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Proceedings Papers

Simulation and Validation of Creep Damage on Grain Boundary of Polycrystalline Alloy 247

Tuan Duc Nguyen, Lucas Mäde, Dirk Kulawinski

Proc. ASME. GT2022, Volume 8B: Structures and Dynamics — Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V08BT25A003, June 13–17, 2022

Paper No: GT2022-82003

DOI: https://doi.org/10.1115/GT2022-82003

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Topics: Alloys, Creep, Damage, Grain boundaries, Machine learning, Simulation

Proceedings Papers

Using Machine Learning for Loss Prediction in a Hybrid Meanline Modeling Method to Deliver Improved Radial Turbine Performance Prediction

Pangbo Ren, Charles Stuart, Stephen Spence, Ryosuke Inomata, Takayuki Kobayashi, Isao Morita

Proc. ASME. GT2022, Volume 10B: Turbomachinery — Axial Flow Turbine Aerodynamics; Deposition, Erosion, Fouling, and Icing; Radial Turbomachinery Aerodynamics, V10BT35A004, June 13–17, 2022

Paper No: GT2022-80186

DOI: https://doi.org/10.1115/GT2022-80186

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Topics: Machine learning, Modeling, Turbines, Turbochargers

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

Multi-Objective Development of Machine-Learnt Closures for Fully Integrated Transition and Wake Mixing Predictions in Low Pressure Turbines

Harshal D. Akolekar, Fabian Waschkowski, Roberto Pacciani, Yaomin Zhao, Richard D. Sandberg

Proc. ASME. GT2022, Volume 10C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, V10CT32A013, June 13–17, 2022

Paper No: GT2022-81091

DOI: https://doi.org/10.1115/GT2022-81091

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Topics: Machinery, Pressure, Turbines, Wakes, Blades, Computational fluid dynamics, Machine learning, Boundary layers, Reynolds number, Reynolds-averaged Navier–Stokes equations

Proceedings Papers

Shrinkage Prediction Using Machine Learning for Additively Manufactured Ceramic and Metallic Components for Gas Turbine Applications

Peter Warren, Nandhini Raju, Milos Krsmanovic, Hossein Ebrahimi, Jayanta Kapat, Ramesh Subramanian, Ranajay Ghosh

Proc. ASME. GT2022, Volume 2: Coal, Biomass, Hydrogen, and Alternative Fuels; Controls, Diagnostics, and Instrumentation; Steam Turbine, V002T05A023, June 13–17, 2022

Paper No: GT2022-83418

DOI: https://doi.org/10.1115/GT2022-83418

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Topics: Additive manufacturing, Ceramics, Gas turbines, Machine learning, Shrinkage (Materials), Sintering, Ovens, Computer-aided design, Computer software, Dimensions

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

Statistical Rule Extraction for Gas Turbine Trip Prediction

Giovanni Bechini, Enzo Losi, Lucrezia Manservigi, Giovanni Pagliarini, Guido Sciavicco, Ionel Eduard Stan, Mauro Venturini

Proc. ASME. GT2022, Volume 7: Industrial and Cogeneration; Manufacturing Materials and Metallurgy; Microturbines, Turbochargers, and Small Turbomachines; Oil & Gas Applications, V007T19A012, June 13–17, 2022

Paper No: GT2022-82915

DOI: https://doi.org/10.1115/GT2022-82915

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Topics: Gas turbines, Artificial intelligence, Computers, Industrial gases, Machine learning, Statistical analysis, Turbines

Proceedings Papers

A Machine Learning Approach for Stress-Rupture Prediction of High Temperature Austenitic Stainless Steels

Md Abir Hossain, Adan J. Mireles, Calvin M. Stewart

Paper No: GT2022-84352

DOI: https://doi.org/10.1115/GT2022-84352

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Topics: Alloys, Computer programming, Design, High temperature, Machine learning, Rupture, Stainless steel, Stress

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

Uncertainty Quantification of Computational Flutter Estimates of a Compressor Cascade

Marco Rauseo, Fanzhou Zhao, Mehdi Vahdati, Quentin Rendu

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, V08AT21A010, June 13–17, 2022

Paper No: GT2022-81935

DOI: https://doi.org/10.1115/GT2022-81935

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Topics: Cascades (Fluid dynamics), Compressors, Flutter (Aerodynamics), Uncertainty quantification, Damping, Flow (Dynamics), Blades, Design, Uncertainty, Aircraft engines

Proceedings Papers

Automated Data-Driven Physics Discovery of Turbine Component Damage

Sheng Zhang, Ryan Jacobs, Sayan Ghosh, Ambarish Kulkarni, Liping Wang

Proc. ASME. GT2022, Volume 8B: Structures and Dynamics — Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V08BT25A008, June 13–17, 2022

Paper No: GT2022-83372

DOI: https://doi.org/10.1115/GT2022-83372

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Topics: Damage, Machine learning, Physics, Turbine components

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

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

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

Paper No: GT2022-82116

DOI: https://doi.org/10.1115/GT2022-82116

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Topics: Machine learning, Noise (Sound), Algorithms, Design, Fans, Simulation, Acoustics, Aerodynamic noise, Blades, Density

Proceedings Papers

Predictive Modelling of Local Film-Cooling Flow on a Turbine Rotor Blade

Eric T. DeShong, Reid A. Berdanier, Karen A. Thole

Proc. ASME. GT2022, Volume 6A: Heat Transfer — Combustors; Film Cooling, V06AT12A027, June 13–17, 2022

Paper No: GT2022-82507

DOI: https://doi.org/10.1115/GT2022-82507

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Topics: Blades, Film cooling, Flow (Dynamics), Modeling, Rotors, Turbines, Cooling, Testing, Turbine blades, Design

Proceedings Papers

Improved Design Space Exploration by Machine Learning Estimation for a Parametric Turbofan Model

Chad Foster, Jack Moore

Proc. ASME. GT2022, Volume 10D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Turbomachinery General Interest; Unsteady Flows in Turbomachinery, V10DT34A010, June 13–17, 2022

Paper No: GT2022-81836

DOI: https://doi.org/10.1115/GT2022-81836

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Topics: Design, Machine learning, Turbofans, Simulation, Noise (Sound), Regression models

Proceedings Papers

Rotor37 Aerodynamic Optimization: A Machine Learning Approach

Klajdi Beqiraj, Andrea Perrone, Marco Sanguineti, Luca Ratto, Gianluca Ricci

Paper No: GT2022-83063

DOI: https://doi.org/10.1115/GT2022-83063

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Topics: Machine learning, Optimization

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

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

From Concept to Wind Tunnel Model: Efficient Design Methodology for Innovative Low-Noise Propellers

Lieven Baert, Gabriele Grasso, Caroline Sainvitu, Ingrid Lepot, Marinus J. van Enkhuizen, Karel Lammers, Nicholas Bown

Paper No: GT2022-77969

DOI: https://doi.org/10.1115/GT2022-77969

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Topics: Design methodology, Noise (Sound), Propellers, Wind tunnels, Design, Aircraft, Optimization, Shapes, Aviation, Blades

Proceedings Papers

Reduced-Order Models for Effusion Modelling in Gas Turbine Combustors

S. Paccati, L. Mazzei, A. Andreini, B. Facchini

Proc. ASME. GT2021, Volume 5A: Heat Transfer — Combustors; Film Cooling, V05AT11A003, June 7–11, 2021

Paper No: GT2021-59384

DOI: https://doi.org/10.1115/GT2021-59384

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Topics: Combustion chambers, Gas turbines, Modeling, Computational fluid dynamics, Coolants, Cooling, Engineering simulation, Simulation, Temperature, Design

Proceedings Papers

Correlating Time-Resolved Pressure Measurements With Rim Sealing Effectiveness for Real-Time Turbine Health Monitoring

Eric T. DeShong, Benjamin Peters, Reid A. Berdanier, Karen A. Thole, Kamran Paynabar, Nagi Gebraeel

Proc. ASME. GT2021, Volume 5B: Heat Transfer — General Interest; Internal Air Systems; Internal Cooling, V05BT14A012, June 7–11, 2021

Paper No: GT2021-59586

DOI: https://doi.org/10.1115/GT2021-59586

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Topics: Pressure measurement, Sealing (Process), Turbines, Engines, Hardware, Feedback, Flow (Dynamics), Compressors, Damage, Design

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NARROW

Simulation and Validation of Creep Damage on Grain Boundary of Polycrystalline Alloy 247

Using Machine Learning for Loss Prediction in a Hybrid Meanline Modeling Method to Deliver Improved Radial Turbine Performance Prediction

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

Multi-Objective Development of Machine-Learnt Closures for Fully Integrated Transition and Wake Mixing Predictions in Low Pressure Turbines

Shrinkage Prediction Using Machine Learning for Additively Manufactured Ceramic and Metallic Components for Gas Turbine Applications

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

Statistical Rule Extraction for Gas Turbine Trip Prediction

A Machine Learning Approach for Stress-Rupture Prediction of High Temperature Austenitic Stainless Steels

Ensemble Learning Approach to the Prediction of Gas Turbine Trip

Uncertainty Quantification of Computational Flutter Estimates of a Compressor Cascade

Automated Data-Driven Physics Discovery of Turbine Component Damage

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

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

Predictive Modelling of Local Film-Cooling Flow on a Turbine Rotor Blade

Improved Design Space Exploration by Machine Learning Estimation for a Parametric Turbofan Model

Rotor37 Aerodynamic Optimization: A Machine Learning Approach

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

From Concept to Wind Tunnel Model: Efficient Design Methodology for Innovative Low-Noise Propellers

Reduced-Order Models for Effusion Modelling in Gas Turbine Combustors

Correlating Time-Resolved Pressure Measurements With Rim Sealing Effectiveness for Real-Time Turbine Health Monitoring

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