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Journal Articles

Using Machine Learning for Loss Prediction in a Hybrid Meanline Modeling Method to Deliver Improved Radial Turbine Performance Prediction

Pangbo Ren, Charles Stuart, Stephen Spence, Ryosuke Inomata, Takayuki Kobayashi, Isao Morita

J. Turbomach. July 2023, 145(7): 071013.

Paper No: TURBO-22-1300

DOI: https://doi.org/10.1115/1.4056777

Published Online: February 10, 2023

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Topics: Computational fluid dynamics, Flow (Dynamics), Geometry, Machine learning, Modeling, Rotors, Turbines, Nozzles, Turbochargers, Artificial neural networks

Journal Articles

An Iterative Neural Operator to Predict the Thermo-Fluid Information in Internal Cooling Channels

Li Yang, Qi Wang, Yu Rao

J. Turbomach. December 2022, 144(12): 121005.

Paper No: TURBO-22-1128

DOI: https://doi.org/10.1115/1.4055335

Published Online: September 15, 2022

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Topics: Artificial neural networks, Cooling, Errors, Heat flux, Heat transfer, Partial differential equations, Physics, Pressure, Temperature, Thermofluids

Journal Articles

Computer 3D Vision-Aided Full-3D Optimization of a Centrifugal Impeller

Cheng Ji, Zhiheng Wang, Guang Xi

J. Turbomach. September 2022, 144(9): 091011.

Paper No: TURBO-21-1228

DOI: https://doi.org/10.1115/1.4053744

Published Online: March 4, 2022

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Topics: Artificial neural networks, Blades, Computers, Flow (Dynamics), Impellers, Optimization, Pressure, Design, Shapes

Journal Articles

A New Loss Generation Body Force Model for Fan/Compressor Blade Rows: Application to Uniform and Non-Uniform Inflow in Rotor 67

Syamak Pazireh, Jeffrey Defoe

J. Turbomach. June 2022, 144(6): 061005.

Paper No: TURBO-21-1022

DOI: https://doi.org/10.1115/1.4053231

Published Online: January 28, 2022

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Topics: Blades, Boundary layers, Compressors, Computation, Flow (Dynamics), Inflow, Pressure, Rotors, Design, Reynolds-averaged Navier–Stokes equations

Journal Articles

Direct and Inverse Model for Single-Hole Film Cooling With Machine Learning

Haifeng Xing, Lei Luo, Wei Du, Songtao Wang

J. Turbomach. April 2022, 144(4): 041006.

Paper No: TURBO-21-1054

DOI: https://doi.org/10.1115/1.4052601

Published Online: November 5, 2021

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Topics: Artificial neural networks, Design, Errors, Film cooling, Machine learning, Coolants, Computational fluid dynamics, Density, Turbulence, Modeling

Journal Articles

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

Diego I. Lopez, Tiziano Ghisu, Shahrokh Shahpar

J. Turbomach. January 2022, 144(1): 011013.

Paper No: TURBO-21-1141

DOI: https://doi.org/10.1115/1.4052136

Published Online: September 21, 2021

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

Journal Articles

Unsteady Steam Turbine Optimization Using High-Fidelity Computational Fluid Dynamics

Vahid Iranidokht, Ilias Papagiannis, Anestis I. Kalfas, Reza S. Abhari, Shigeki Senoo, Kazuhiro Momma

J. Turbomach. September 2021, 143(9): 091006.

Paper No: TURBO-20-1367

DOI: https://doi.org/10.1115/1.4050441

Published Online: May 5, 2021

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Topics: Computational fluid dynamics, Flow (Dynamics), Geometry, Optimization, Pressure, Rotors, Steam turbines, Blades, Cavities, Turbines

Journal Articles

Rapid Estimate of Wind Turbine Energy Loss Due to Blade Leading Edge Delamination Using Artificial Neural Networks

M. Sergio Campobasso, Anna Cavazzini, Edmondo Minisci

J. Turbomach. July 2020, 142(7): 071002.

Paper No: TURBO-19-1260

DOI: https://doi.org/10.1115/1.4047186

Published Online: June 26, 2020

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Topics: Airfoils, Blades, Computational fluid dynamics, Damage, Delamination, Geometry, Turbines, Wind turbines, Artificial neural networks, Energy dissipation

Journal Articles

Predicting the Operability of Damaged Compressors Using Machine Learning

J. V. Taylor, B. Conduit, A. Dickens, C. Hall, M. Hillel, R. J. Miller

J. Turbomach. May 2020, 142(5): 051010.

Paper No: TURBO-19-1281

DOI: https://doi.org/10.1115/1.4046658

Published Online: April 30, 2020

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Topics: Artificial neural networks, Blades, Compressors, Damage, Machine learning, Pressure, Parametrization

Journal Articles

Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks

Jason K. Ostanek

J. Turbomach. May 2014, 136(5): 051010.

Paper No: TURBO-13-1142

DOI: https://doi.org/10.1115/1.4025217

Published Online: September 27, 2013

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Topics: Artificial neural networks, Heat transfer, Errors

Journal Articles

Optimization of a U-Bend for Minimal Pressure Loss in Internal Cooling Channels—Part I: Numerical Method

Tom Verstraete, Filippo Coletti, Jérémy Bulle, Timothée Vanderwielen, Tony Arts

J. Turbomach. September 2013, 135(5): 051015.

Paper No: TURBO-12-1006

DOI: https://doi.org/10.1115/1.4023030

Published Online: June 28, 2013

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Topics: Optimization, Pressure, Artificial neural networks, Design, Pressure drop, Databases, Cooling

Journal Articles

Mistuning Identification of Integrally Bladed Disks With Cascaded Optimization and Neural Networks

Mehmet Ersin Yumer, Ender Cigeroglu, H. Nevzat Özgüven

J. Turbomach. May 2013, 135(3): 031008.

Paper No: TURBO-11-1259

DOI: https://doi.org/10.1115/1.4006667

Published Online: March 25, 2013

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Topics: Artificial neural networks, Disks, Optimization, Blades, Mode shapes

Journal Articles

Multidisciplinary Optimization of a Radial Compressor for Microgas Turbine Applications

T. Verstraete, Z. Alsalihi, R. A. Van den Braembussche

J. Turbomach. July 2010, 132(3): 031004.

DOI: https://doi.org/10.1115/1.3144162

Published Online: March 24, 2010

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Topics: Artificial neural networks, Blades, Compressors, Design, Finite element analysis, Optimization, Stress, Databases, Geometry, Impellers

Journal Articles

Design and Optimization of the Internal Cooling Channels of a High Pressure Turbine Blade—Part II: Optimization

Tom Verstraete, Sergio Amaral, René Van den Braembussche, Tony Arts

J. Turbomach. April 2010, 132(2): 021014.

DOI: https://doi.org/10.1115/1.3104615

Published Online: January 13, 2010

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Topics: Artificial neural networks, Cooling, Design, Flow (Dynamics), Optimization, Geometry, Databases, Blades, Genetic algorithms, High pressure (Physics)

Journal Articles

Application of Artificial Neural Networks in Investigations of Steam Turbine Cascades

Krzysztof Kosowski, Karol Tucki, Adrian Kosowski

J. Turbomach. January 2010, 132(1): 014501.

DOI: https://doi.org/10.1115/1.3103923

Published Online: September 11, 2009

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Topics: Artificial neural networks, Blades, Design, Steam turbines, Turbines, Flow (Dynamics), Computational fluid dynamics, Cascades (Fluid dynamics)

Journal Articles

Optimization of a Real-Time Simulator Based on Recurrent Neural Networks for Compressor Transient Behavior Prediction

M. Venturini

J. Turbomach. July 2007, 129(3): 468–478.

DOI: https://doi.org/10.1115/1.2437232

Published Online: May 31, 2006

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Topics: Artificial neural networks, Compressors, Delays, Errors, Physics, Testing, Simulation, Transients (Dynamics), Optimization, Dynamic models

Journal Articles

Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models

M. Venturini

J. Turbomach. July 2006, 128(3): 444–454.

DOI: https://doi.org/10.1115/1.2183315

Published Online: February 1, 2005

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Topics: Compressors, Delays, Errors, Physics, Simulation, Transients (Dynamics), Artificial neural networks, Measurement uncertainty, Testing

Journal Articles

Turbomachinery Blade Design Using a Navier–Stokes Solver and Artificial Neural Network

S. Pierret, R. A. Van den Braembussche

J. Turbomach. April 1999, 121(2): 326–332.

DOI: https://doi.org/10.1115/1.2841318

Published Online: April 1, 1999

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Topics: Artificial neural networks, Blades, Design, Turbomachinery, Turbine blades, Databases, Degrees of freedom, Geometry, Optimization, Response surface methodology

Journal citation

NARROW

Using Machine Learning for Loss Prediction in a Hybrid Meanline Modeling Method to Deliver Improved Radial Turbine Performance Prediction

An Iterative Neural Operator to Predict the Thermo-Fluid Information in Internal Cooling Channels

Computer 3D Vision-Aided Full-3D Optimization of a Centrifugal Impeller

A New Loss Generation Body Force Model for Fan/Compressor Blade Rows: Application to Uniform and Non-Uniform Inflow in Rotor 67

Direct and Inverse Model for Single-Hole Film Cooling With Machine Learning

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

Unsteady Steam Turbine Optimization Using High-Fidelity Computational Fluid Dynamics

Rapid Estimate of Wind Turbine Energy Loss Due to Blade Leading Edge Delamination Using Artificial Neural Networks

Predicting the Operability of Damaged Compressors Using Machine Learning

Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks

Optimization of a U-Bend for Minimal Pressure Loss in Internal Cooling Channels—Part I: Numerical Method

Mistuning Identification of Integrally Bladed Disks With Cascaded Optimization and Neural Networks

Multidisciplinary Optimization of a Radial Compressor for Microgas Turbine Applications

Design and Optimization of the Internal Cooling Channels of a High Pressure Turbine Blade—Part II: Optimization

Application of Artificial Neural Networks in Investigations of Steam Turbine Cascades

Optimization of a Real-Time Simulator Based on Recurrent Neural Networks for Compressor Transient Behavior Prediction

Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models

Turbomachinery Blade Design Using a Navier–Stokes Solver and Artificial Neural Network

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