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Proceedings Papers

Anomaly Detection for Large Fleets of Industrial Equipment: Utilizing Machine Learning With Applications to Power Plant Monitoring

Cody W. Allen, Chad Holcomb, Maurício de Oliveira
Proc. ASME. GT2021, Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power, V004T05A016, June 7–11, 2021
Publisher: American Society of Mechanical Engineers
Paper No: GT2021-60116
https://doi.org/10.1115/GT2021-60116
View Paper titled, Anomaly Detection for Large Fleets of Industrial Equipment: Utilizing Machine Learning With Applications to Power Plant Monitoring
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Topics: Machine learning, Power stations, Machinery, Gas turbines, Algorithms, Errors, Failure, Model development, Neural network models, Reliability
Proceedings Papers

Fast Model Predictive Control for Aircraft Engine Based on Automatic Differentiation

Yunlai Wang, Xi Wang
Proc. ASME. GT2020, Volume 5: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage, V005T05A032, September 21–25, 2020
Publisher: American Society of Mechanical Engineers
Paper No: GT2020-16161
https://doi.org/10.1115/GT2020-16161
View Paper titled, Fast Model Predictive Control for Aircraft Engine Based on Automatic Differentiation
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Topics: Aircraft engines, Predictive control, Jacobian matrices, Algorithms, Control systems, Dynamics (Mechanics), Matlab, Neural network models, Simulation results
Proceedings Papers

An Assessment of Machine Learning Techniques for Predicting Turbine Airfoil Component Temperatures, Using FEA Simulations for Training Data

James A. Tallman, Michal Osusky, Nick Magina, Evan Sewall
Proc. ASME. GT2019, Volume 5A: Heat Transfer, V05AT20A002, June 17–21, 2019
Publisher: American Society of Mechanical Engineers
Paper No: GT2019-91004
https://doi.org/10.1115/GT2019-91004
View Paper titled, An Assessment of Machine Learning Techniques for Predicting Turbine Airfoil Component Temperatures, Using FEA Simulations for Training Data
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Topics: Airfoils, Engineering simulation, Finite element analysis, Machine learning, Simulation, Temperature, Turbines, Algorithms, Artificial neural networks, Design
Proceedings Papers

A Comparative Study of Data and Physically Based Gas Turbine Modeling for Long-Term Monitoring Scenarios: Part I — Thermodynamic Performance Prediction Without Design Information

Thomas Bexten, Moritz Lipperheide, Manfred Wirsum, Liu Pei, Li Zheng
Proc. ASME. GT2018, Volume 6: Ceramics; Controls, Diagnostics, and Instrumentation; Education; Manufacturing Materials and Metallurgy, V006T05A022, June 11–15, 2018
Publisher: American Society of Mechanical Engineers
Paper No: GT2018-76630
https://doi.org/10.1115/GT2018-76630
View Paper titled, A Comparative Study of Data and Physically Based Gas Turbine Modeling for Long-Term Monitoring Scenarios: Part I — Thermodynamic Performance Prediction Without Design Information
Open the PDF for A Comparative Study of Data and Physically Based Gas Turbine Modeling for Long-Term Monitoring Scenarios: Part I — Thermodynamic Performance Prediction Without Design Information in another window
Topics: Design, Gas turbines, Modeling, Stress, Boundary-value problems, Combined cycle power stations, Heat, Multilayer perceptron, Neural network models, Renewable energy
Proceedings Papers

Neural Network Modeling of Turbofan Parameters

Mark J. Embrechts, Aaron L. Schweizerhof, Mark Bushman, Mike H. Sabatella
Proc. ASME. GT2000, Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education, V004T04A008, May 8–11, 2000
Publisher: American Society of Mechanical Engineers
Paper No: 2000-GT-0036
https://doi.org/10.1115/2000-GT-0036
View Paper titled, Neural Network Modeling of Turbofan Parameters
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Topics: Artificial neural networks, Modeling, Turbofans, Neural network models, Engines, Flight, Errors, Algorithms, Architecture, Data collection
Proceedings Papers

Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks

Jason K. Ostanek
Proc. ASME. GT2013, Volume 3A: Heat Transfer, V03AT12A053, June 3–7, 2013
Publisher: American Society of Mechanical Engineers
Paper No: GT2013-95903
https://doi.org/10.1115/GT2013-95903
View Paper titled, Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks
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Topics: Artificial neural networks, Heat transfer, Reynolds number, Ducts, Neural network models, Rotation, Shapes, Uncertainty
Proceedings Papers

Numerical Analysis of Film-Cooling Performance and Optimization for a Novel Shaped Film-Cooling Hole

Ki-Don Lee, Sun-Min Kim, Kwang-Yong Kim
Proc. ASME. GT2012, Volume 4: Heat Transfer, Parts A and B, 1345-1355, June 11–15, 2012
Publisher: American Society of Mechanical Engineers
Paper No: GT2012-68529
https://doi.org/10.1115/GT2012-68529
View Paper titled, Numerical Analysis of Film-Cooling Performance and Optimization for a Novel Shaped Film-Cooling Hole
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Topics: Film cooling, Numerical analysis, Optimization, Coolants, Cooling, Density, Design, Diffusers, Geometry, Neural network models
Proceedings Papers

Automated Preliminary Structural Rotor Design Using Genetic Algorithms and Neural Networks

S. Dominique, J.-Y. Tre´panier
Proc. ASME. GT2008, Volume 6: Turbomachinery, Parts A, B, and C, 2555-2567, June 9–13, 2008
Publisher: American Society of Mechanical Engineers
Paper No: GT2008-51181
https://doi.org/10.1115/GT2008-51181
View Paper titled, Automated Preliminary Structural Rotor Design Using Genetic Algorithms and Neural Networks
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Topics: Artificial neural networks, Design, Genetic algorithms, Rotors, Algorithms, Aerodynamics, Blades, Cycles, Disks, Gas turbines
Proceedings Papers

Optimum Gain-Scheduling PID Controllers for Gas Turbine Engines Based on NARMAX and Neural Network Models

Junxia Mu, David Rees, Neophytos Chiras
Proc. ASME. GT2003, Volume 1: Turbo Expo 2003, 509-515, June 16–19, 2003
Publisher: American Society of Mechanical Engineers
Paper No: GT2003-38667
https://doi.org/10.1115/GT2003-38667
View Paper titled, Optimum Gain-Scheduling PID Controllers for Gas Turbine Engines Based on NARMAX and Neural Network Models
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Topics: Control equipment, Feedforward control, Gain scheduling, Gas turbines, Neural network models
Proceedings Papers

Sensor Validation and Recovery for Combustion Turbines in Electrical Power Generation

Michael H. Schoeller, Rolf Orsagh, Michael Roemer, Leonard C. Angello
Proc. ASME. GT2004, Volume 5: Turbo Expo 2004, Parts A and B, 71-80, June 14–17, 2004
Publisher: American Society of Mechanical Engineers
Paper No: GT2004-54079
https://doi.org/10.1115/GT2004-54079
View Paper titled, Sensor Validation and Recovery for Combustion Turbines in Electrical Power Generation
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Topics: Combustion, Electric power generation, Sensors, Turbines, Artificial neural networks, Computer software, Flaw detection, Maintainability, Neural network models, Reliability
Proceedings Papers

Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models

M. Venturini
Proc. ASME. GT2005, Volume 4: Turbo Expo 2005, 255-266, June 6–9, 2005
Publisher: American Society of Mechanical Engineers
Paper No: GT2005-68030
https://doi.org/10.1115/GT2005-68030
View Paper titled, Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models
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Topics: Compressors, Neural network models, Simulation, Transients (Dynamics), Errors, Physics, Pressure, Algorithms, Artificial neural networks, Delays
Proceedings Papers

Neural Network Models for Usage Based Remaining Life Computation

Girija Parthasarathy, Sunil Menon, Kurt Richardson, Ahsan Jameel, Dawn McNamee, Tori Desper, Michael Gorelik, Chris Hickenbottom
Proc. ASME. GT2006, Volume 5: Marine; Microturbines and Small Turbomachinery; Oil and Gas Applications; Structures and Dynamics, Parts A and B, 995-1002, May 8–11, 2006
Publisher: American Society of Mechanical Engineers
Paper No: GT2006-91099
https://doi.org/10.1115/GT2006-91099
View Paper titled, Neural Network Models for Usage Based Remaining Life Computation
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Topics: Computation, Neural network models, Stress, Temperature, Flight, Design, Engines, Compressors, Cycles, Damage