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Proc. ASME. GT2021, Volume 2B: Turbomachinery — Axial Flow Turbine Aerodynamics; Deposition, Erosion, Fouling, and Icing, V02BT32A002, June 7–11, 2021

Paper No: GT2021-58816

DOI: https://doi.org/10.1115/GT2021-58816

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Topics: Engineering simulation, High pressure (Physics), Simulation, Temperature profiles, Turbines, Boundary layers, Blades, Pressure, Suction, Combustion chambers

Proceedings Papers

High-Fidelity Simulations of a High-Pressure Turbine Stage: Effects of Reynolds Number and Inlet Turbulence

Yaomin Zhao, Richard D. Sandberg

Proc. ASME. GT2021, Volume 2B: Turbomachinery — Axial Flow Turbine Aerodynamics; Deposition, Erosion, Fouling, and Icing, V02BT32A004, June 7–11, 2021

Paper No: GT2021-58995

DOI: https://doi.org/10.1115/GT2021-58995

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Topics: Engineering simulation, High pressure (Physics), Reynolds number, Simulation, Turbines, Turbulence, Rotors, Wakes, Blades, Flow (Dynamics)

Proceedings Papers

Turbomachinery Loss Analysis: The Relationship Between Mechanical Work Potential and Entropy Analyses

John Leggett, Yaomin Zhao, Edward S. Richardson, Richard D. Sandberg

Proc. ASME. GT2021, Volume 2C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, V02CT34A023, June 7–11, 2021

Paper No: GT2021-59436

DOI: https://doi.org/10.1115/GT2021-59436

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Topics: Entropy, Turbomachinery, Energy dissipation, High pressure (Physics), Simulation, Turbulence, Compressors, Computational fluid dynamics, Physics, Pressure

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

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

Paper No: TURBO-20-1427

DOI: https://doi.org/10.1115/1.4051417

Published Online: June 23, 2021

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

Journal Articles

Large-Eddy Simulations of High Rossby Number Flow in the High-Pressure Compressor Inter-Disk Cavity

Deepak Saini, Richard D. Sandberg

J. Turbomach. November 2021, 143(11): 111002.

Paper No: TURBO-20-1206

DOI: https://doi.org/10.1115/1.4050951

Published Online: June 15, 2021

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Topics: Cavities, Disks, Flow (Dynamics), Compressors, High pressure (Physics), Vortices, Buoyancy, Large eddy simulation, Heat transfer

Journal Articles

High-Fidelity Simulations of Multi-Jet Impingement Cooling Flows

J. Javier Otero-Pérez, Richard D. Sandberg, Satoshi Mizukami, Koichi Tanimoto

J. Turbomach. August 2021, 143(8): 081011.

Paper No: TURBO-20-1346

DOI: https://doi.org/10.1115/1.4050446

Published Online: May 3, 2021

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Topics: Cross-flow, Flow (Dynamics), Heat transfer, Impingement cooling, Inflow, Simulation, Turbulence, Jets, Deflection

Journal Articles

High-Fidelity Simulations of a High-Pressure Turbine Vane Subject to Large Disturbances: Effect of Exit Mach Number on Losses

Yaomin Zhao, Richard D. Sandberg

J. Turbomach. September 2021, 143(9): 091002.

Paper No: TURBO-20-1392

DOI: https://doi.org/10.1115/1.4050453

Published Online: May 3, 2021

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Topics: Blades, Boundary layers, Entropy, Flow (Dynamics), Mach number, Shock (Mechanics), Simulation, Turbulence, Turbines, Pressure

Proceedings Papers

High-Fidelity Simulations of Multi-Jet Impingement Cooling Flows

J. Javier Otero-Pérez, Richard D. Sandberg, Satoshi Mizukami, Koichi Tanimoto

Proc. ASME. GT2020, Volume 7A: Heat Transfer, V07AT15A011, September 21–25, 2020

Paper No: GT2020-14728

DOI: https://doi.org/10.1115/GT2020-14728

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Topics: Engineering simulation, Flow (Dynamics), Impingement cooling, Simulation, Cross-flow, Heat transfer, Turbulence, Inflow, Computer simulation, Deflection

Proceedings Papers

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

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

Proc. ASME. GT2020, Volume 7A: Heat Transfer, V07AT15A026, September 21–25, 2020

Paper No: GT2020-15928

DOI: https://doi.org/10.1115/GT2020-15928

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Topics: Coolants, Machine learning, Turbulence, Design, Cooling, Geometry, Heat transfer, Reynolds-averaged Navier–Stokes equations, Additive manufacturing, Computer programming

Proceedings Papers

Unsteady Simulations of a Trailing-Edge Slot Using Machine-Learnt Turbulence Stress and Heat-Flux Closures

Chitrarth Lav, Richard D. Sandberg

Proc. ASME. GT2020, Volume 7B: Heat Transfer, V07BT12A012, September 21–25, 2020

Paper No: GT2020-14398

DOI: https://doi.org/10.1115/GT2020-14398

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Topics: Engineering simulation, Heat flux, Machinery, Simulation, Stress, Turbulence, Flow (Dynamics), Anisotropy, Machine learning, Reynolds-averaged Navier–Stokes equations

Proceedings Papers

Large Eddy Simulations of High Rossby Number Flow in the High Pressure Compressor Inter-Disk Cavity

Deepak Saini, Richard D. Sandberg

Proc. ASME. GT2020, Volume 7C: Heat Transfer, V07CT14A009, September 21–25, 2020

Paper No: GT2020-14463

DOI: https://doi.org/10.1115/GT2020-14463

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Topics: Cavities, Compressors, Disks, Flow (Dynamics), High pressure (Physics), Large eddy simulation, Rayleigh number, Buoyancy, Cooling systems, Gas turbines

Proceedings Papers

High-Fidelity Simulations of a High-Pressure Turbine Vane Subject to Large Disturbances: Effect of Exit Mach Number on Losses

Yaomin Zhao, Richard D. Sandberg

Proc. ASME. GT2020, Volume 2B: Turbomachinery, V02BT33A007, September 21–25, 2020

Paper No: GT2020-14445

DOI: https://doi.org/10.1115/GT2020-14445

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Topics: Engineering simulation, High pressure (Physics), Mach number, Simulation, Turbines, Blades, Turbulence, Boundary layers, Flow (Dynamics), Shock (Mechanics)

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

Paper No: GT2020-14732

DOI: https://doi.org/10.1115/GT2020-14732

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Topics: Computational fluid dynamics, Machine learning, Turbines, Turbulence, Wakes, Reynolds-averaged Navier–Stokes equations, Model development, Computer programming, Diffusion (Physics), Modeling

Journal Articles

Using a New Entropy Loss Analysis to Assess the Accuracy of RANS Predictions of an High-Pressure Turbine Vane

Yaomin Zhao, Richard D. Sandberg

J. Turbomach. August 2020, 142(8): 081008.

Paper No: TURBO-20-1017

DOI: https://doi.org/10.1115/1.4046531

Published Online: July 29, 2020

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Topics: Entropy, Reynolds-averaged Navier–Stokes equations, Flow (Dynamics), Turbines

Proceedings Papers

Using a New Entropy Loss Analysis to Assess the Accuracy of RANS Predictions of an HPT Vane

Yaomin Zhao, Richard D. Sandberg

Proc. ASME. GT2019, Volume 2C: Turbomachinery, V02CT41A003, June 17–21, 2019

Paper No: GT2019-90126

DOI: https://doi.org/10.1115/GT2019-90126

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Topics: Entropy, Reynolds-averaged Navier–Stokes equations, Turbulence, Flow (Dynamics), Energy dissipation, Heat flux, Wakes, Errors, Failure, Fluctuations (Physics)

Proceedings Papers

A New Data-Driven Turbulence Model Framework for Unsteady Flows Applied to Wall-Jet and Wall-Wake Flows

Chitrarth Lav, Jimmy Philip, Richard D. Sandberg

Proc. ASME. GT2019, Volume 2A: Turbomachinery, V02AT45A002, June 17–21, 2019

Paper No: GT2019-90179

DOI: https://doi.org/10.1115/GT2019-90179

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Topics: Flow (Dynamics), Turbulence, Unsteady flow, Wakes, Machine learning, Algorithms, Vortex shedding, Geometry, Model development, Momentum

Proceedings Papers

Large Eddy Simulations of a Low-Pressure Turbine: Roughness Modeling and the Effects on Boundary Layer Transition and Losses

F. Hammer, Neil D. Sandham, Richard D. Sandberg

Proc. ASME. GT2018, Volume 2B: Turbomachinery, V02BT41A014, June 11–15, 2018

Paper No: GT2018-75796

DOI: https://doi.org/10.1115/GT2018-75796

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Topics: Boundary layers, Large eddy simulation, Modeling, Pressure, Surface roughness, Turbines, Boundary layer turbulence, Flow (Dynamics), Blades, Cascades (Fluid dynamics)

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

Paper No: GT2018-75447

DOI: https://doi.org/10.1115/GT2018-75447

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Topics: Algebra, Machinery, Stress, Wakes, Reynolds-averaged Navier–Stokes equations, Turbulence, Flow (Dynamics), Algorithms, Anisotropy, Blades

Journal citation

NARROW

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

Highly Resolved Simulations of a CDA Compressor Cascade: Effect of Reynolds Number on Losses

High-Fidelity Simulations of a High-Pressure Turbine Vane With End Walls: Impact of Secondary Structures and Spanwise Temperature Profiles on Losses

High-Fidelity Simulations of a High-Pressure Turbine Stage: Effects of Reynolds Number and Inlet Turbulence

Turbomachinery Loss Analysis: The Relationship Between Mechanical Work Potential and Entropy Analyses

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

Large-Eddy Simulations of High Rossby Number Flow in the High-Pressure Compressor Inter-Disk Cavity

High-Fidelity Simulations of Multi-Jet Impingement Cooling Flows

High-Fidelity Simulations of a High-Pressure Turbine Vane Subject to Large Disturbances: Effect of Exit Mach Number on Losses

High-Fidelity Simulations of Multi-Jet Impingement Cooling Flows

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

Unsteady Simulations of a Trailing-Edge Slot Using Machine-Learnt Turbulence Stress and Heat-Flux Closures

Large Eddy Simulations of High Rossby Number Flow in the High Pressure Compressor Inter-Disk Cavity

High-Fidelity Simulations of a High-Pressure Turbine Vane Subject to Large Disturbances: Effect of Exit Mach Number on Losses

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

Using a New Entropy Loss Analysis to Assess the Accuracy of RANS Predictions of an High-Pressure Turbine Vane

Using a New Entropy Loss Analysis to Assess the Accuracy of RANS Predictions of an HPT Vane

A New Data-Driven Turbulence Model Framework for Unsteady Flows Applied to Wall-Jet and Wall-Wake Flows

Large Eddy Simulations of a Low-Pressure Turbine: Roughness Modeling and the Effects on Boundary Layer Transition and Losses

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

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