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Harshal D. Akolekar
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Proceedings Papers

Exploiting a Transformer Architecture for Simultaneous Development of Transition and Turbulence Models for Turbine Flow Predictions

Yuan Fang, Maximilian Reissmann, Roberto Pacciani, Yaomin Zhao, Andrew S. H. Ooi, Michele Marconcini, Harshal D. Akolekar, Richard D. Sandberg
Proc. ASME. GT2024, Volume 12C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, V12CT32A023, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-125550
https://doi.org/10.1115/GT2024-125550
View Paper titled, Exploiting a Transformer Architecture for Simultaneous Development of Transition and Turbulence Models for Turbine Flow Predictions
Open the PDF for Exploiting a Transformer Architecture for Simultaneous Development of Transition and Turbulence Models for Turbine Flow Predictions in another window
Topics: Flow (Dynamics), Turbines, Turbulence, Computational fluid dynamics, Errors, Reynolds-averaged Navier–Stokes equations, Computer programming, Machine learning, Natural language processing, Physics
Journal Articles

A Data-Driven Approach for Generalizing the Laminar Kinetic Energy Model for Separation and Bypass Transition in Low- and High-Pressure Turbines

Yuan Fang, Yaomin Zhao, Harshal D. Akolekar, Andrew S. H. Ooi, Richard D. Sandberg, Roberto Pacciani, Michele Marconcini
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. September 2024, 146(9): 091005.
Paper No: TURBO-23-1139
https://doi.org/10.1115/1.4065124
Published Online: April 4, 2024
View Article titled, A Data-Driven Approach for Generalizing the Laminar Kinetic Energy Model for Separation and Bypass Transition in Low- and High-Pressure Turbines
Open the PDF for A Data-Driven Approach for Generalizing the Laminar Kinetic Energy Model for Separation and Bypass Transition in Low- and High-Pressure Turbines in another window
Topics: Computational fluid dynamics, Kinetic energy, Pressure, Separation (Technology), Shear stress, Turbines, Turbulence, Wakes, High pressure (Physics), Flow (Dynamics)
Proceedings Papers

A Data-Driven Approach for Generalizing the Laminar Kinetic Energy Model for Separation and Bypass Transition in Low- and High-Pressure Turbines

Yuan Fang, Yaomin Zhao, Harshal D. Akolekar, Andrew S. H. Ooi, Richard D. Sandberg, Roberto Pacciani, Michele Marconcini
Proc. ASME. GT2023, Volume 13C: Turbomachinery — Deposition, Erosion, Fouling, and Icing; Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, V13CT32A025, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-102902
https://doi.org/10.1115/GT2023-102902
View Paper titled, A Data-Driven Approach for Generalizing the Laminar Kinetic Energy Model for Separation and Bypass Transition in Low- and High-Pressure Turbines
Open the PDF for A Data-Driven Approach for Generalizing the Laminar Kinetic Energy Model for Separation and Bypass Transition in Low- and High-Pressure Turbines in another window
Topics: High pressure (Physics), Kinetic energy, Separation (Technology), Turbines, Computational fluid dynamics, Wakes, Pressure, Turbulence, Acoustics, Blades
Proceedings Papers

Multi-Objective Development of Machine-Learnt Closures for Fully Integrated Transition and Wake Mixing Predictions in Low Pressure Turbines

Harshal D. Akolekar, Fabian Waschkowski, Roberto Pacciani, Yaomin Zhao, Richard D. Sandberg
Proc. ASME. GT2022, Volume 10C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, V10CT32A013, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-81091
https://doi.org/10.1115/GT2022-81091
View Paper titled, Multi-Objective Development of Machine-Learnt Closures for Fully Integrated Transition and Wake Mixing Predictions in Low Pressure Turbines
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Topics: Machinery, Pressure, Turbines, Wakes, Blades, Computational fluid dynamics, Machine learning, Boundary layers, Reynolds number, Reynolds-averaged Navier–Stokes equations
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
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
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 Article titled, Integration of Machine Learning and Computational Fluid Dynamics to Develop Turbulence Models for Improved Low-Pressure Turbine Wake Mixing Prediction
Open the PDF for Integration of Machine Learning and Computational Fluid Dynamics to Develop Turbulence Models for Improved Low-Pressure Turbine Wake Mixing Prediction in another window
Topics: Blades, Computational fluid dynamics, Reynolds-averaged Navier–Stokes equations, Turbines, Wakes, Flow (Dynamics), Turbulence, Pressure, Model development, Machine learning
Proceedings Papers

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

Harshal D. Akolekar, Yaomin Zhao, Richard D. Sandberg, Roberto Pacciani
Proc. ASME. GT2020, Volume 2C: Turbomachinery, V02CT35A020, September 21–25, 2020
Publisher: American Society of Mechanical Engineers
Paper No: GT2020-14732
https://doi.org/10.1115/GT2020-14732
View Paper titled, Integration of Machine Learning and Computational Fluid Dynamics to Develop Turbulence Models for Improved Turbine Wake Mixing Prediction
Open the PDF for Integration of Machine Learning and Computational Fluid Dynamics to Develop Turbulence Models for Improved Turbine Wake Mixing Prediction in another window
Topics: Computational fluid dynamics, Machine learning, Turbines, Turbulence, Wakes, Reynolds-averaged Navier–Stokes equations, Model development, Computer programming, Diffusion (Physics), Modeling
Proceedings Papers

Development and Use of Machine-Learnt Algebraic Reynolds Stress Models for Enhanced Prediction of Wake Mixing in LPTs

Harshal D. Akolekar, Jack Weatheritt, Nicholas Hutchins, Richard D. Sandberg, Gregory Laskowski, Vittorio Michelassi
Proc. ASME. GT2018, Volume 2C: Turbomachinery, V02CT42A009, June 11–15, 2018
Publisher: American Society of Mechanical Engineers
Paper No: GT2018-75447
https://doi.org/10.1115/GT2018-75447
View Paper titled, Development and Use of Machine-Learnt Algebraic Reynolds Stress Models for Enhanced Prediction of Wake Mixing in LPTs
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Topics: Algebra, Machinery, Stress, Wakes, Reynolds-averaged Navier–Stokes equations, Turbulence, Flow (Dynamics), Algorithms, Anisotropy, Blades