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Proceedings Papers

The Application of an Artificial Neural Network As a Baseline Model For Condition Monitoring of Innovative Humidified Micro Gas Turbine Cycles

Kathryn Colquhoun, Homam Nikpey Somehsaraei, Ward De Paepe
Proc. ASME. GT2022, Volume 4: Cycle Innovations; Cycle Innovations: Energy Storage, V004T06A021, June 13–17, 2022
Paper No: GT2022-82444
DOI: https://doi.org/10.1115/GT2022-82444
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Topics: Artificial neural networks, Condition monitoring, Cycles, Micro gas turbines
Proceedings Papers

Assessment of the Impeller/Volute Relationship of Centrifugal Fans From an Aerodynamic and Aeroacoustic Perspective

Till M. Biedermann, Youssef Moutamassik, Frank Kameier
Proc. ASME. GT2022, Volume 5: Education; Electric Power; Fans and Blowers, V005T10A003, June 13–17, 2022
Paper No: GT2022-79389
DOI: https://doi.org/10.1115/GT2022-79389
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Topics: Fans, Impellers, Aerodynamics, Artificial neural networks, Cement plants, Design, Optimization, Papermaking, Pareto optimization, Petrochemicals
Proceedings Papers

Machine-Learning Assisted Optimization of Generalized K-Omega (GEKO) Turbulence Model Parameters for Turbocharger Radial Compressor

Patrick Amstad, Kwok Kai So, Magnus Fischer
Proc. ASME. GT2022, Volume 10D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Turbomachinery General Interest; Unsteady Flows in Turbomachinery, V10DT34A002, June 13–17, 2022
Paper No: GT2022-78306
DOI: https://doi.org/10.1115/GT2022-78306
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Topics: Compressors, Machine learning, Optimization, Turbochargers, Turbulence, Artificial neural networks, Diffusers, Errors, Flow (Dynamics), Pressure
Proceedings Papers

Investigation of Physics-Informed Neural Networks Based Solution Techniques for Internal Flows

Pascal Post, Benjamin Winhart, Francesca di Mare
Proc. ASME. GT2022, Volume 10C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, V10CT32A011, June 13–17, 2022
Paper No: GT2022-80960
DOI: https://doi.org/10.1115/GT2022-80960
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Topics: Artificial neural networks, Internal flow, Physics, Cascades (Fluid dynamics), Computational fluid dynamics, Pressure, Blades, Boundary-value problems, Dimensions, Flow (Dynamics)
Proceedings Papers

Development of Machine-Learnt Turbulence Closures for Wake Mixing Predictions in Low-Pressure Turbines

Yuri Frey Marioni, Paolo Adami, Raul Vazquez Diaz, Andrea Cassinelli, Spencer Sherwin, Francesco Montomoli
Proc. ASME. GT2022, Volume 10C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, V10CT32A032, June 13–17, 2022
Paper No: GT2022-82531
DOI: https://doi.org/10.1115/GT2022-82531
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Topics: Machinery, Pressure, Turbines, Turbulence, Wakes, Artificial neural networks, Aircraft engines, Algebra, Flow (Dynamics), Kinetic energy
Proceedings Papers

Modeling of Inviscid Flow Shock Formation in a Wedge-Shaped Domain Using a Physics-Informed Neural Network-Based Partial Differential Equation Solver

Ryno Laubscher, Pieter Rousseau, Chris Meyer
Proc. ASME. GT2022, Volume 10C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, V10CT32A020, June 13–17, 2022
Paper No: GT2022-81768
DOI: https://doi.org/10.1115/GT2022-81768
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Topics: Artificial neural networks, Computational fluid dynamics, Inviscid flow, Modeling, Partial differential equations, Physics, Shock (Mechanics), Wedges, Architecture, Equations of state
Proceedings Papers

Axial Compressor Map Generation Leveraging Autonomous Self-Training AI

Maksym Burlaka, Leonid Moroz
Proc. ASME. GT2022, Volume 1: Aircraft Engine; Ceramics and Ceramic Composites, V001T01A009, June 13–17, 2022
Paper No: GT2022-81215
DOI: https://doi.org/10.1115/GT2022-81215
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Topics: Artificial neural networks, Compressors, Optimization
Proceedings Papers

Explainable and Interpretable AI-Assisted Remaining Useful Life Estimation for Aeroengines

Giorgois Protopapadakis, Asteris Apostolidis, Anestis I. Kalfas
Proc. ASME. GT2022, Volume 2: Coal, Biomass, Hydrogen, and Alternative Fuels; Controls, Diagnostics, and Instrumentation; Steam Turbine, V002T05A002, June 13–17, 2022
Paper No: GT2022-80777
DOI: https://doi.org/10.1115/GT2022-80777
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Topics: Service life (Equipment), Maintenance, Algorithms, Artificial intelligence, Artificial neural networks, Cycles, Databases, Engines, Feature selection, Flight
Proceedings Papers

Comparing Different Schemes in a Combined Technique of Kalman Filter, Artificial Neural Network and Fuzzy Logic for Gas Turbines Online Diagnostics

Simone Togni, Theoklis Nikolaidis, Suresh Sampath
Proc. ASME. GT2022, Volume 2: Coal, Biomass, Hydrogen, and Alternative Fuels; Controls, Diagnostics, and Instrumentation; Steam Turbine, V002T05A010, June 13–17, 2022
Paper No: GT2022-82037
DOI: https://doi.org/10.1115/GT2022-82037
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Topics: Artificial neural networks, Failure, Filtration, Fuzzy logic, Gas turbines, Kalman filters
Proceedings Papers

Transient Engine Condition Monitoring of Micro Gas Turbines in a Highly Dynamic Setting

Tihomir Varchev, Matthias Weiss, Stephan Staudacher, Christian Koch
Proc. ASME. GT2022, Volume 2: Coal, Biomass, Hydrogen, and Alternative Fuels; Controls, Diagnostics, and Instrumentation; Steam Turbine, V002T05A013, June 13–17, 2022
Paper No: GT2022-82377
DOI: https://doi.org/10.1115/GT2022-82377
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Topics: Condition monitoring, Engines, Micro gas turbines, Transients (Dynamics), Flight, Artificial neural networks, Flow (Dynamics), Measurement uncertainty, Noise (Sound), Wavelets
Proceedings Papers

Use of Convolutional Neural Network Image Classification and High-Speed Ion Probe Data Towards Real-Time Detonation Characterization in a Water-Cooled Rotating Detonation Engine

Kristyn B. Johnson, Donald H. Ferguson, Andrew C. Nix
Proc. ASME. GT2022, Volume 3B: Combustion, Fuels, and Emissions, V03BT04A052, June 13–17, 2022
Paper No: GT2022-83401
DOI: https://doi.org/10.1115/GT2022-83401
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Topics: Artificial neural networks, Engines, Explosions, Machine learning, Probes, Water
Proceedings Papers

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

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giovanni Bechini
Proc. ASME. GT2022, Volume 7: Industrial and Cogeneration; Manufacturing Materials and Metallurgy; Microturbines, Turbochargers, and Small Turbomachines; Oil & Gas Applications, V007T19A004, June 13–17, 2022
Paper No: GT2022-80666
DOI: https://doi.org/10.1115/GT2022-80666
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Topics: Gas turbines, Artificial neural networks, Failure, Maintenance, Sensors, Service life (Equipment)
Proceedings Papers

An Iterative Neural Operator to Predict the Thermo-Fluid Information in Internal Cooling Channels

Li Yang, Qi Wang, Yu Rao
Proc. ASME. GT2022, Volume 6B: Heat Transfer — General Interest/Additive Manufacturing Impacts on Heat Transfer; Internal Air Systems; Internal Cooling, V06BT15A016, June 13–17, 2022
Paper No: GT2022-82095
DOI: https://doi.org/10.1115/GT2022-82095
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Topics: Artificial neural networks, Cooling, Partial differential equations, Thermofluids
Proceedings Papers

Multidisciplinary Optimization of Radial Outflow Turbines With Highly Deformable Blades

André Governo, Bayindir H. Saracoglu
Proc. ASME. GT2022, Volume 7: Industrial and Cogeneration; Manufacturing Materials and Metallurgy; Microturbines, Turbochargers, and Small Turbomachines; Oil & Gas Applications, V007T18A022, June 13–17, 2022
Paper No: GT2022-84333
DOI: https://doi.org/10.1115/GT2022-84333
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Topics: Blades, Optimization, Outflow, Turbines, Flow (Dynamics), Rotors, Cavities, Design, Stress, Air flow
Proceedings Papers

Ensemble Learning Approach to the Prediction of Gas Turbine Trip

Enzo Losi, Mauro Venturini, Lucrezia Manservigi, Giovanni Bechini
Proc. ASME. GT2022, Volume 7: Industrial and Cogeneration; Manufacturing Materials and Metallurgy; Microturbines, Turbochargers, and Small Turbomachines; Oil & Gas Applications, V007T19A002, June 13–17, 2022
Paper No: GT2022-80372
DOI: https://doi.org/10.1115/GT2022-80372
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Topics: Gas turbines, Machine learning, Reliability, Artificial neural networks, Robustness, Support vector machines
Proceedings Papers

Investigation of Droplet Shedding in an Aeroengine Bearing Chamber Using Convolutional Neural Networks

Katrina Farbrother, Paloma Paleo Cagaeo, Kathy Johnson, Stephen Ambrose
Proc. ASME. GT2022, Volume 8A: Structures and Dynamics — Aerodynamics Excitation and Damping; Bearing and Seal Dynamics; Emerging Methods in Engineering Design, Analysis, and Additive Manufacturing; Fatigue, Fracture, and Life Prediction, V08AT22A013, June 13–17, 2022
Paper No: GT2022-82275
DOI: https://doi.org/10.1115/GT2022-82275
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Topics: Artificial neural networks, Bearings, Drops, Design, Two-phase flow, Commercial air transport, Film thickness, Geometry, Image processing, Imaging
Proceedings Papers

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

Giuseppe Bruni, Senthil Krishnababu, Simon Jackson
Proc. ASME. GT2022, Volume 8A: Structures and Dynamics — Aerodynamics Excitation and Damping; Bearing and Seal Dynamics; Emerging Methods in Engineering Design, Analysis, and Additive Manufacturing; Fatigue, Fracture, and Life Prediction, V08AT21A019, June 13–17, 2022
Paper No: GT2022-82427
DOI: https://doi.org/10.1115/GT2022-82427
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Topics: Blades, Compressors, Machine learning, Rotors, Design, Artificial neural networks, Computational fluid dynamics, Algorithms, Cycles, Damping
Proceedings Papers

Centrifugal Compressor Stage Efficiency and Rotor Stiffness Augmentation via Artificial Neural Networks

Andrea Agnolucci, Michele Marconcini, Andrea Arnone, Lorenzo Toni, Angelo Grimaldi, Marco Giachi
Proc. ASME. GT2021, Volume 2D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V02DT37A014, June 7–11, 2021
Paper No: GT2021-59998
DOI: https://doi.org/10.1115/GT2021-59998
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Topics: Artificial neural networks, Compressors, Rotors, Stiffness, Optimization, Impellers, Design, Diffusers, Reynolds-averaged Navier–Stokes equations
Proceedings Papers

Automatically Designed Deep Gaussian Process for Turbomachinery Application

Yuan Jin, Jin Chai, Olivier Jung
Proc. ASME. GT2021, Volume 2D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V02DT36A001, June 7–11, 2021
Paper No: GT2021-58469
DOI: https://doi.org/10.1115/GT2021-58469
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Topics: Turbomachinery, Turbines, Design, Artificial neural networks, Blades, Dimensions, Pressure, Robustness, Stochastic processes
Proceedings Papers

AI Assisted High Fidelity Multi-Physics Digital Twin of Industrial Gas Turbines

Senthil Krishnababu, Omar Valero, Roger Wells
Proc. ASME. GT2021, Volume 2D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V02DT36A007, June 7–11, 2021
Paper No: GT2021-58925
DOI: https://doi.org/10.1115/GT2021-58925
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Topics: Industrial gases, Physics, Turbines, Manufacturing, Compressors, Gas turbines, Maintenance, Teams, Artificial neural networks, Blocks (Building materials)