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Proceedings Papers

Undergraduate Hypersonics Research: Lessons From Two Years of the REU Site HYPER

Jeffrey L. Kauffman, Ali P. Gordon

Proc. ASME. GT2022, Volume 5: Education; Electric Power; Fans and Blowers, V005T08A002, June 13–17, 2022

Paper No: GT2022-81021

DOI: https://doi.org/10.1115/GT2022-81021

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Topics: Undergraduate students, Students, Mentoring, Aerospace engineering, Career development, Engineers, Graduate students, Trains, Transportation systems, Turbomachinery

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

Nonlinear Dynamics of Turbine Generator Shaft Trains: Evaluation of Bifurcation Sets Applying Numerical Continuation

Ioannis Gavalas, Athanasios Chasalevris

Proc. ASME. GT2022, Volume 8B: Structures and Dynamics — Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V08BT26A005, June 13–17, 2022

Paper No: GT2022-80107

DOI: https://doi.org/10.1115/GT2022-80107

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Topics: Bifurcation, Nonlinear dynamics, Trains, Turbogenerators

Proceedings Papers

Reducing Uncertainty in the Onset of Combustion Instabilities Using Dynamic Pressure Information and Bayesian Neural Networks

Michael McCartney, Ushnish Sengupta, Matthew Juniper

Proc. ASME. GT2021, Volume 3B: Combustion, Fuels, and Emissions, V03BT04A042, June 7–11, 2021

Paper No: GT2021-60283

DOI: https://doi.org/10.1115/GT2021-60283

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Topics: Artificial neural networks, Combustion, Pressure, Uncertainty, Hidden variables, Combustion chambers, Combustion systems, Emissions, Errors, Wall temperature

Proceedings Papers

Data-Driven Analysis of Engine Mission Severity Using Non-Dimensional Groups

Tim Brandes, Stefano Scarso, Christian Koch, Stephan Staudacher

Proc. ASME. GT2021, Volume 2B: Turbomachinery — Axial Flow Turbine Aerodynamics; Deposition, Erosion, Fouling, and Icing, V02BT33A002, June 7–11, 2021

Paper No: GT2021-58673

DOI: https://doi.org/10.1115/GT2021-58673

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Topics: Engines, Flight, Machinery, Accuracy and precision, Algorithms, Blades, Compressors, Computation, Databases, Erosion

Proceedings Papers

Alarms, Shutdowns and Trip Rationalization

Rasidi Mohamed, Syafeq Moazari Sukeri, Robert Mendoza, Rainer Kurz

Proc. ASME. GT2021, Volume 8: Oil and Gas Applications; Steam Turbine, V008T20A001, June 7–11, 2021

Paper No: GT2021-00646

DOI: https://doi.org/10.1115/GT2021-00646

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Topics: Compressors, Control algorithms, Control systems, Damage, Downtime, Gas turbines, Instrumentation, Machinery, Profitability, Trains

Proceedings Papers

Gaussian Stochastic Process Modeling of Blend Repaired Airfoil Modal Response Using Reduced Basis Mode Shape Approach

Jeffrey M. Brown, Alex A. Kaszynski, Daniel L. Gillaugh, Emily B. Carper, Joseph A. Beck

Proc. ASME. GT2021, Volume 9B: Structures and Dynamics — Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V09BT29A028, June 7–11, 2021

Paper No: GT2021-60238

DOI: https://doi.org/10.1115/GT2021-60238

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Topics: Airfoils, Mode shapes, Modeling, Stochastic processes, Modal assurance criterion, Maintenance, Displacement, Geometry, Design, Finite element model

Proceedings Papers

Performance and Emission Assessment of Thermo-Electric Power Plant for Rotorcraft Propulsion

Ioannis Roumeliotis, Francesco Arena, Yize Liu, Stavros Vouros, Vassilios Pachidis, Olivier Broca, Djiby Toure, Deniz Unlu

Proc. ASME. GT2020, Volume 5: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage, V005T06A031, September 21–25, 2020

Paper No: GT2020-15724

DOI: https://doi.org/10.1115/GT2020-15724

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Topics: Emissions, Power stations, Propulsion, Electricity (Physics), Nitrogen oxides, Trains, Engines, Gas turbines, Propulsion systems, Carbon dioxide

Proceedings Papers

Integrated Systems Simulation for Assessing Fuel Thermal Management Capabilities for Hybrid-Electric Rotorcraft

Ioannis Roumeliotis, Lorenzo Castro, Soheil Jafari, Vassilios Pachidis, Louis De Riberolles, Olivier Broca, Deniz Unlu

Proc. ASME. GT2020, Volume 5: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage, V005T06A011, September 21–25, 2020

Paper No: GT2020-15107

DOI: https://doi.org/10.1115/GT2020-15107

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Topics: Fuels, Integrated systems, Simulation, Thermal management, Propulsion systems, Cooling, Stress, Fuel storage, Air travel, Aircraft

Proceedings Papers

A Degradation Diagnosis Method for Gas Turbine – Fuel Cell Hybrid Systems Using Bayesian Networks

L. Mantelli, V. Zaccaria, K. Kyprianidis, M. L. Ferrari

Proc. ASME. GT2020, Volume 5: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage, V005T06A037, September 21–25, 2020

Paper No: GT2020-16203

DOI: https://doi.org/10.1115/GT2020-16203

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Topics: Fuel cells, Gas turbines, Solid oxide fuel cells, Sensors, Maintenance, Turbomachinery, Carbon, Compressors, Control systems, Electricity (Physics)

Proceedings Papers

Detecting Nonsynchronous Vibration in Transonic Fans Using Machine Learning Techniques

Yaozhi Lu, Mehdi Vahdati

Proc. ASME. GT2020, Volume 2D: Turbomachinery, V02DT38A002, September 21–25, 2020

Paper No: GT2020-14261

DOI: https://doi.org/10.1115/GT2020-14261

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Topics: Fans, Machine learning, Vibration, Blades, Geometry, Manufacturing, High cycle fatigue, Stress, Switches, Trains

Proceedings Papers

Prediction of Non-Linear Mechanical Behavior With Deep Neural Network: Application on Low Pressure Turbine Disc

Yuan Jin, Weichen Li, Zheyi Yang, Olivier Jung

Proc. ASME. GT2020, Volume 2D: Turbomachinery, V02DT38A005, September 21–25, 2020

Paper No: GT2020-14382

DOI: https://doi.org/10.1115/GT2020-14382

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Topics: Artificial neural networks, Disks, Mechanical behavior, Pressure, Turbines, Constitutive equations, Displacement, Design, Engineering simulation, Elastoplasticity

Proceedings Papers

Matching of Synchronous Motors and Centrifugal Compressors: Oil and Gas Industry Practice

Matt Taher, Dragan Ristanovic, Cyrus Meher-Homji, Pradeep Pillai

Proc. ASME. GT2020, Volume 9: Oil and Gas Applications; Organic Rankine Cycle Power Systems; Steam Turbine, V009T21A015, September 21–25, 2020

Paper No: GT2020-15643

DOI: https://doi.org/10.1115/GT2020-15643

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Topics: Compressors, Motors, Petroleum industry, American Petroleum Institute, Design, Trains, Capacitors, Engine design, Optimization, Plant design

Proceedings Papers

Identification of Losses in Turbomachinery With Machine Learning

Gino Angelini, Alessandro Corsini, Giovanni Delibra, Marco Giovannelli

Proc. ASME. GT2020, Volume 1: Aircraft Engine; Fans and Blowers, V001T10A008, September 21–25, 2020

Paper No: GT2020-15337

DOI: https://doi.org/10.1115/GT2020-15337

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Topics: Machine learning, Turbomachinery, Cascades (Fluid dynamics), Experimental design, Flow (Dynamics), Algorithms, Artificial neural networks, Boundary layers, Computational fluid dynamics, Design

Proceedings Papers

Analytical Model for the Heat Transfer in Impingement Cooled Spur Gears

Christian Kromer, Felix C. von Plehwe, Marc C. Keller, Corina Schwitzke, Hans-Jörg Bauer

Proc. ASME. GT2020, Volume 1: Aircraft Engine; Fans and Blowers, V001T01A023, September 21–25, 2020

Paper No: GT2020-14760

DOI: https://doi.org/10.1115/GT2020-14760

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Topics: Heat transfer, Spur gears, Gear teeth, Gear trains, Heat, Impingement cooling, Jet engines, Pressure, Turbines, Turbofans

Proceedings Papers

Application of Artificial Neural Networks for Monitoring and Optimum Operation Prediction of Solar Hybrid MGT Systems

Homam Nikpey Somehsaraei, Davide Iaria, Jafar Al Zaili, Mohsen Assadi, Abdulnaser Sayma, Mohsen Ghavami

Proc. ASME. GT2019, Volume 3: Coal, Biomass, Hydrogen, and Alternative Fuels; Cycle Innovations; Electric Power; Industrial and Cogeneration; Organic Rankine Cycle Power Systems, V003T06A011, June 17–21, 2019

Paper No: GT2019-91180

DOI: https://doi.org/10.1115/GT2019-91180

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Topics: Artificial neural networks, Solar energy, Combined heat and power, Distributed power generation, Micro gas turbines, Electrical efficiency, Genetic algorithms, Irradiation (Radiation exposure), Modeling, Optimization algorithms

Proceedings Papers

Assessment of Thermo-Electric Power Plants for Rotorcraft Application

Ioannis Roumeliotis, Christos Mourouzidis, Mirko Zafferetti, Vassilios Pachidis, Olivier Broca, Deniz Unlu

Paper No: GT2019-91481

DOI: https://doi.org/10.1115/GT2019-91481

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Topics: Power stations, Engines, Cycles, Gas turbines, Heat exchangers, Electricity (Physics), Transients (Dynamics), Weight (Mass), Corporate average fuel economy, Fuel efficiency

Proceedings Papers

Multi-Fidelity Global-Local Optimization of a Transonic Compressor Rotor

Sudeepta Mondal, Michael M. Joly, Soumalya Sarkar

Proc. ASME. GT2019, Volume 2D: Turbomachinery, V02DT46A020, June 17–21, 2019

Paper No: GT2019-91778

DOI: https://doi.org/10.1115/GT2019-91778

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Topics: Compressors, Optimization, Rotors, Design, Reynolds-averaged Navier–Stokes equations, Space, Thread, Algorithms, Computational fluid dynamics, Machine learning

Proceedings Papers

Application of the Nozzle Analogy to Easily Predict Steam Turbine Performance With Minimal Geometric Input

W. F. Fuls, J. S. Clark, A. C. Marx

Proc. ASME. GT2019, Volume 8: Microturbines, Turbochargers, and Small Turbomachines; Steam Turbines, V008T29A019, June 17–21, 2019

Paper No: GT2019-90891

DOI: https://doi.org/10.1115/GT2019-90891

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Topics: Nozzles, Steam turbines, Turbines, Blades, Pressure, Boilers, Computational fluid dynamics, Computer software, Feedwater, Geometry

Proceedings Papers

Application Concepts and Experimental Validation of Constitutive Material Models for Creep-Fatigue Assessment of Components

Christian Kontermann, Stefan Linn, Matthias Oechsner

Proc. ASME. GT2019, Volume 8: Microturbines, Turbochargers, and Small Turbomachines; Steam Turbines, V008T29A026, June 17–21, 2019

Paper No: GT2019-91450

DOI: https://doi.org/10.1115/GT2019-91450

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Topics: Creep, Fatigue, Cycles, Design, Machine components, Machine learning, Model development, Trains, Turbochargers

NARROW

Undergraduate Hypersonics Research: Lessons From Two Years of the REU Site HYPER

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

Nonlinear Dynamics of Turbine Generator Shaft Trains: Evaluation of Bifurcation Sets Applying Numerical Continuation

Reducing Uncertainty in the Onset of Combustion Instabilities Using Dynamic Pressure Information and Bayesian Neural Networks

Data-Driven Analysis of Engine Mission Severity Using Non-Dimensional Groups

Alarms, Shutdowns and Trip Rationalization

Gaussian Stochastic Process Modeling of Blend Repaired Airfoil Modal Response Using Reduced Basis Mode Shape Approach

Performance and Emission Assessment of Thermo-Electric Power Plant for Rotorcraft Propulsion

Integrated Systems Simulation for Assessing Fuel Thermal Management Capabilities for Hybrid-Electric Rotorcraft

A Degradation Diagnosis Method for Gas Turbine – Fuel Cell Hybrid Systems Using Bayesian Networks

Detecting Nonsynchronous Vibration in Transonic Fans Using Machine Learning Techniques

Prediction of Non-Linear Mechanical Behavior With Deep Neural Network: Application on Low Pressure Turbine Disc

Matching of Synchronous Motors and Centrifugal Compressors: Oil and Gas Industry Practice

Identification of Losses in Turbomachinery With Machine Learning

Analytical Model for the Heat Transfer in Impingement Cooled Spur Gears

Application of Artificial Neural Networks for Monitoring and Optimum Operation Prediction of Solar Hybrid MGT Systems

Assessment of Thermo-Electric Power Plants for Rotorcraft Application

Multi-Fidelity Global-Local Optimization of a Transonic Compressor Rotor

Application of the Nozzle Analogy to Easily Predict Steam Turbine Performance With Minimal Geometric Input

Application Concepts and Experimental Validation of Constitutive Material Models for Creep-Fatigue Assessment of Components

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