication systems, Optimization, Trains, Turbomachinery
Proceedings Papers

Application of Deep Learning for Fan Rotor Blade Performance Prediction in Turbomachinery

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Jean Fesquet, Michael Bauerheim, Ludovic Rojda, Yannick Bousquet, Nicolas Binder
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A021, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-127348
https://doi.org/10.1115/GT2024-127348
View Paper titled, Application of Deep Learning for Fan Rotor Blade Performance Prediction in Turbomachinery
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Topics: Blades, Deep learning, Rotors, Turbomachinery, Artificial neural networks, Geometry, Optimization, Boundary layers, Computation, Deep neural networks
Proceedings Papers

Prediction of Steady and Unsteady Flow Quantities Using Multiscale Graph Neural Networks

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Sebastian Strönisch, Maximilian Sander, Marcus Meyer, Andreas Knüpfer
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A003, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-121697
https://doi.org/10.1115/GT2024-121697
View Paper titled, Prediction of Steady and Unsteady Flow Quantities Using Multiscale Graph Neural Networks
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Topics: Computational fluid dynamics, Graph neural networks, Turbomachinery, Unsteady flow, Flow (Dynamics), Fluids, Artificial neural networks, Databases, Design, Engineering simulation
Proceedings Papers

Hybrid Turbo-Compressor: A Technology Solution to Enable and Fully Exploit the Electricity and Gas Sector Coupling

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Fabio Baldanzini, Dante Tommaso Rubino, Marco Baldini, Bruno Rodà, Marco Tarenzi
Proc. ASME. GT2023, Volume 9: Microturbines, Turbochargers, and Small Turbomachines; Oil and Gas Applications, V009T19A015, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-103985
https://doi.org/10.1115/GT2023-103985
View Paper titled, Hybrid Turbo-Compressor: A Technology Solution to Enable and Fully Exploit the Electricity and Gas Sector Coupling
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Topics: Compressors, Gas turbines, Turbochargers, Trains, Generators, Electric motors, Emissions, Engines, Motors, Machinery
Proceedings Papers

Coupled Lateral-Torsional Analysis With Structural Interaction on Geared Shaft

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Mirko Libraschi, Francesco Capanni, Luca Innaco, Stefano Rossin
Proc. ASME. GT2023, Volume 11B: Structures and Dynamics — Emerging Methods in Engineering Design, Analysis, and Additive Manufacturing; Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V11BT26A004, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-100923
https://doi.org/10.1115/GT2023-100923
View Paper titled, Coupled Lateral-Torsional Analysis With Structural Interaction on Geared Shaft
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Topics: Bearings, Diaphragms (Mechanical devices), Diaphragms (Structural), Dynamic modeling, Dynamic systems, Gas turbines, Generators, Machinery, Mechanical drives, Modal analysis
Proceedings Papers

Wind Turbine Drivetrain Fault Detection Using Multi-Variate Deep Learning Combined With Signal Processing

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Faras Jamil, Francisco Jara Avila, Konstantinos Vratsinis, Cédric Peeters, Jan Helsen
Proc. ASME. GT2023, Volume 14: Wind Energy, V014T37A003, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-101689
https://doi.org/10.1115/GT2023-101689
View Paper titled, Wind Turbine Drivetrain Fault Detection Using Multi-Variate Deep Learning Combined With Signal Processing
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Topics: Flaw detection, Maintenance, Signal processing, Vibration, Wind turbines, Artificial neural networks, Emission spectroscopy, Errors, Fourier analysis, Machine learning
Proceedings Papers

Early Fault Detection Using Deep Learning on Compressed Cyclic Spectral Coherence Maps

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Fabian Perez Sanjines, Cédric Peeters, Timothy Verstraeten, Ivo Vervlimmeren, Ann Nowé, Jan Helsen
Proc. ASME. GT2023, Volume 14: Wind Energy, V014T37A001, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-101596
https://doi.org/10.1115/GT2023-101596
View Paper titled, Early Fault Detection Using Deep Learning on Compressed Cyclic Spectral Coherence Maps
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Topics: Flaw detection, Turbines, Vibration, Failure, Maintenance, Signals, Artificial neural networks, Electromagnetic spectrum, Machinery, Renewable energy
Proceedings Papers

Dynamic Modeling and Degradation Study of a Hybrid Electric Power System for Urban Air Mobility

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Teresa Donateo, Antonio Ficarella, Ludovica Spada Chiodo
Proc. ASME. GT2023, Volume 1: Aircraft Engine, V001T01A015, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-101597
https://doi.org/10.1115/GT2023-101597
View Paper titled, Dynamic Modeling and Degradation Study of a Hybrid Electric Power System for Urban Air Mobility
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Topics: Dynamic modeling, Power systems (Machinery), Rotorcraft, Urban air mobility, Dynamic response, Engines, Rotors, Aircraft, Algorithms, Batteries
Proceedings Papers

Multi-Objective Experimental Combustor Development Using Surrogate Model-Based Optimization

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Johann Moritz Reumschüssel, Jakob G. R. von Saldern, Bernhard Ćosić, Christian Oliver Paschereit
Proc. ASME. GT2023, Volume 3B: Combustion, Fuels, and Emissions, V03BT04A008, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-102832
https://doi.org/10.1115/GT2023-102832
View Paper titled, Multi-Objective Experimental Combustor Development Using Surrogate Model-Based Optimization
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Topics: Combustion chambers, Optimization, Fuels, Stress, Design, Emissions, Combustion, Combustion systems, Ejectors, Errors
Proceedings Papers

Prediction of Flow Separations in Turbine Center Frames Using Support Vector Machines and Random Forests

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Marian Staggl, Wolfgang Sanz, Peter Leitl, Maximilian Kurzthaler, Paul Pieringer
Proc. ASME. GT2023, Volume 13C: Turbomachinery — Deposition, Erosion, Fouling, and Icing; Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, V13CT32A015, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-101884
https://doi.org/10.1115/GT2023-101884
View Paper titled, Prediction of Flow Separations in Turbine Center Frames Using Support Vector Machines and Random Forests
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Topics: Flow separation, Support vector machines, Turbines, Aircraft engines, Algorithms, Computational fluid dynamics, Fuel consumption, Pressure, Trains
Proceedings Papers

Nonlinear Dynamics of Turbine Generator Shaft Trains: Evaluation of Bifurcation Sets Applying Numerical Continuation

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Ioannis Gavalas, Athanasios Chasalevris
Proc. ASME. GT2022, Volume 8B: Structures and Dynamics — Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V08BT26A005, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-80107
https://doi.org/10.1115/GT2022-80107
View Paper titled, Nonlinear Dynamics of Turbine Generator Shaft Trains: Evaluation of Bifurcation Sets Applying Numerical Continuation
Open the PDF for Nonlinear Dynamics of Turbine Generator Shaft Trains: Evaluation of Bifurcation Sets Applying Numerical Continuation in another window
Topics: Bifurcation, Nonlinear dynamics, Trains, Turbogenerators
Proceedings Papers

Undergraduate Hypersonics Research: Lessons From Two Years of the REU Site HYPER

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Jeffrey L. Kauffman, Ali P. Gordon
Proc. ASME. GT2022, Volume 5: Education; Electric Power; Fans and Blowers, V005T08A002, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-81021
https://doi.org/10.1115/GT2022-81021
View Paper titled, Undergraduate Hypersonics Research: Lessons From Two Years of the REU Site HYPER
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Topics: Undergraduate students, Students, Mentoring, Aerospace engineering, Career development, Engineers, Graduate students, Trains, Transportation systems, Turbomachinery
Proceedings Papers

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

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Lorenzo Tieghi, Stefan Becker, Alessandro Corsini, Giovanni Delibra, Stefan Schoder, Felix Czwielong
Proc. ASME. GT2022, Volume 5: Education; Electric Power; Fans and Blowers, V005T10A008, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-82116
https://doi.org/10.1115/GT2022-82116
View Paper titled, Machine-Learning Clustering Methods Applied to Detection of Noise Sources in Low-Speed Axial Fan
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Topics: Machine learning, Noise (Sound), Algorithms, Design, Fans, Simulation, Acoustics, Aerodynamic noise, Blades, Density
Proceedings Papers

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

Open Access

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

Gaussian Stochastic Process Modeling of Blend Repaired Airfoil Modal Response Using Reduced Basis Mode Shape Approach

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Jeffrey M. Brown, Alex A. Kaszynski, Daniel L. Gillaugh, Emily B. Carper, Joseph A. Beck
Proc. ASME. GT2021, Volume 9B: Structures and Dynamics — Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V09BT29A028, June 7–11, 2021
Publisher: American Society of Mechanical Engineers
Paper No: GT2021-60238
https://doi.org/10.1115/GT2021-60238
View Paper titled, Gaussian Stochastic Process Modeling of Blend Repaired Airfoil Modal Response Using Reduced Basis Mode Shape Approach
Open the PDF for Gaussian Stochastic Process Modeling of Blend Repaired Airfoil Modal Response Using Reduced Basis Mode Shape Approach in another window
Topics: Airfoils, Mode shapes, Modeling, Stochastic processes, Modal assurance criterion, Maintenance, Displacement, Geometry, Design, Finite element model
Proceedings Papers

Data-Driven Analysis of Engine Mission Severity Using Non-Dimensional Groups

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Tim Brandes, Stefano Scarso, Christian Koch, Stephan Staudacher
Proc. ASME. GT2021, Volume 2B: Turbomachinery — Axial Flow Turbine Aerodynamics; Deposition, Erosion, Fouling, and Icing, V02BT33A002, June 7–11, 2021
Publisher: American Society of Mechanical Engineers
Paper No: GT2021-58673
https://doi.org/10.1115/GT2021-58673
View Paper titled, Data-Driven Analysis of Engine Mission Severity Using Non-Dimensional Groups
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Topics: Engines, Flight, Machinery, Accuracy and precision, Algorithms, Blades, Compressors, Computation, Databases, Erosion
Proceedings Papers

Alarms, Shutdowns and Trip Rationalization

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Rasidi Mohamed, Syafeq Moazari Sukeri, Robert Mendoza, Rainer Kurz
Proc. ASME. GT2021, Volume 8: Oil and Gas Applications; Steam Turbine, V008T20A001, June 7–11, 2021
Publisher: American Society of Mechanical Engineers
Paper No: GT2021-00646
https://doi.org/10.1115/GT2021-00646
View Paper titled, Alarms, Shutdowns and Trip Rationalization
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Topics: Compressors, Control algorithms, Control systems, Damage, Downtime, Gas turbines, Instrumentation, Machinery, Profitability, Trains
Proceedings Papers

Detecting Nonsynchronous Vibration in Transonic Fans Using Machine Learning Techniques

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Yaozhi Lu, Mehdi Vahdati
Proc. ASME. GT2020, Volume 2D: Turbomachinery, V02DT38A002, September 21–25, 2020
Publisher: American Society of Mechanical Engineers
Paper No: GT2020-14261
https://doi.org/10.1115/GT2020-14261
View Paper titled, Detecting Nonsynchronous Vibration in Transonic Fans Using Machine Learning Techniques
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Topics: Fans, Machine learning, Vibration, Blades, Geometry, Manufacturing, High cycle fatigue, Stress, Switches, Trains