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Proc. ASME. GT2021, Volume 9A: Structures and Dynamics — Aerodynamics Excitation and Damping; Bearing and Seal Dynamics; Emerging Methods in Design and Engineering, V09AT23A017, June 7–11, 2021

Paper No: GT2021-60075

DOI: https://doi.org/10.1115/GT2021-60075

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Topics: Airfoils, Artificial neural networks, Flow (Dynamics), Oscillations

Proceedings Papers

Data-Driven Approach for Identifying Mistuning in As-Manufactured Blisks

Sean T. Kelly, Andrea Lupini, Bogdan I. Epureanu

Proc. ASME. GT2021, Volume 9B: Structures and Dynamics — Fatigue, Fracture, and Life Prediction; Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V09BT29A025, June 7–11, 2021

Paper No: GT2021-59887

DOI: https://doi.org/10.1115/GT2021-59887

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Topics: Artificial neural networks, Blades, Cantilevers, Computational methods, Damping, Density, Disks, Dynamics (Mechanics), Engines, Excitation

Proceedings Papers

Centrifugal Compressor Stage Efficiency and Rotor Stiffness Augmentation via Artificial Neural Networks

Andrea Agnolucci, Michele Marconcini, Andrea Arnone, Lorenzo Toni, Angelo Grimaldi, Marco Giachi

Proc. ASME. GT2021, Volume 2D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V02DT37A014, June 7–11, 2021

Paper No: GT2021-59998

DOI: https://doi.org/10.1115/GT2021-59998

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Topics: Artificial neural networks, Compressors, Rotors, Stiffness, Optimization, Impellers, Design, Diffusers, Reynolds-averaged Navier–Stokes equations

Proceedings Papers

Automatically Designed Deep Gaussian Process for Turbomachinery Application

Yuan Jin, Jin Chai, Olivier Jung

Paper No: GT2021-58469

DOI: https://doi.org/10.1115/GT2021-58469

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Topics: Turbomachinery, Turbines, Design, Artificial neural networks, Blades, Dimensions, Pressure, Robustness, Stochastic processes

Proceedings Papers

AI Assisted High Fidelity Multi-Physics Digital Twin of Industrial Gas Turbines

Senthil Krishnababu, Omar Valero, Roger Wells

Paper No: GT2021-58925

DOI: https://doi.org/10.1115/GT2021-58925

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Topics: Industrial gases, Physics, Turbines, Manufacturing, Compressors, Gas turbines, Maintenance, Teams, Artificial neural networks, Blocks (Building materials)

Proceedings Papers

Global Optimisation of a Transonic Fan Blade Through AI-Enabled Active Subspaces

Diego I. Lopez, Tiziano Ghisu, Shahrokh Shahpar

Proc. ASME. GT2021, Volume 2A: Turbomachinery — Axial Flow Fan and Compressor Aerodynamics, V02AT31A026, June 7–11, 2021

Paper No: GT2021-59166

DOI: https://doi.org/10.1115/GT2021-59166

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Topics: Artificial neural networks, Blades, Computational fluid dynamics, Design, Optimization

Proceedings Papers

Prediction of Dynamic Behavior of a Single Shaft Gas Turbine Using NARX Models

Hamid Asgari, Emmanuel Ory

Proc. ASME. GT2021, Volume 6: Ceramics and Ceramic Composites; Coal, Biomass, Hydrogen, and Alternative Fuels; Microturbines, Turbochargers, and Small Turbomachines, V006T19A007, June 7–11, 2021

Paper No: GT2021-58960

DOI: https://doi.org/10.1115/GT2021-58960

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Topics: Artificial neural networks, Gas turbines, Modeling

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

Towards Reduced Order Models of Small-Scale Acoustically Significant Components in Gas Turbine Combustion Chambers

Suhas A. Kowshik, Sumukha Shridhar, N. C. W. Treleaven

Proc. ASME. GT2021, Volume 3B: Combustion, Fuels, and Emissions, V03BT04A013, June 7–11, 2021

Paper No: GT2021-59601

DOI: https://doi.org/10.1115/GT2021-59601

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Topics: Acoustics, Combustion chambers, Gas turbines, Simulation, Computational fluid dynamics, Flow (Dynamics), Artificial neural networks, Damping, Cooling, Design

Proceedings Papers

A Comprehensive Model for Cetane Number Prediction Using Machine Learning

Abdul Gani Abdul Jameel

Proc. ASME. GT2021, Volume 3B: Combustion, Fuels, and Emissions, V03BT04A030, June 7–11, 2021

Paper No: GT2021-60097

DOI: https://doi.org/10.1115/GT2021-60097

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Topics: Artificial neural networks, Ignition, Machine learning, Fuels, Algorithms, Combustion, Errors, Ester, Ethers (Class of compounds), Feedforward control

Proceedings Papers

Rapid Defect Detection and Classification in Images Using Convolutional Neural Networks

Peter Warren, Hessein Ali, Hossein Ebrahimi, Ranajay Ghosh

Proc. ASME. GT2021, Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power, V004T05A013, June 7–11, 2021

Paper No: GT2021-59801

DOI: https://doi.org/10.1115/GT2021-59801

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Topics: Artificial neural networks, Flaw detection, Optical quality control, Machine learning, Image processing, Inspection, Manufacturing, Nondestructive evaluation, Turbomachinery

Proceedings Papers

A Novel Gas Path Fault Diagnostic Model for Gas Turbine Based on Explainable Convolutional Neural Network With LIME Method

Chen Yao, Xi Yueyun, Chen Jinwei, Zhang Huisheng

Proc. ASME. GT2021, Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power, V004T05A008, June 7–11, 2021

Paper No: GT2021-59289

DOI: https://doi.org/10.1115/GT2021-59289

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Topics: Artificial neural networks, Gas turbines, Fault diagnosis, Aviation, Energy industry, Failure mechanisms, Maintenance, Sensors

Proceedings Papers

Bayesian Machine Learning for the Prognosis of Combustion Instabilities From Noise

Ushnish Sengupta, Carl E. Rasmussen, Matthew P. Juniper

Proc. ASME. GT2020, Volume 4A: Combustion, Fuels, and Emissions, V04AT04A047, September 21–25, 2020

Paper No: GT2020-14904

DOI: https://doi.org/10.1115/GT2020-14904

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Topics: Combustion, Machine learning, Noise (Sound), Acoustics, Flames, Algorithms, Artificial neural networks, Boundary-value problems, Combustion chambers, Fingerprints

Proceedings Papers

Application of a Convolutional Neural Network for Wave Mode Identification in a Rotating Detonation Combustor Using High-Speed Imaging

Kristyn B. Johnson, Donald H. Ferguson, Robert S. Tempke, Andrew C. Nix

Proc. ASME. GT2020, Volume 4B: Combustion, Fuels, and Emissions, V04BT04A020, September 21–25, 2020

Paper No: GT2020-15676

DOI: https://doi.org/10.1115/GT2020-15676

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Topics: Artificial neural networks, Combustion chambers, Engines, Explosions, Imaging, Machine learning, Waves

Proceedings Papers

Finding the Best Architecture of an Artificial Neural Network to Model Prefilming Airblast Atomization: Not So Deep Learning

G. Chaussonnet, S. Gepperth, S. Holz, R. Koch, H.-J. Bauer

Proc. ASME. GT2020, Volume 4B: Combustion, Fuels, and Emissions, V04BT04A055, September 21–25, 2020

Paper No: GT2020-16172

DOI: https://doi.org/10.1115/GT2020-16172

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Topics: Artificial neural networks, Architecture, Drops, Shapes, Testing, Computers, Databases, Deformation, Errors, Fittings

Proceedings Papers

Improving the Safety of Power Plants by Developing a Digital Twin and an Expert System for Adaptive-Predictive Analysis of the Operability of Gas Turbine Units

Roman Polyakov, Eugenii Paholkin, Igor Kudryavcev, Nikolay Krupenin

Proc. ASME. GT2020, Volume 6: Education; Electric Power, V006T09A001, September 21–25, 2020

Paper No: GT2020-14217

DOI: https://doi.org/10.1115/GT2020-14217

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Topics: Artificial neural networks, Construction, Expert systems, Failure, Gas turbines, Modeling, Power stations, Safety, Security, Turbogenerators

Proceedings Papers

Prediction of Gas Turbine Performance Using Machine Learning Methods

Vipul Goyal, Mengyu Xu, Jayanta Kapat, Ladislav Vesely

Proc. ASME. GT2020, Volume 6: Education; Electric Power, V006T09A004, September 21–25, 2020

Paper No: GT2020-15232

DOI: https://doi.org/10.1115/GT2020-15232

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Topics: Artificial neural networks, Gas turbines, Machine learning, Algorithms, Failure, Temperature, Blades, Flow (Dynamics), Fuels, Power stations

Proceedings Papers

An Ensemble of Recurrent Neural Networks for Real Time Performance Modelling of Three-Spool Aero-Derivative Gas Turbine Engine

Ibrahem M. A. Ibrahem, Ouassima Akhrif, Hany Moustapha, Martin Staniszewski

Proc. ASME. GT2020, Volume 6: Education; Electric Power, V006T09A008, September 21–25, 2020

Paper No: GT2020-15756

DOI: https://doi.org/10.1115/GT2020-15756

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Topics: Artificial neural networks, Gas turbines, Modeling

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NARROW

Research on Fault Diagnosis of Steam Turbine Rotor Unbalance and Parallel Misalignment Based on Numerical Simulation and Convolutional Neural Network

Multi-Parameter Prediction for Steam Turbine Based on Real-Time Data Using Deep Learning Approaches

A Novel Deep Learning Model for the Flow Field Reconstruction of An Oscillating Airfoil

Data-Driven Approach for Identifying Mistuning in As-Manufactured Blisks

Centrifugal Compressor Stage Efficiency and Rotor Stiffness Augmentation via Artificial Neural Networks

Automatically Designed Deep Gaussian Process for Turbomachinery Application

AI Assisted High Fidelity Multi-Physics Digital Twin of Industrial Gas Turbines

Global Optimisation of a Transonic Fan Blade Through AI-Enabled Active Subspaces

Prediction of Dynamic Behavior of a Single Shaft Gas Turbine Using NARX Models

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

Towards Reduced Order Models of Small-Scale Acoustically Significant Components in Gas Turbine Combustion Chambers

A Comprehensive Model for Cetane Number Prediction Using Machine Learning

Rapid Defect Detection and Classification in Images Using Convolutional Neural Networks

A Novel Gas Path Fault Diagnostic Model for Gas Turbine Based on Explainable Convolutional Neural Network With LIME Method

Bayesian Machine Learning for the Prognosis of Combustion Instabilities From Noise

Application of a Convolutional Neural Network for Wave Mode Identification in a Rotating Detonation Combustor Using High-Speed Imaging

Finding the Best Architecture of an Artificial Neural Network to Model Prefilming Airblast Atomization: Not So Deep Learning

Improving the Safety of Power Plants by Developing a Digital Twin and an Expert System for Adaptive-Predictive Analysis of the Operability of Gas Turbine Units

Prediction of Gas Turbine Performance Using Machine Learning Methods

An Ensemble of Recurrent Neural Networks for Real Time Performance Modelling of Three-Spool Aero-Derivative Gas Turbine Engine

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