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Journal Articles

Application of Machine Learning Techniques in Calibration and Data Reduction of Multihole Probes

Arman Mirhashemi, Paht Juangphanich, Kenji Miki

Publisher: ASME

J. Turbomach. July 2025, 147(7): 071011.

Paper No: TURBO-24-1179

https://doi.org/10.1115/1.4067123

Published Online: December 16, 2024

View Articletitled, Application of Machine Learning Techniques in Calibration and Data Reduction of Multihole Probes

Topics: Algorithms, Artificial neural networks, Calibration, Flow (Dynamics), Mach number, Machine learning, Pressure, Probes, Uncertainty

Journal Articles

The Development of Machine Learning Models for Radial Compressor Monitoring With Instability Detection

Lorenzo Carrattieri, Carlo Cravero, Davide Marsano, Emiliano Valenti, Vishnu Sishtla, Chaitanya Halbe

Publisher: ASME

J. Turbomach. May 2025, 147(5): 051004.

Paper No: TURBO-24-1128

https://doi.org/10.1115/1.4066918

Published Online: November 12, 2024

View Articletitled, The Development of Machine Learning Models for Radial Compressor Monitoring With Instability Detection

Topics: Compressors, Diffusers, Machine learning, Probes, Signals, Surges, Pressure, Stability, Computational fluid dynamics, Algorithms

Journal Articles

DEVELOPMENT OF MACHINE-LEARNT TURBULENCE CLOSURES FOR WAKE MIXING PREDICTIONS IN LOW-PRESSURE TURBINES

Yuri Frey Marioni, Paolo Adami, Raul Vazquez Diaz, Andrea Cassinelli, Spencer J. Sherwin, Francesco Montomoli

Publisher: ASME

J. Turbomach.

Paper No: TURBO-22-1316

https://doi.org/10.1115/1.4066919

Published Online: October 17, 2024

View Articletitled, DEVELOPMENT OF MACHINE-LEARNT TURBULENCE CLOSURES FOR WAKE MIXING PREDICTIONS IN LOW-PRESSURE TURBINES

Topics: Machinery, Pressure, Turbines, Turbochargers, Turbulence, Wakes, Artificial neural networks, Computational fluid dynamics, Aircraft engines, Algebra

Journal Articles

A Data-Centric Approach to Loss Mechanisms

Alistair C. Senior, Robert J. Miller

Publisher: ASME

J. Turbomach. April 2024, 146(4): 041007.

Paper No: TURBO-23-1233

https://doi.org/10.1115/1.4064167

Published Online: December 15, 2023

View Articletitled, A Data-Centric Approach to Loss Mechanisms

Topics: Design, Flow (Dynamics), Machine learning, Pressure, Wedges, Compressors, Wakes, Blades

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

Publisher: ASME

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

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

Journal Articles

Machine Learning for the Development of Data-Driven Turbulence Closures in Coolant Systems

James Hammond, Francesco Montomoli, Marco Pietropaoli, Richard D. Sandberg, Vittorio Michelassi

Publisher: ASME

J. Turbomach. August 2022, 144(8): 081003.

Paper No: TURBO-20-1174

https://doi.org/10.1115/1.4053533

Published Online: March 3, 2022

View Articletitled, Machine Learning for the Development of Data-Driven Turbulence Closures in Coolant Systems

Topics: Anisotropy, Design, Errors, Flow (Dynamics), Geometry, Heat transfer, Reynolds-averaged Navier–Stokes equations, Tensors, Testing, Turbulence

Journal Articles

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

Haifeng Xing, Lei Luo, Wei Du, Songtao Wang

Publisher: ASME

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

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

Journal Articles

Integration of Machine Learning and Computational Fluid Dynamics to Develop Turbulence Models for Improved Low-Pressure Turbine Wake Mixing Prediction

Harshal D. Akolekar, Yaomin Zhao, Richard D. Sandberg, Roberto Pacciani

Publisher: ASME

J. Turbomach. December 2021, 143(12): 121001.

Paper No: TURBO-20-1427

https://doi.org/10.1115/1.4051417

Published Online: June 23, 2021

View Articletitled, Integration of Machine Learning and Computational Fluid Dynamics to Develop Turbulence Models for Improved Low-Pressure Turbine Wake Mixing Prediction

Topics: Blades, Computational fluid dynamics, Reynolds-averaged Navier–Stokes equations, Turbines, Wakes, Flow (Dynamics), Turbulence, Pressure, Model development, Machine learning

Journal Articles

A Machine-Learnt Wall Function for Rotating Diffusers

Lorenzo Tieghi, Alessandro Corsini, Giovanni Delibra, Francesco Aldo Tucci

Publisher: ASME

J. Turbomach. August 2021, 143(8): 081012.

Paper No: TURBO-20-1368

https://doi.org/10.1115/1.4050442

Published Online: May 3, 2021

View Articletitled, A Machine-Learnt Wall Function for Rotating Diffusers

Topics: Algorithms, Computer simulation, Diffusers, Flow (Dynamics), Machinery, Modeling, Reynolds-averaged Navier–Stokes equations, Simulation, Turbulence, Machine learning

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

Publisher: ASME

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 Articletitled, Predicting the Operability of Damaged Compressors Using Machine Learning

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

Journal Articles

Generalization of Machine-Learned Turbulent Heat Flux Models Applied to Film Cooling Flows

Pedro M. Milani, Julia Ling, John K. Eaton

Publisher: ASME

J. Turbomach. January 2020, 142(1): 011007.

Paper No: TURBO-19-1234

https://doi.org/10.1115/1.4045389

Published Online: December 17, 2019

View Articletitled, Generalization of Machine-Learned Turbulent Heat Flux Models Applied to Film Cooling Flows

Topics: Film cooling, Flow (Dynamics), Heat flux, Machine learning, Prandtl number, Temperature, Turbulence, Simulation, Geometry

Journal Articles

ISUAAAT15 Special Issue

Li He, Kenneth C. Hall

Publisher: ASME

J. Turbomach. October 2019, 141(10): 100201.

Paper No: TURBO-19-1167

https://doi.org/10.1115/1.4044448

Published Online: October 10, 2019

View Articletitled, ISUAAAT15 Special Issue

Topics: Aerodynamic noise, Aerodynamics, Aeroelasticity, Turbomachinery, Blades, Engineering disciplines, Engineers, Engines, Fluid structure interaction, Machine learning

Journal Articles

Machine Learning Enabled Adaptive Optimization of a Transonic Compressor Rotor With Precompression

Michael Joly, Soumalya Sarkar, Dhagash Mehta

Publisher: ASME

J. Turbomach. May 2019, 141(5): 051011.

Paper No: TURBO-18-1262

https://doi.org/10.1115/1.4041808

Published Online: January 25, 2019

View Articletitled, Machine Learning Enabled Adaptive Optimization of a Transonic Compressor Rotor With Precompression

Topics: Design, Optimization, Machine learning, Computational fluid dynamics, Rotors, Compressors

Journal Articles

Physical Interpretation of Machine Learning Models Applied to Film Cooling Flows

Pedro M. Milani, Julia Ling, John K. Eaton

Publisher: ASME

J. Turbomach. January 2019, 141(1): 011004.

Paper No: TURBO-18-1209

https://doi.org/10.1115/1.4041291

Published Online: October 17, 2018

View Articletitled, Physical Interpretation of Machine Learning Models Applied to Film Cooling Flows

Topics: Film cooling, Flow (Dynamics), Machine learning, Reynolds-averaged Navier–Stokes equations, Scalars, Turbulence, Simulation, Geometry, Algorithms

Journal Articles

Applying Machine Learnt Explicit Algebraic Stress and Scalar Flux Models to a Fundamental Trailing Edge Slot

R. D. Sandberg, R. Tan, J. Weatheritt, A. Ooi, A. Haghiri, V. Michelassi, G. Laskowski

Publisher: ASME

J. Turbomach. October 2018, 140(10): 101008.

Paper No: TURBO-18-1202

https://doi.org/10.1115/1.4041268

Published Online: September 28, 2018

View Articletitled, Applying Machine Learnt Explicit Algebraic Stress and Scalar Flux Models to a Fundamental Trailing Edge Slot

Topics: Anisotropy, Heat flux, Large eddy simulation, Reynolds-averaged Navier–Stokes equations, Scalars, Stress, Tensors, Turbulence, Eddies (Fluid dynamics), Algebra

Journal Articles

A Machine Learning Approach for Determining the Turbulent Diffusivity in Film Cooling Flows

Pedro M. Milani, Julia Ling, Gonzalo Saez-Mischlich, Julien Bodart, John K. Eaton

Publisher: ASME

J. Turbomach. February 2018, 140(2): 021006.

Paper No: TURBO-17-1132

https://doi.org/10.1115/1.4038275

Published Online: December 6, 2017

View Articletitled, A Machine Learning Approach for Determining the Turbulent Diffusivity in Film Cooling Flows

Topics: Algorithms, Film cooling, Machine learning, Reynolds-averaged Navier–Stokes equations, Turbulence, Flow (Dynamics), Temperature, Simulation, Scalars

Journal Articles

Uncertainty Analysis and Data-Driven Model Advances for a Jet-in-Crossflow

Julia Ling, Anthony Ruiz, Guilhem Lacaze, Joseph Oefelein

Publisher: ASME

J. Turbomach. February 2017, 139(2): 021008.

Paper No: TURBO-16-1175

https://doi.org/10.1115/1.4034556

Published Online: October 4, 2016

View Articletitled, Uncertainty Analysis and Data-Driven Model Advances for a Jet-in-Crossflow

Topics: Anisotropy, Flow (Dynamics), Machine learning, Reynolds-averaged Navier–Stokes equations, Stress, Turbulence, Viscosity

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Application of Machine Learning Techniques in Calibration and Data Reduction of Multihole Probes

The Development of Machine Learning Models for Radial Compressor Monitoring With Instability Detection

DEVELOPMENT OF MACHINE-LEARNT TURBULENCE CLOSURES FOR WAKE MIXING PREDICTIONS IN LOW-PRESSURE TURBINES

A Data-Centric Approach to Loss Mechanisms

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

Machine Learning for the Development of Data-Driven Turbulence Closures in Coolant Systems

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

Integration of Machine Learning and Computational Fluid Dynamics to Develop Turbulence Models for Improved Low-Pressure Turbine Wake Mixing Prediction

A Machine-Learnt Wall Function for Rotating Diffusers

Predicting the Operability of Damaged Compressors Using Machine Learning

Generalization of Machine-Learned Turbulent Heat Flux Models Applied to Film Cooling Flows

ISUAAAT15 Special Issue

Machine Learning Enabled Adaptive Optimization of a Transonic Compressor Rotor With Precompression

Physical Interpretation of Machine Learning Models Applied to Film Cooling Flows

Applying Machine Learnt Explicit Algebraic Stress and Scalar Flux Models to a Fundamental Trailing Edge Slot

A Machine Learning Approach for Determining the Turbulent Diffusivity in Film Cooling Flows

Uncertainty Analysis and Data-Driven Model Advances for a Jet-in-Crossflow

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