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Proceedings Papers

Highly Resolved Large-Eddy Simulations of a Transonic Compressor Stage Midspan Section Part I: Effect of Inflow Disturbances

Christoph Bode, Pawel J. Przytarski, John Leggett, Richard D. Sandberg

Proc. ASME. GT2022, Volume 10D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Turbomachinery General Interest; Unsteady Flows in Turbomachinery, V10DT37A011, June 13–17, 2022

Paper No: GT2022-81673

DOI: https://doi.org/10.1115/GT2022-81673

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Topics: Compressors, Inflow, Large eddy simulation, Shock (Mechanics), Boundary layers, Rotors, Suction, Flow (Dynamics), Stators, Turbulence

Proceedings Papers

High-Fidelity Simulation Study of the Unsteady Flow Effects on High-Pressure Turbine Blade Performance

John Leggett, Yaomin Zhao, Richard D. Sandberg

Paper No: GT2022-82370

DOI: https://doi.org/10.1115/GT2022-82370

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Topics: High pressure (Physics), Simulation, Turbine blades, Unsteady flow, Turbulence, Blades, Rotors, Wakes, Boundary layers, Flow (Dynamics)

Proceedings Papers

Profile Loss Reduction of High Lift Turbine Blades With Rough and Ribbed Surfaces

S. M. Ananth, Massimiliano Nardini, Aditya Vaid, Richard D. Sandberg, Nagabhushana Rao Vadlamani

Proc. ASME. GT2022, Volume 10B: Turbomachinery — Axial Flow Turbine Aerodynamics; Deposition, Erosion, Fouling, and Icing; Radial Turbomachinery Aerodynamics, V10BT30A026, June 13–17, 2022

Paper No: GT2022-82558

DOI: https://doi.org/10.1115/GT2022-82558

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Topics: Surface roughness, Turbine blades, Blades, Turbulence, Boundary layers, Pressure gradient, Separation (Technology), Suction, Boundary layer turbulence, Bubbles

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

Paper No: GT2022-81091

DOI: https://doi.org/10.1115/GT2022-81091

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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

Profile Loss Reduction of High-Lift Turbine Blades With Rough and Ribbed Surfaces

Ananth Sivaramakrishnan Malathi, Massimiliano Nardini, Aditya Vaid, Nagabhushana Rao Vadlamani, Richard D. Sandberg

J. Turbomach. February 2023, 145(2): 021001.

Paper No: TURBO-22-1214

DOI: https://doi.org/10.1115/1.4055501

Published Online: October 11, 2022

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Topics: Boundary layers, Flow (Dynamics), Pressure gradient, Suction, Surface roughness, Turbulence, Simulation, Skin friction (Fluid dynamics), Turbine blades, Shapes

Journal Articles

High-Fidelity Simulation Study of the Unsteady Flow Effects on High-Pressure Turbine Blade Performance

John Leggett, Yaomin Zhao, Richard D. Sandberg

J. Turbomach. January 2023, 145(1): 011002.

Paper No: TURBO-22-1215

DOI: https://doi.org/10.1115/1.4055576

Published Online: October 3, 2022

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Topics: Blades, Boundary layers, Flow (Dynamics), Pressure, Rotors, Stators, Suction, Turbulence, Wakes, High pressure (Physics)

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

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

Paper No: TURBO-20-1174

DOI: https://doi.org/10.1115/1.4053533

Published Online: March 3, 2022

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

Proceedings Papers

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

John Leggett, Richard D. Sandberg

Proc. ASME. GT2021, Volume 2A: Turbomachinery — Axial Flow Fan and Compressor Aerodynamics, V02AT31A009, June 7–11, 2021

Paper No: GT2021-58665

DOI: https://doi.org/10.1115/GT2021-58665

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Topics: Cascades (Fluid dynamics), Compressors, Engineering simulation, Reynolds number, Simulation, Blades, Geometry, Wakes, Airfoils, Boundary layers

Proceedings Papers

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

Yaomin Zhao, Richard D. Sandberg

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 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

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

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

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

Journal citation

NARROW

Highly Resolved Large-Eddy Simulations of a Transonic Compressor Stage Midspan Section Part I: Effect of Inflow Disturbances

High-Fidelity Simulation Study of the Unsteady Flow Effects on High-Pressure Turbine Blade Performance

Profile Loss Reduction of High Lift Turbine Blades With Rough and Ribbed Surfaces

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

Profile Loss Reduction of High-Lift Turbine Blades With Rough and Ribbed Surfaces

High-Fidelity Simulation Study of the Unsteady Flow Effects on High-Pressure Turbine Blade Performance

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 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

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

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

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