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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

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

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

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

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

Prediction of Dynamic Behavior of a Single Shaft Gas Turbine Using NARX Models

Hamid Asgari, Emmanuel Ory

Proc. ASME. GT2021, Volume 6: Ceramics and Ceramic Composites; Coal, Biomass, Hydrogen, and Alternative Fuels; Microturbines, Turbochargers, and Small Turbomachines, V006T19A007, June 7–11, 2021

Paper No: GT2021-58960

DOI: https://doi.org/10.1115/GT2021-58960

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Topics: Artificial neural networks, Gas turbines, Modeling

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

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

2 3 4 5

NARROW

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

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

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

Axial Compressor Map Generation Leveraging Autonomous Self-Training AI

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

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

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

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

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

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

Multidisciplinary Optimization of Radial Outflow Turbines With Highly Deformable Blades

Ensemble Learning Approach to the Prediction of Gas Turbine Trip

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

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

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

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

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

Prediction of Dynamic Behavior of a Single Shaft Gas Turbine Using NARX Models

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

Automatically Designed Deep Gaussian Process for Turbomachinery Application

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