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Proceedings Papers

Building Digital Twins to Simulate Manufacturing Variation

Shahrokh Shahpar

Proc. ASME. GT2020, Volume 2A: Turbomachinery, V02AT32A049, September 21–25, 2020

Paper No: GT2020-15263

DOI: https://doi.org/10.1115/GT2020-15263

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Topics: Design, Manufacturing, Geometry, Simulation, Aircraft, Artificial intelligence, Cloud computing, Computerized tomography, Coordinate measuring machines, Engineering design

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

Paper No: GT2020-16012

DOI: https://doi.org/10.1115/GT2020-16012

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Topics: Artificial intelligence, Design, Turbochargers, Turbines, Pumps, Algorithms, Turbomachinery, Artificial neural networks, Blades, Flow (Dynamics)

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

Paper No: GT2019-91588

DOI: https://doi.org/10.1115/GT2019-91588

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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

Paper No: 85-GT-102

DOI: https://doi.org/10.1115/85-GT-102

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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

Paper No: 87-GT-217

DOI: https://doi.org/10.1115/87-GT-217

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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

Paper No: 90-GT-374

DOI: https://doi.org/10.1115/90-GT-374

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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

Paper No: 90-GT-148

DOI: https://doi.org/10.1115/90-GT-148

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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

Paper No: 91-GT-334

DOI: https://doi.org/10.1115/91-GT-334

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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

Paper No: 93-GT-349

DOI: https://doi.org/10.1115/93-GT-349

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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

Paper No: 93-GT-161

DOI: https://doi.org/10.1115/93-GT-161

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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

Paper No: 95-GT-161

DOI: https://doi.org/10.1115/95-GT-161

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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

Paper No: 96-GT-035

DOI: https://doi.org/10.1115/96-GT-035

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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

Paper No: 97-GT-442

DOI: https://doi.org/10.1115/97-GT-442

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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)

Proceedings Papers

The Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics

Hans R. DePold, F. Douglas Gass

Proc. ASME. GT1998, Volume 5: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education, V005T15A009, June 2–5, 1998

Paper No: 98-GT-101

DOI: https://doi.org/10.1115/98-GT-101

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Topics: Artificial neural networks, Expert systems, Gas turbines, Engines, Condition monitoring, Maintenance, Artificial intelligence, Collaboration, Decision making, Filters

Proceedings Papers

ICEMS: Intelligent Condition-Based Engine/Equipment Management System (www.icems.com)

Link Jaw, Yu-tsung (Jim) Wang, Richard Friend

Proc. ASME. GT2001, Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education; IGTI Scholar Award, V004T04A008, June 4–7, 2001

Paper No: 2001-GT-0015

DOI: https://doi.org/10.1115/2001-GT-0015

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Topics: Engines, Air Force, Machinery, Algorithms, Artificial intelligence, Computer software, Condition monitoring, Failure, Gas turbines, Maintenance

Proceedings Papers

Advanced Gas Turbine Diagnostics Using Pattern Recognition

Shintaro Kumano, Naotaka Mikami, Kuniaki Aoyama

Proc. ASME. GT2011, Volume 3: Controls, Diagnostics and Instrumentation; Education; Electric Power; Microturbines and Small Turbomachinery; Solar Brayton and Rankine Cycle, 179-187, June 6–10, 2011

Paper No: GT2011-45670

DOI: https://doi.org/10.1115/GT2011-45670

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Topics: Gas turbines, Pattern recognition, Artificial intelligence, Energy generation, Maintenance, Reliability, Clocks, Engineers, Inspection, Instrumentation

Proceedings Papers

A Comprehensive High Frequency Vibration Monitoring System for Incipient Fault Detection and Isolation of Gears, Bearings and Shafts/Couplings in Turbine Engines and Accessories

Matt Watson, Jeremy Sheldon, Sanket Amin, Hyungdae Lee, Carl Byington, Michael Begin

Proc. ASME. GT2007, Volume 5: Turbo Expo 2007, 885-894, May 14–17, 2007

Paper No: GT2007-27660

DOI: https://doi.org/10.1115/GT2007-27660

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Topics: Bearings, Couplings, Flaw detection, Gas turbines, Gears, Vibration monitors, Vibration, Algorithms, Machinery, Sensors

Proceedings Papers

Gas Turbine Health Indices Determination by Using Neural Networks

R. Bettocchi, P. R. Spina, G. Torella

Proc. ASME. GT2002, Volume 2: Turbo Expo 2002, Parts A and B, 1083-1089, June 3–6, 2002

Paper No: GT2002-30276

DOI: https://doi.org/10.1115/GT2002-30276

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Topics: Artificial neural networks, Gas turbines, Cycles, Artificial intelligence, Generators, Industrial gases, Robustness, Testing, Trains, Turbines

Proceedings Papers

Upgrading Engine Test Cells for Improved Troubleshooting and Diagnostics

Michael J. Roemer, Rolf F. Orsagh, Gregory J. Kacprzynski, James Scheid, Richard Friend, William Sotomayer

Proc. ASME. GT2002, Volume 2: Turbo Expo 2002, Parts A and B, 135-143, June 3–6, 2002

Paper No: GT2002-30034

DOI: https://doi.org/10.1115/GT2002-30034

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Topics: Engines, Algorithms, Air Force, Aircraft, Artificial intelligence, Computer software, Maintenance, Military systems, Probability, Sensors

Proceedings Papers

Gas Path Fault Diagnosis of a Turbofan Engine From Transient Data Using Artificial Neural Networks

S. O. T. Ogaji, Y. G. Li, S. Sampath, R. Singh

Proc. ASME. GT2003, Volume 1: Turbo Expo 2003, 405-414, June 16–19, 2003

Paper No: GT2003-38423

DOI: https://doi.org/10.1115/GT2003-38423

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Topics: Artificial neural networks, Engines, Fault diagnosis, Transients (Dynamics), Turbofans, Sensors, Compressors, Gas turbines, High pressure (Physics), Pressure

NARROW

Building Digital Twins to Simulate Manufacturing Variation

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

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

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

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

The Role for Expert Systems in Commercial Gas Turbine Engine Monitoring

Turbine Preliminary Design Using Artificial Intelligence and Numerical Optimization Techniques

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

A Diagnostic Expert System for Oil Systems of Aeroengines

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

Field Experience Versus Theory in Turbine Engine Deterioration

The Negative Information Problem in Mechanical Diagnostics

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

The Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics

ICEMS: Intelligent Condition-Based Engine/Equipment Management System (www.icems.com)

Advanced Gas Turbine Diagnostics Using Pattern Recognition

A Comprehensive High Frequency Vibration Monitoring System for Incipient Fault Detection and Isolation of Gears, Bearings and Shafts/Couplings in Turbine Engines and Accessories

Gas Turbine Health Indices Determination by Using Neural Networks

Upgrading Engine Test Cells for Improved Troubleshooting and Diagnostics

Gas Path Fault Diagnosis of a Turbofan Engine From Transient Data Using Artificial Neural Networks

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