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Proceedings Papers

Bayesian Data Assimilation in Cold Flow Experiments on an Industrial Thermoacoustic Rig

Jingquan Zheng, André Fischer, Claus Lahiri, Matthew Yoko, Matthew Juniper
Proc. ASME. GT2024, Volume 3A: Combustion, Fuels, and Emissions, V03AT04A018, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-122656
https://doi.org/10.1115/GT2024-122656
View Paper titled, Bayesian Data Assimilation in Cold Flow Experiments on an Industrial Thermoacoustic Rig
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Topics: Flow (Dynamics), Acoustics, Combustion chambers, Emissions, Geometry, Network models, Optimization, Physics, Probability, Uncertainty
Proceedings Papers

Aero-Engine Remaining Useful Life Prediction via Physics-Informed Self-Attention Encoder

Xuanwu Zhang, Jiayue Lou, Jifa Zhang, Yao Zheng, Yifan Xia
Proc. ASME. GT2024, Volume 4: Controls, Diagnostics, and Instrumentation, V004T05A022, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-124893
https://doi.org/10.1115/GT2024-124893
View Paper titled, Aero-Engine Remaining Useful Life Prediction via Physics-Informed Self-Attention Encoder
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Topics: Aircraft engines, Physics, Service life (Equipment)
Proceedings Papers

Implementation of Artificial Intelligence for Aircraft Engine Health Monitoring and Prognostics

Aditya Aditya, Theoklis Nikolaidis, Arias Chao Manuel, Simone Togni
Proc. ASME. GT2024, Volume 4: Controls, Diagnostics, and Instrumentation, V004T05A030, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-127081
https://doi.org/10.1115/GT2024-127081
View Paper titled, Implementation of Artificial Intelligence for Aircraft Engine Health Monitoring and Prognostics
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Topics: Aircraft engines, Artificial intelligence, Condition monitoring, Deep learning, Engines, Convolutional neural networks, Cycles, Flight, Service life (Equipment), Aircraft
Proceedings Papers

Research on Performance Degradation Prediction Method of Heavy-Duty Gas Turbine Based on Data-Physics Fusion Under Uncertainty

Yiyang Liu, Xiaomo Jiang, Manman Wei, Xin Ge
Proc. ASME. GT2024, Volume 4: Controls, Diagnostics, and Instrumentation, V004T05A020, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-124704
https://doi.org/10.1115/GT2024-124704
View Paper titled, Research on Performance Degradation Prediction Method of Heavy-Duty Gas Turbine Based on Data-Physics Fusion Under Uncertainty
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Topics: Gas turbines, Physics, Uncertainty
Proceedings Papers

Predicting Gas Turbine Power Output in Single Shaft Combined Cycle Power Plants: A Physics-Based and Data Analytics Approach

Pugalenthi Nandagopal, Manjunath More, Kirti Sharma, Uttam Saroj, Gurdev Singh
Proc. ASME. GT2024, Volume 4: Controls, Diagnostics, and Instrumentation, V004T05A042, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-128650
https://doi.org/10.1115/GT2024-128650
View Paper titled, Predicting Gas Turbine Power Output in Single Shaft Combined Cycle Power Plants: A Physics-Based and Data Analytics Approach
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Topics: Combined cycle power stations, Cycles, Gas turbines, Physics
Proceedings Papers

Pilot-Scale Testing of a Transcritical CO 2 -Based Pumped Thermal Energy Storage (PTES) System

Timothy J. Held, Jason Miller, Jason Mallinak, Vamshi Avadhanula, Luke Magyar
Proc. ASME. GT2024, Volume 6: Education; Electric Power; Energy Storage; Fans and Blowers, V006T09A016, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-129211
https://doi.org/10.1115/GT2024-129211
View Paper titled, Pilot-Scale Testing of a Transcritical CO 2 -Based Pumped Thermal Energy Storage (PTES) System
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Topics: Carbon dioxide, Testing, Thermal energy storage, Reservoirs, Cycles, Low temperature, Thermal energy, Fluids, Heat engines, Heat pumps
Proceedings Papers

A Physics Informed Neural Network for Solving the Inverse Heat Transfer Problem in Gas Turbine Rotating Cavities

Mark R. Puttock-Brown, Goutham Krishna Mahesh Bindhu, Colin E. Ashby
Proc. ASME. GT2024, Volume 8: Heat Transfer: Internal Air Systems; Heat Transfer: Internal Cooling; Industrial and Cogeneration, V008T14A022, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-128967
https://doi.org/10.1115/GT2024-128967
View Paper titled, A Physics Informed Neural Network for Solving the Inverse Heat Transfer Problem in Gas Turbine Rotating Cavities
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Topics: Artificial neural networks, Cavities, Gas turbines, Heat transfer, Physics, Temperature profiles, Flux (Metallurgy), Heat, Heat conduction, Inverse problems
Proceedings Papers

Experimental Measurements of Buoyancy Induced Flow in Rotating Cavities Under High Reynolds Number Conditions

Mark R. Puttock-Brown, Vasudevan Kanjirakkad
Proc. ASME. GT2024, Volume 8: Heat Transfer: Internal Air Systems; Heat Transfer: Internal Cooling; Industrial and Cogeneration, V008T14A021, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-128942
https://doi.org/10.1115/GT2024-128942
View Paper titled, Experimental Measurements of Buoyancy Induced Flow in Rotating Cavities Under High Reynolds Number Conditions
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Topics: Buoyancy, Cavities, Flow (Dynamics), Reynolds number, Disks, Temperature, Heat transfer, Compressibility, Cycles, Density
Proceedings Papers

The Role of the Working Fluid and Non-Ideal Thermodynamic Effects on Performance of Gas Lubricated Bearings

Wessel de Waart, Matteo Pini
Proc. ASME. GT2024, Volume 10A: Structures and Dynamics — Aerodynamics Excitation and Damping; Bearing and Seal Dynamics; Emerging Methods in Engineering Design, Analysis, and Additive Manufacturing, V10AT22A017, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-127645
https://doi.org/10.1115/GT2024-127645
View Paper titled, The Role of the Working Fluid and Non-Ideal Thermodynamic Effects on Performance of Gas Lubricated Bearings
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Topics: Bearings, Fluids, Gas bearings, Turbomachinery, Bulk modulus, Machinery, Rotors, Turbines, Vapors, Contamination
Proceedings Papers

Validation of a Methodology to Assess the Flutter Limit Cycle Oscillation Amplitude of Low-Pressure Turbine Bladed-Disks - Part II: Shaft Speed Effects

Alvaro Escudero, Salvador Rodríguez-Blanco, Roque Corral
Proc. ASME. GT2024, Volume 10A: Structures and Dynamics — Aerodynamics Excitation and Damping; Bearing and Seal Dynamics; Emerging Methods in Engineering Design, Analysis, and Additive Manufacturing, V10AT21A018, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-129134
https://doi.org/10.1115/GT2024-129134
View Paper titled, Validation of a Methodology to Assess the Flutter Limit Cycle Oscillation Amplitude of Low-Pressure Turbine Bladed-Disks - Part II: Shaft Speed Effects
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Topics: Bladed disks, Flutter (Aerodynamics), Limit cycles, Oscillations, Pressure, Turbines, Vibration, Dry friction, Friction, Aerodynamics
Proceedings Papers

Few Common Observations From a Vibration Test Data

Avinash Kumar, Praveen Iyappan, Krishna Mohan Kumbham
Proc. ASME. GT2024, Volume 10B: Structures and Dynamics — Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V10BT27A003, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-122426
https://doi.org/10.1115/GT2024-122426
View Paper titled, Few Common Observations From a Vibration Test Data
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Topics: Accelerometers, Shock (Mechanics), Vibration, Vibration tests, Excitation, Signals, Displacement, Physics, Steady state, Strain gages
Proceedings Papers

Industrial Applications of Transfer Learning Methods for Aircraft Propulsion Systems

Ryan Jacobs, Sandipp Krishnan, Anindya Bhaduri, Lele Luan, Piyush Pandita, Sayan Ghosh, Liping Wang
Proc. ASME. GT2024, Volume 10B: Structures and Dynamics — Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V10BT25A004, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-125838
https://doi.org/10.1115/GT2024-125838
View Paper titled, Industrial Applications of Transfer Learning Methods for Aircraft Propulsion Systems
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Topics: Aircraft propulsion, Design, Maintenance, Propulsion, Blades, Machine learning, Artificial neural networks, Cycles, Deep neural networks, Downtime
Proceedings Papers

A Physics-Informed Neural Network Surrogate Model for Mistuned Bladed Disks Dynamics

Yu Fan, Jiasen Fan, Anlue Li, Yaguang Wu, Yuning Zhang, Zhiqiang Song
Proc. ASME. GT2024, Volume 10B: Structures and Dynamics — Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V10BT27A016, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-125397
https://doi.org/10.1115/GT2024-125397
View Paper titled, A Physics-Informed Neural Network Surrogate Model for Mistuned Bladed Disks Dynamics
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Topics: Artificial neural networks, Bladed disks, Dynamics (Mechanics), Physics
Proceedings Papers

Turbulence Generation Mechanisms in Compressor Boundary Layers

Matteo Dellacasagrande, Pawel J. Przytarski
Proc. ASME. GT2024, Volume 12A: Turbomachinery — Axial Flow Fan and Compressor Aerodynamics, V12AT29A045, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-128738
https://doi.org/10.1115/GT2024-128738
View Paper titled, Turbulence Generation Mechanisms in Compressor Boundary Layers
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Topics: Boundary layers, Compressors, Turbulence, Flow (Dynamics), Kinetic energy, Fluctuations (Physics), Turbomachinery, Blades, Construction, Engineering simulation
Proceedings Papers

Adjoint Optimization of Non-Axisymmetric Endwall Contours to Reduce Losses in a High-Pressure Turbine

Austin Hendrickson, Spencer Sperling, Bao Nguyen, Richard Celestina, Jong Liu, Hakan Aksoy, Jeremy Nickol, Randall Mathison
Proc. ASME. GT2024, Volume 12B: Turbomachinery — Axial Flow Turbine Aerodynamics; Deposition, Erosion, Fouling, and Icing, V12BT30A047, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-129375
https://doi.org/10.1115/GT2024-129375
View Paper titled, Adjoint Optimization of Non-Axisymmetric Endwall Contours to Reduce Losses in a High-Pressure Turbine
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Topics: Computational fluid dynamics, Gas turbines, High pressure (Physics), Optimization, Turbines, Flow (Dynamics), Geometry, Vortices, Computer software, Design
Proceedings Papers

Exploiting a Transformer Architecture for Simultaneous Development of Transition and Turbulence Models for Turbine Flow Predictions

Yuan Fang, Maximilian Reissmann, Roberto Pacciani, Yaomin Zhao, Andrew S. H. Ooi, Michele Marconcini, Harshal D. Akolekar, Richard D. Sandberg
Proc. ASME. GT2024, Volume 12C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, V12CT32A023, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-125550
https://doi.org/10.1115/GT2024-125550
View Paper titled, Exploiting a Transformer Architecture for Simultaneous Development of Transition and Turbulence Models for Turbine Flow Predictions
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Topics: Flow (Dynamics), Turbines, Turbulence, Computational fluid dynamics, Errors, Reynolds-averaged Navier–Stokes equations, Computer programming, Machine learning, Natural language processing, Physics
Proceedings Papers

Advanced Methods for Assessing Flow Physics of the TU Darmstadt Compressor Stage: Part 2 – Uncertainty Quantification of RANS Turbulence Modeling

Marcel Matha, Felix M. Möller, Christoph Bode, Christian Morsbach, Edmund Kügeler
Proc. ASME. GT2024, Volume 12C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, V12CT32A013, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-122495
https://doi.org/10.1115/GT2024-122495
View Paper titled, Advanced Methods for Assessing Flow Physics of the TU Darmstadt Compressor Stage: Part 2 – Uncertainty Quantification of RANS Turbulence Modeling
Open the PDF for Advanced Methods for Assessing Flow Physics of the TU Darmstadt Compressor Stage: Part 2 – Uncertainty Quantification of RANS Turbulence Modeling in another window
Topics: Compressors, Flow (Dynamics), Modeling, Physics, Reynolds-averaged Navier–Stokes equations, Turbulence, Uncertainty quantification
Proceedings Papers

Modeling Combustor Turbine Interaction Using Efficient Joint Simulation Strategy

Ishan Verma, Laith Zori, Jaydeep Basani, Benjamin Kamrath, Dustin Brandt
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT36A012, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-126729
https://doi.org/10.1115/GT2024-126729
View Paper titled, Modeling Combustor Turbine Interaction Using Efficient Joint Simulation Strategy
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Topics: Combustion chambers, Modeling, Simulation, Turbines, Boundary-value problems, Flow (Dynamics), Rotors, Blades, Combustion, Computer simulation
Proceedings Papers

Loss Breakdown in Axial Turbines: A New Method for Vortex Loss and Wake Detection From 3D RANS Simulations

Greta Raina, Yannick Bousquet, David Luquet, Eric Lippinois, Nicolas Binder
Proc. ASME. GT2024, Volume 12C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, V12CT32A003, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-121181
https://doi.org/10.1115/GT2024-121181
View Paper titled, Loss Breakdown in Axial Turbines: A New Method for Vortex Loss and Wake Detection From 3D RANS Simulations
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Topics: Engineering simulation, Reynolds-averaged Navier–Stokes equations, Simulation, Turbines, Vortices, Wakes, Flow (Dynamics), Entropy, Nozzle guide vanes, Boundary layers
Proceedings Papers

Advancing Airfoil Design: A Physics-Inspired Neural Network Model

Can Unlusoy, Bill Maier, Khalil Al Handawi, Tittu Varghese Mathew, Ravichandra Srinivasan, Mathieu Salz, Michael Kokkolaras
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A010, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-122682
https://doi.org/10.1115/GT2024-122682
View Paper titled, Advancing Airfoil Design: A Physics-Inspired Neural Network Model
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Topics: Airfoils, Artificial neural networks, Design, Neural network models, Physics