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Proceedings Papers

Axial Compressor Map Generation Leveraging Autonomous Self-Training Artificial Intelligence. Phase 2

Maksym Burlaka, Sascha Podlech, Leonid Moroz
Proc. ASME. GT2023, Volume 1: Aircraft Engine, V001T01A018, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-102054
https://doi.org/10.1115/GT2023-102054
View Paper titled, Axial Compressor Map Generation Leveraging Autonomous Self-Training Artificial Intelligence. Phase 2
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Topics: Artificial intelligence, Compressors, Machine learning
Proceedings Papers

Development of an Artificial Intelligence Model to Predict Combustion Properties, With a Focus on Auto-Ignition Delay

Roda Bounaceur, Romain Heymes, Pierre-Alexandre Glaude, Baptiste Sirjean, René Fournet, Pierre Montagne, Alexandre Auvray, Eric Impellizzeri, Pierre Biehler, Alexandre Picard, Bastien Prieur-Garrouste, Michel Molière
Proc. ASME. GT2023, Volume 2: Ceramics and Ceramic Composites; Coal, Biomass, Hydrogen, and Alternative Fuels, V002T03A013, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-102128
https://doi.org/10.1115/GT2023-102128
View Paper titled, Development of an Artificial Intelligence Model to Predict Combustion Properties, With a Focus on Auto-Ignition Delay
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Topics: Artificial intelligence, Combustion, Delays, Hydrogen, Ignition, Ignition delay, Errors, Natural gas, Robustness, Testing
Proceedings Papers

Adapting Artificial Neural Networks Training Algorithms to Adjoint-Based Aerodynamic Shape Optimization

Fernando Gisbert, David Cadrecha, Jaime Quintanal, Adrián Sotillo, Jesús Pueblas, Aida Serrano, Ricardo Puente, Roque Corral
Proc. ASME. GT2023, Volume 13D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V13DT34A021, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-103906
https://doi.org/10.1115/GT2023-103906
View Paper titled, Adapting Artificial Neural Networks Training Algorithms to Adjoint-Based Aerodynamic Shape Optimization
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Topics: Algorithms, Artificial neural networks, Shape optimization, Design, Pressure, Blades, Computation, Artificial intelligence, Columns (Structural), Construction
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
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-82915
https://doi.org/10.1115/GT2022-82915
View Paper titled, Statistical Rule Extraction for Gas Turbine Trip Prediction
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Topics: Gas turbines, Artificial intelligence, Computers, Industrial gases, Machine learning, Statistical analysis, Turbines
Proceedings Papers

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

Georgios 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
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-80777
https://doi.org/10.1115/GT2022-80777
View Paper titled, Explainable and Interpretable AI-Assisted Remaining Useful Life Estimation for Aeroengines
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Topics: Service life (Equipment), Maintenance, Algorithms, Artificial intelligence, Artificial neural networks, Cycles, Databases, Engines, Feature selection, Flight
Proceedings Papers

Automatized Experimental Combustor Development Using Adaptive Surrogate Model-Based Optimization

Johann Moritz Reumschüssel, Philipp Maximilian zur Nedden, Jakob G. R. von Saldern, Thoralf G. Reichel, Bernhard Ćosić, Christian Oliver Paschereit
Proc. ASME. GT2022, Volume 3A: Combustion, Fuels, and Emissions, V03AT04A048, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-81756
https://doi.org/10.1115/GT2022-81756
View Paper titled, Automatized Experimental Combustor Development Using Adaptive Surrogate Model-Based Optimization
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Topics: Combustion chambers, Optimization, Flames, Emissions, Fuels, Gas turbines, Nitrogen oxides, Stability, Valves, Artificial intelligence
Proceedings Papers

Using Engineering Enhanced AI to Forecast Combined Cycle Power Plant Performance in the Presence of Uncertain Weather Conditions

Christopher A. Perullo, Lea Boche, Alex Redling, Jamie Lim, Woosung Choi, Timothy C. Lieuwen, David Noble
Proc. ASME. GT2022, Volume 5: Education; Electric Power; Fans and Blowers, V005T09A008, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-82718
https://doi.org/10.1115/GT2022-82718
View Paper titled, Using Engineering Enhanced AI to Forecast Combined Cycle Power Plant Performance in the Presence of Uncertain Weather Conditions
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Topics: Combined cycle power stations, Modeling, Power stations, Uncertainty, Artificial intelligence, Combined cycles, Economics , Energy generation, Gas turbines, Heat
Proceedings Papers

An Artificial Intelligence Based Method for Performance Prediction and Inverse Design of Hydraulic Turbochargers

Azam Thatte, Ganesh Vurimi, Prabhav Borate, Teymour Javaherchi
Proc. ASME. GT2020, Volume 8: Industrial and Cogeneration; Manufacturing Materials and Metallurgy; Marine; Microturbines, Turbochargers, and Small Turbomachines, V008T20A027, September 21–25, 2020
Publisher: American Society of Mechanical Engineers
Paper No: GT2020-16012
https://doi.org/10.1115/GT2020-16012
View Paper titled, An Artificial Intelligence Based Method for Performance Prediction and Inverse Design of Hydraulic Turbochargers
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Topics: Artificial intelligence, Design, Turbochargers, Turbines, Pumps, Algorithms, Turbomachinery, Artificial neural networks, Blades, Flow (Dynamics)
Proceedings Papers

Building Digital Twins to Simulate Manufacturing Variation

Shahrokh Shahpar
Proc. ASME. GT2020, Volume 2A: Turbomachinery, V02AT32A049, September 21–25, 2020
Publisher: American Society of Mechanical Engineers
Paper No: GT2020-15263
https://doi.org/10.1115/GT2020-15263
View Paper titled, Building Digital Twins to Simulate Manufacturing Variation
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Topics: Design, Manufacturing, Geometry, Simulation, Aircraft, Artificial intelligence, Cloud computing, Computerized tomography, Coordinate measuring machines, Engineering design
Proceedings Papers

A Multi-Dimensional Extension of Balje Chart for Axial Flow Turbomachinery Using Artificial Intelligence Based Meta-Models

Gino Angelini, Alessandro Corsini, Giovanni Delibra, Lorenzo Tieghi
Proc. ASME. GT2019, Volume 1: Aircraft Engine; Fans and Blowers; Marine; Honors and Awards, V001T09A009, June 17–21, 2019
Publisher: American Society of Mechanical Engineers
Paper No: GT2019-91588
https://doi.org/10.1115/GT2019-91588
View Paper titled, A Multi-Dimensional Extension of Balje Chart for Axial Flow Turbomachinery Using Artificial Intelligence Based Meta-Models
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Topics: Artificial intelligence, Axial flow, Blades, Computer software, Design, Kinematics, Machine learning, Principal component analysis, Rotors, Turbomachinery
Proceedings Papers

A Feasibility Study of the Application of Artificial Intelligence Techniques for Turbomachinery Diagnostics

Cyrus B. Meher-Homji
Proc. ASME. GT1985, Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education; Process Industries, V004T14A003, March 18–21, 1985
Publisher: American Society of Mechanical Engineers
Paper No: 85-GT-102
https://doi.org/10.1115/85-GT-102
View Paper titled, A Feasibility Study of the Application of Artificial Intelligence Techniques for Turbomachinery Diagnostics
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Topics: Artificial intelligence, Turbomachinery, Expert systems, Computers
Proceedings Papers

Initial User Experience With an Artificial Intelligence Program for the Preliminary Design of Centrifugal Compressors

Carol J. Russo, Dennis J. Nicklaus, Siu S. Tong
Proc. ASME. GT1987, Volume 1: Turbomachinery, V001T01A075, May 31–June 4, 1987
Publisher: American Society of Mechanical Engineers
Paper No: 87-GT-217
https://doi.org/10.1115/87-GT-217
View Paper titled, Initial User Experience With an Artificial Intelligence Program for the Preliminary Design of Centrifugal Compressors
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Topics: Artificial intelligence, Compressors, Design, Tradeoffs, Computer software, Turbomachinery
Proceedings Papers

The Role for Expert Systems in Commercial Gas Turbine Engine Monitoring

David L. Doel
Proc. ASME. GT1990, Volume 5: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; General, V005T15A014, June 11–14, 1990
Publisher: American Society of Mechanical Engineers
Paper No: 90-GT-374
https://doi.org/10.1115/90-GT-374
View Paper titled, The Role for Expert Systems in Commercial Gas Turbine Engine Monitoring
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Topics: Expert systems, Gas turbines, Computer software, Condition monitoring, Algorithms, Artificial intelligence, Sensors
Proceedings Papers

Turbine Preliminary Design Using Artificial Intelligence and Numerical Optimization Techniques

Siu Shing Tong, Brent A. Gregory
Proc. ASME. GT1990, Volume 5: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; General, V005T16A006, June 11–14, 1990
Publisher: American Society of Mechanical Engineers
Paper No: 90-GT-148
https://doi.org/10.1115/90-GT-148
View Paper titled, Turbine Preliminary Design Using Artificial Intelligence and Numerical Optimization Techniques
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Topics: Artificial intelligence, Design, Optimization, Turbines, Aircraft engines, Computer software, Shells, System architecture
Proceedings Papers

Preliminary Design of Axial Compressors Using Artificial Intelligence and Numerical Optimization Techniques

George Holt, Stuart Bassler
Proc. ASME. GT1991, Volume 1: Turbomachinery, V001T01A100, June 3–6, 1991
Publisher: American Society of Mechanical Engineers
Paper No: 91-GT-334
https://doi.org/10.1115/91-GT-334
View Paper titled, Preliminary Design of Axial Compressors Using Artificial Intelligence and Numerical Optimization Techniques
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Topics: Artificial intelligence, Compressors, Design, Optimization, Computer software, Expert systems, FORTRAN, Shapes, Shells, Turbomachinery
Proceedings Papers

A Diagnostic Expert System for Oil Systems of Aeroengines

Hong Cao, Mei Ma
Proc. ASME. GT1993, Volume 3C: General, V03CT17A010, May 24–27, 1993
Publisher: American Society of Mechanical Engineers
Paper No: 93-GT-349
https://doi.org/10.1115/93-GT-349
View Paper titled, A Diagnostic Expert System for Oil Systems of Aeroengines
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Topics: Expert systems, Engines, Failure, Aircraft, Artificial intelligence, Design, Gas turbines, Uncertainty
Proceedings Papers

Optimization of a Transonic Turbine Airfoil Using Artificial Intelligence, CFD and Cascade Testing

M. L. Shelton, B. A. Gregory, S. H. Lamson, H. L. Moses, R. L. Doughty, T. Kiss
Proc. ASME. GT1993, Volume 3A: General, V03AT15A012, May 24–27, 1993
Publisher: American Society of Mechanical Engineers
Paper No: 93-GT-161
https://doi.org/10.1115/93-GT-161
View Paper titled, Optimization of a Transonic Turbine Airfoil Using Artificial Intelligence, CFD and Cascade Testing
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Topics: Airfoils, Artificial intelligence, Cascades (Fluid dynamics), Computational fluid dynamics, Optimization, Testing, Turbines, Design, Shells, Blades
Proceedings Papers

Field Experience Versus Theory in Turbine Engine Deterioration

James L. Pettigrew
Proc. ASME. GT1995, Volume 2: Aircraft Engine; Marine; Microturbines and Small Turbomachinery, V002T02A008, June 5–8, 1995
Publisher: American Society of Mechanical Engineers
Paper No: 95-GT-161
https://doi.org/10.1115/95-GT-161
View Paper titled, Field Experience Versus Theory in Turbine Engine Deterioration
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Topics: Army, Artificial intelligence, Databases, Energy generation, Engines, Gas turbines, Generators, Instrumentation, Turbines
Proceedings Papers

The Negative Information Problem in Mechanical Diagnostics

David L. Hall, Robert J. Hansen, Derek C. Lang
Proc. ASME. GT1996, Volume 5: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education; General, V005T15A007, June 10–13, 1996
Publisher: American Society of Mechanical Engineers
Paper No: 96-GT-035
https://doi.org/10.1115/96-GT-035
View Paper titled, The Negative Information Problem in Mechanical Diagnostics
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Topics: Acoustics, Artificial intelligence, Artificial neural networks, Design, Emissions, Engineering disciplines, Expert systems, Failure, Fuzzy logic, Gas turbines
Proceedings Papers

Use of Artificial Neural Networks for the Simulation of Combined Cycle Transients

Umberto Desideri, Francesco Fantozzi, Gianni Bidini, Philippe Mathieu
Proc. ASME. GT1997, Volume 2: Coal, Biomass and Alternative Fuels; Combustion and Fuels; Oil and Gas Applications; Cycle Innovations, V002T08A015, June 2–5, 1997
Publisher: American Society of Mechanical Engineers
Paper No: 97-GT-442
https://doi.org/10.1115/97-GT-442
View Paper titled, Use of Artificial Neural Networks for the Simulation of Combined Cycle Transients
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Topics: Artificial neural networks, Combined cycles, Simulation, Transients (Dynamics), Heat recovery steam generators, Monitoring systems, Stress, Artificial intelligence, Carbon capture and storage, Cascades (Fluid dynamics)