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Proceedings Papers

Evaluation of a Machine Learning Turbulence Model in a Square Transverse Jet in Crossflow

Fabíola Paula Costa, Rubén Bruno Díaz, Pedro M. Milani, Jesuíno Takachi Tomita, Cleverson Bringhenti

Proc. ASME. GT2020, Volume 7B: Heat Transfer, V07BT12A030, September 21–25, 2020

Publisher: American Society of Mechanical Engineers

Paper No: GT2020-14811

https://doi.org/10.1115/GT2020-14811

View Paper titled, Evaluation of a Machine Learning Turbulence Model in a Square Transverse Jet in Crossflow

Topics: Film cooling, Heat transfer, Machine learning, Turbulence

Journal Articles

The Discrete Green's Function for Convective Heat Transfer—Part 2: Semi-Analytical Estimates of Boundary Layer Discrete Green's Function

John K. Eaton, Pedro M. Milani

Publisher: ASME

J. Heat Mass Transfer. October 2020, 142(10): 102102.

Paper No: HT-20-1162

https://doi.org/10.1115/1.4047516

Published Online: July 10, 2020

View Article titled, The Discrete Green's Function for Convective Heat Transfer—Part 2: Semi-Analytical Estimates of Boundary Layer Discrete Green's Function

Topics: Boundary layer turbulence, Boundary layers, Flow (Dynamics), Skin friction (Fluid dynamics), Heat transfer, Flat plates

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 Article titled, 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

Proceedings Papers

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

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

Proc. ASME. GT2019, Volume 5A: Heat Transfer, V05AT12A008, June 17–21, 2019

Publisher: American Society of Mechanical Engineers

Paper No: GT2019-90498

https://doi.org/10.1115/GT2019-90498

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

Topics: Film cooling, Flow (Dynamics), Heat flux, Machinery, Turbulence, Design, Diffusion (Physics), Engineering simulation, Heat transfer, Machine learning

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 Article titled, 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

Proceedings Papers

Physical Interpretation of Machine Learning Models Applied to Film Cooling Flows

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

Proc. ASME. GT2018, Volume 5A: Heat Transfer, V05AT12A019, June 11–15, 2018

Publisher: American Society of Mechanical Engineers

Paper No: GT2018-76927

https://doi.org/10.1115/GT2018-76927

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

Topics: Film cooling, Flow (Dynamics), Machine learning, Turbulence, Diffusion (Physics), Eddies (Fluid dynamics), Heat flux, Temperature distribution, Viscosity

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 Article titled, 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

Proceedings Papers

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

Proc. ASME. GT2017, Volume 5A: Heat Transfer, V05AT12A002, June 26–30, 2017

Publisher: American Society of Mechanical Engineers

Paper No: GT2017-63299

https://doi.org/10.1115/GT2017-63299

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

Topics: Film cooling, Flow (Dynamics), Machine learning, Turbulence, Reynolds-averaged Navier–Stokes equations, Algorithms, Diffusion (Physics), Heat flux, Modeling, Temperature

Journal citation

NARROW

Evaluation of a Machine Learning Turbulence Model in a Square Transverse Jet in Crossflow

The Discrete Green's Function for Convective Heat Transfer—Part 2: Semi-Analytical Estimates of Boundary Layer Discrete Green's Function

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

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

Physical Interpretation of Machine Learning Models Applied to Film Cooling Flows

Physical Interpretation of Machine Learning Models Applied to Film Cooling Flows

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

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

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