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Journal Articles

High-Dimensional Uncertainty Quantification of High-Pressure Turbine Vane Based on Multifidelity Deep Neural Networks

Zhihui Li, Francesco Montomoli, Nicola Casari, Michele Pinelli
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. November 2023, 145(11): 111009.
Paper No: TURBO-23-1153
https://doi.org/10.1115/1.4063391
Published Online: October 4, 2023
View Article titled, High-Dimensional Uncertainty Quantification of High-Pressure Turbine Vane Based on Multifidelity Deep Neural Networks
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Topics: Artificial neural networks, Computational fluid dynamics, Design, Flow (Dynamics), Nozzles, Pressure, Turbines, Uncertainty quantification, Turbomachinery, Uncertainty
Journal Articles

Robust Design of Herringbone Grooved Journal Bearings Using Multi-Objective Optimization With Artificial Neural Networks

Soheyl Massoudi, Jürg Schiffmann
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. November 2023, 145(11): 111010.
Paper No: TURBO-23-1158
https://doi.org/10.1115/1.4063392
Published Online: October 4, 2023
View Article titled, Robust Design of Herringbone Grooved Journal Bearings Using Multi-Objective Optimization With Artificial Neural Networks
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Topics: Artificial neural networks, Bearings, Design, Journal bearings, Manufacturing, Optimization, Pareto optimization, Robustness, Rotors, Clearances (Engineering)
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
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. July 2023, 145(7): 071013.
Paper No: TURBO-22-1300
https://doi.org/10.1115/1.4056777
Published Online: February 10, 2023
View Article titled, Using Machine Learning for Loss Prediction in a Hybrid Meanline Modeling Method to Deliver Improved Radial Turbine Performance Prediction
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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
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. December 2022, 144(12): 121005.
Paper No: TURBO-22-1128
https://doi.org/10.1115/1.4055335
Published Online: September 15, 2022
View Article titled, An Iterative Neural Operator to Predict the Thermo-Fluid Information in Internal Cooling Channels
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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
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. September 2022, 144(9): 091011.
Paper No: TURBO-21-1228
https://doi.org/10.1115/1.4053744
Published Online: March 4, 2022
View Article titled, Computer 3D Vision-Aided Full-3D Optimization of a Centrifugal Impeller
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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
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. June 2022, 144(6): 061005.
Paper No: TURBO-21-1022
https://doi.org/10.1115/1.4053231
Published Online: January 28, 2022
View Article titled, A New Loss Generation Body Force Model for Fan/Compressor Blade Rows: Application to Uniform and Non-Uniform Inflow in Rotor 67
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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
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. April 2022, 144(4): 041006.
Paper No: TURBO-21-1054
https://doi.org/10.1115/1.4052601
Published Online: November 5, 2021
View Article titled, Direct and Inverse Model for Single-Hole Film Cooling With Machine Learning
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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
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. January 2022, 144(1): 011013.
Paper No: TURBO-21-1141
https://doi.org/10.1115/1.4052136
Published Online: September 21, 2021
View Article titled, Global Optimization of a Transonic Fan Blade Through AI-Enabled Active Subspaces
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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
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. September 2021, 143(9): 091006.
Paper No: TURBO-20-1367
https://doi.org/10.1115/1.4050441
Published Online: May 5, 2021
View Article titled, Unsteady Steam Turbine Optimization Using High-Fidelity Computational Fluid Dynamics
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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
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. July 2020, 142(7): 071002.
Paper No: TURBO-19-1260
https://doi.org/10.1115/1.4047186
Published Online: June 26, 2020
View Article titled, Rapid Estimate of Wind Turbine Energy Loss Due to Blade Leading Edge Delamination Using Artificial Neural Networks
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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
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. May 2020, 142(5): 051010.
Paper No: TURBO-19-1281
https://doi.org/10.1115/1.4046658
Published Online: April 30, 2020
View Article titled, Predicting the Operability of Damaged Compressors Using Machine Learning
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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
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research-Article
J. Turbomach. May 2014, 136(5): 051010.
Paper No: TURBO-13-1142
https://doi.org/10.1115/1.4025217
Published Online: September 27, 2013
View Article titled, Improving Pin-Fin Heat Transfer Predictions Using Artificial Neural Networks
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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
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research-Article
J. Turbomach. September 2013, 135(5): 051015.
Paper No: TURBO-12-1006
https://doi.org/10.1115/1.4023030
Published Online: June 28, 2013
View Article titled, Optimization of a U-Bend for Minimal Pressure Loss in Internal Cooling Channels—Part I: Numerical Method
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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
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research-Article
J. Turbomach. May 2013, 135(3): 031008.
Paper No: TURBO-11-1259
https://doi.org/10.1115/1.4006667
Published Online: March 25, 2013
View Article titled, Mistuning Identification of Integrally Bladed Disks With Cascaded Optimization and Neural Networks
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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
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. July 2010, 132(3): 031004.
https://doi.org/10.1115/1.3144162
Published Online: March 24, 2010
View Article titled, Multidisciplinary Optimization of a Radial Compressor for Microgas Turbine Applications
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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
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. April 2010, 132(2): 021014.
https://doi.org/10.1115/1.3104615
Published Online: January 13, 2010
View Article titled, Design and Optimization of the Internal Cooling Channels of a High Pressure Turbine Blade—Part II: Optimization
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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
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Technical Briefs
J. Turbomach. January 2010, 132(1): 014501.
https://doi.org/10.1115/1.3103923
Published Online: September 11, 2009
View Article titled, Application of Artificial Neural Networks in Investigations of Steam Turbine Cascades
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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
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Technical Papers
J. Turbomach. July 2007, 129(3): 468–478.
https://doi.org/10.1115/1.2437232
Published Online: May 31, 2006
View Article titled, Optimization of a Real-Time Simulator Based on Recurrent Neural Networks for Compressor Transient Behavior Prediction
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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
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Technical Papers
J. Turbomach. July 2006, 128(3): 444–454.
https://doi.org/10.1115/1.2183315
Published Online: February 1, 2005
View Article titled, Simulation of Compressor Transient Behavior Through Recurrent Neural Network Models
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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
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. April 1999, 121(2): 326–332.
https://doi.org/10.1115/1.2841318
Published Online: April 1, 1999
View Article titled, Turbomachinery Blade Design Using a Navier–Stokes Solver and Artificial Neural Network
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Topics: Artificial neural networks, Blades, Design, Turbomachinery, Turbine blades, Databases, Degrees of freedom, Geometry, Optimization, Response surface methodology