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Takuo Oda
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Journal Articles

Improving a Two-Equation Eddy-Viscosity Turbulence Model for High-Rayleigh-Number Natural-Convection Flows Using Machine Learning

Ali Haghiri, Xiaowei Xu, Richard D. Sandberg, Koichi Tanimoto, Takuo Oda
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. January 2025, 147(1): 011026.
Paper No: GTP-24-1407
https://doi.org/10.1115/1.4066594
Published Online: October 25, 2024
View Article titled, Improving a Two-Equation Eddy-Viscosity Turbulence Model for High-Rayleigh-Number Natural-Convection Flows Using Machine Learning
Open the PDF for Improving a Two-Equation Eddy-Viscosity Turbulence Model for High-Rayleigh-Number Natural-Convection Flows Using Machine Learning in another window
Topics: Flow (Dynamics), Heat flux, Modeling, Natural convection, Reynolds stress, Turbulence, Eddies (Fluid dynamics), Viscosity, Temperature, Testing
Proceedings Papers

Improving a Two-Equation Eddy-Viscosity Turbulence Model for High-Rayleigh-Number Natural-Convection Flows Using Machine Learning

Ali Haghiri, Xiaowei Xu, Richard D. Sandberg, Koichi Tanimoto, Takuo Oda
Proc. ASME. GT2024, Volume 13: Heat Transfer: General Interest / Additive Manufacturing Impacts on Heat Transfer; Wind Energy, V013T13A003, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-121161
https://doi.org/10.1115/GT2024-121161
View Paper titled, Improving a Two-Equation Eddy-Viscosity Turbulence Model for High-Rayleigh-Number Natural-Convection Flows Using Machine Learning
Open the PDF for Improving a Two-Equation Eddy-Viscosity Turbulence Model for High-Rayleigh-Number Natural-Convection Flows Using Machine Learning in another window
Topics: Eddies (Fluid dynamics), Flow (Dynamics), Machine learning, Modeling, Natural convection, Turbulence, Viscosity
Proceedings Papers

High-Fidelity Computational Assessment of Aero-Thermal Performance and the Reynolds’ Analogy for Additively Manufactured Anisotropic Surface Roughness

Thomas O. Jelly, Wagih Abu Rowin, Nicholas Hutchins, Daniel Chung, Koichi Tanimoto, Takuo Oda, Richard D. Sandberg
Proc. ASME. GT2023, Volume 7B: Heat Transfer — General Interest/Additive Manufacturing Impacts on Heat Transfer; Internal Air Systems; Internal Cooling, V07BT13A005, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-102138
https://doi.org/10.1115/GT2023-102138
View Paper titled, High-Fidelity Computational Assessment of Aero-Thermal Performance and the Reynolds’ Analogy for Additively Manufactured Anisotropic Surface Roughness
Open the PDF for High-Fidelity Computational Assessment of Aero-Thermal Performance and the Reynolds’ Analogy for Additively Manufactured Anisotropic Surface Roughness in another window
Topics: Additive manufacturing, Anisotropy, Heat transfer, Surface roughness, Turbulence, Flow (Dynamics), Pressure, Channel flow, Computational fluid dynamics, Computer simulation
Journal Articles

High-Fidelity Computational Assessment of Aero-Thermal Performance and the Reynolds’ Analogy for Additively Manufactured Anisotropic Surface Roughness

Thomas O. Jelly, Wagih Abu Rowin, Nicholas Hutchins, Daniel Chung, Koichi Tanimoto, Takuo Oda, Richard D. Sandberg
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. November 2023, 145(11): 111005.
Paper No: TURBO-23-1148
https://doi.org/10.1115/1.4063298
Published Online: September 14, 2023
View Article titled, High-Fidelity Computational Assessment of Aero-Thermal Performance and the Reynolds’ Analogy for Additively Manufactured Anisotropic Surface Roughness
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Topics: Surface roughness, Turbulence, Heat transfer, Additive manufacturing
Proceedings Papers

Study on the Applicability of a Machine-Learning Framework to Improve Modeling for the Stratification Phenomenon

Takuo Oda, Shimpei Okayasu, Nobuhide Hara, Koichi Tanimoto, Ali Haghiri, Xiaowei Xu, Richard Sandberg
Proc. ASME. ICONE29, Volume 8: Computational Fluid Dynamics (CFD) and Applications, V008T08A026, August 8–12, 2022
Publisher: American Society of Mechanical Engineers
Paper No: ICONE29-91829
https://doi.org/10.1115/ICONE29-91829
View Paper titled, Study on the Applicability of a Machine-Learning Framework to Improve Modeling for the Stratification Phenomenon
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Topics: Computer programming, Machine learning, Modeling, Thermal stratification, Turbulence