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Journal Articles

Physics-Guided Graph Neural Networks: Numerical Investigation of High-Pressure Turbine Flow

Open Access
Zhihui Li, Francesco Montomoli
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. December 2025, 147(12): 121005.
Paper No: TURBO-24-1133
https://doi.org/10.1115/1.4068832
Published Online: June 30, 2025
View Articletitled, Physics-Guided Graph Neural Networks: Numerical Investigation of High-Pressure Turbine Flow
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Topics: Computational fluid dynamics, Flow (Dynamics), Graph neural networks, Physics, Turbines, Inverse problems, Pressure, High pressure (Physics)
Journal Articles

DEVELOPMENT OF MACHINE-LEARNT TURBULENCE CLOSURES FOR WAKE MIXING PREDICTIONS IN LOW-PRESSURE TURBINES

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Accepted Manuscript
Yuri Frey Marioni, Paolo Adami, Raul Vazquez Diaz, Andrea Cassinelli, Spencer J. Sherwin, Francesco Montomoli
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach.
Paper No: TURBO-22-1316
https://doi.org/10.1115/1.4066919
Published Online: October 17, 2024
View Articletitled, DEVELOPMENT OF MACHINE-LEARNT TURBULENCE CLOSURES FOR WAKE MIXING PREDICTIONS IN LOW-PRESSURE TURBINES
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Topics: Machinery, Pressure, Turbines, Turbochargers, Turbulence, Wakes, Artificial neural networks, Computational fluid dynamics, Aircraft engines, Algebra
Journal Articles

Characteristics of Deterministic and Stochastic Unsteadiness of Trailing Edge Cutback Film Cooling Flows

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Xiao He, Francesco Montomoli, Vittorio Michelassi, Andrea Panizza, Leonardo Pulga
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. January 2025, 147(1): 011009.
Paper No: TURBO-24-1115
https://doi.org/10.1115/1.4066276
Published Online: September 3, 2024
View Articletitled, Characteristics of Deterministic and Stochastic Unsteadiness of Trailing Edge Cutback Film Cooling Flows
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Topics: Anisotropy, Cooling, Film cooling, Flow (Dynamics), Heat flux, Reynolds-averaged Navier–Stokes equations, Stress tensors, Turbulence, Vortex shedding, Kinetic energy
Proceedings Papers

Characteristics of Deterministic and Stochastic Unsteadiness of Trailing Edge Cutback Film Cooling Flows

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Xiao He, Francesco Montomoli, Vittorio Michelassi, Andrea Panizza, Leonardo Pulga
Proc. ASME. GT2024, Volume 7: Heat Transfer: Combustors; Heat Transfer: Film Cooling, V007T12A011, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-123002
https://doi.org/10.1115/GT2024-123002
View Paper titled, Characteristics of Deterministic and Stochastic Unsteadiness of Trailing Edge Cutback Film Cooling Flows
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Topics: Film cooling, Flow (Dynamics), Heat flux, Stress tensors, Cooling, Errors, Anisotropy, Cooling systems, Design, Detached eddy simulation
Proceedings Papers

Applications and Recent Developments of the Open-Source Computational Fluid Dynamics High-Fidelity Spectral/Hp Element Framework Nektar++ for Turbomachinery Configurations

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Guglielmo Vivarelli, João Anderson Isler, Francesco Montomoli, Chris Cantwell, Spencer J. Sherwin, Yuri Frey-Marioni, Raul Vazquez-Diaz
Proc. ASME. GT2024, Volume 12C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, V12CT32A022, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-125039
https://doi.org/10.1115/GT2024-125039
View Paper titled, Applications and Recent Developments of the Open-Source Computational Fluid Dynamics High-Fidelity Spectral/Hp Element Framework Nektar++ for Turbomachinery Configurations
Open the PDF for Applications and Recent Developments of the Open-Source Computational Fluid Dynamics High-Fidelity Spectral/Hp Element Framework Nektar++ for Turbomachinery Configurations in another window
Topics: Computational fluid dynamics, Turbomachinery, Hardware, Blades, Boundary layers, Computer simulation, Degrees of freedom, Design, Engines, Flat plates
Proceedings Papers

Physics Guided Graph Neural Networks (PG-GNNs): Numerical Investigation of High-Pressure Turbine Flow

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Zhihui Li, Francesco Montomoli
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A012, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-123649
https://doi.org/10.1115/GT2024-123649
View Paper titled, Physics Guided Graph Neural Networks (PG-GNNs): Numerical Investigation of High-Pressure Turbine Flow
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Topics: Flow (Dynamics), Graph neural networks, High pressure (Physics), Inverse problems, Physics, Turbines
Journal Articles

High-Dimensional Uncertainty Quantification of High-Pressure Turbine Vane Based on Multifidelity Deep Neural Networks

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Zhihui Li, Francesco Montomoli, Nicola Casari, Michele Pinelli
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. November 2023, 145(11): 111009.
Paper No: TURBO-23-1153
https://doi.org/10.1115/1.4063391
Published Online: October 4, 2023
View Articletitled, High-Dimensional Uncertainty Quantification of High-Pressure Turbine Vane Based on Multifidelity Deep Neural Networks
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Topics: Artificial neural networks, Computational fluid dynamics, Design, Flow (Dynamics), Nozzles, Pressure, Turbines, Uncertainty quantification, Turbomachinery, Uncertainty
Proceedings Papers

High-Dimensional Uncertainty Quantification of High-Pressure Turbine Vane Based on Multi-Fidelity Deep Neural Networks

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Zhihui Li, Francesco Montomoli, Nicola Casari, Michele Pinelli
Proc. ASME. GT2023, Volume 13D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V13DT34A007, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-101698
https://doi.org/10.1115/GT2023-101698
View Paper titled, High-Dimensional Uncertainty Quantification of High-Pressure Turbine Vane Based on Multi-Fidelity Deep Neural Networks
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Topics: Artificial neural networks, High pressure (Physics), Nozzles, Turbines, Uncertainty quantification
Proceedings Papers

High-Order Spectral/hp Compressible and Incompressible Comparison of Transitional Boundary-Layers Subject to a Realistic Pressure Gradient and High Reynolds Number

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Guglielmo Vivarelli, João Anderson Isler, Francesco Montomoli, Spencer J. Sherwin, Paolo Adami
Proc. ASME. GT2022, Volume 10C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, V10CT32A024, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-82100
https://doi.org/10.1115/GT2022-82100
View Paper titled, High-Order Spectral/hp Compressible and Incompressible Comparison of Transitional Boundary-Layers Subject to a Realistic Pressure Gradient and High Reynolds Number
Open the PDF for High-Order Spectral/hp Compressible and Incompressible Comparison of Transitional Boundary-Layers Subject to a Realistic Pressure Gradient and High Reynolds Number in another window
Topics: Boundary layers, Pressure gradient, Reynolds number, Flow (Dynamics), Turbulence, Bubbles, Compressibility, Compressible flow, Computer simulation, Cross-flow
Proceedings Papers

Development of Machine-Learnt Turbulence Closures for Wake Mixing Predictions in Low-Pressure Turbines

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Yuri Frey Marioni, Paolo Adami, Raul Vazquez Diaz, Andrea Cassinelli, Spencer Sherwin, Francesco Montomoli
Proc. ASME. GT2022, Volume 10C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, V10CT32A032, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-82531
https://doi.org/10.1115/GT2022-82531
View Paper titled, Development of Machine-Learnt Turbulence Closures for Wake Mixing Predictions in Low-Pressure Turbines
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Topics: Machinery, Pressure, Turbines, Turbulence, Wakes, Artificial neural networks, Aircraft engines, Algebra, Flow (Dynamics), Kinetic energy
Journal Articles

A Non-Parametric Histogram Interpolation Method for Design Space Exploration

Available to Purchase
Nick Pepper, Francesco Montomoli, Sanjiv Sharma
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. August 2022, 144(8): 081703.
Paper No: MD-21-1637
https://doi.org/10.1115/1.4054085
Published Online: April 8, 2022
View Articletitled, A Non-Parametric Histogram Interpolation Method for Design Space Exploration
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Topics: Algorithms, Design, Interpolation, Polynomials, Statistical distributions, Uncertainty
Journal Articles

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

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James Hammond, Francesco Montomoli, Marco Pietropaoli, Richard D. Sandberg, Vittorio Michelassi
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. August 2022, 144(8): 081003.
Paper No: TURBO-20-1174
https://doi.org/10.1115/1.4053533
Published Online: March 3, 2022
View Articletitled, Machine Learning for the Development of Data-Driven Turbulence Closures in Coolant Systems
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Topics: Anisotropy, Design, Errors, Flow (Dynamics), Geometry, Heat transfer, Reynolds-averaged Navier–Stokes equations, Tensors, Testing, Turbulence
Proceedings Papers

Towards a Machine Learning Based Design for Fouling of an Axial Turbine Vane

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Riccardo Friso, Stefano Oliani, Nicola Casari, Michele Pinelli, Alessio Suman, Francesco Montomoli
Proc. ASME. GT2021, Volume 2C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, V02CT34A027, June 7–11, 2021
Publisher: American Society of Mechanical Engineers
Paper No: GT2021-59520
https://doi.org/10.1115/GT2021-59520
View Paper titled, Towards a Machine Learning Based Design for Fouling of an Axial Turbine Vane
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Topics: Design, Machine learning, Turbines, Design methodology, Computational fluid dynamics, Engines, Sensitivity analysis, Turbomachinery, Aircraft engines, Approximation
Proceedings Papers

Uncertainty Quantification and Missing Data for Turbomachinery With Probabilistic Equivalence and Arbitrary Polynomial Chaos, Applied to Scroll Compressors

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Nick Pepper, Francesco Montomoli, Francesco Giacomel, Giovanna Cavazzini, Michele Pinelli, Nicola Casari, Sanjiv Sharma
Proc. ASME. GT2020, Volume 10B: Structures and Dynamics, V10BT28A007, September 21–25, 2020
Publisher: American Society of Mechanical Engineers
Paper No: GT2020-16139
https://doi.org/10.1115/GT2020-16139
View Paper titled, Uncertainty Quantification and Missing Data for Turbomachinery With Probabilistic Equivalence and Arbitrary Polynomial Chaos, Applied to Scroll Compressors
Open the PDF for Uncertainty Quantification and Missing Data for Turbomachinery With Probabilistic Equivalence and Arbitrary Polynomial Chaos, Applied to Scroll Compressors in another window
Topics: Chaos, Compressors, Polynomials, Turbomachinery, Uncertainty quantification, Statistical distributions, Additive manufacturing, Blocks (Building materials), Carbon fibers, Computer simulation
Proceedings Papers

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

Available to Purchase

James Hammond, Francesco Montomoli, Marco Pietropaoli, Richard D. Sandberg, Vittorio Michelassi
Proc. ASME. GT2020, Volume 7A: Heat Transfer, V07AT15A026, September 21–25, 2020
Publisher: American Society of Mechanical Engineers
Paper No: GT2020-15928
https://doi.org/10.1115/GT2020-15928
View Paper titled, Machine Learning for the Development of Data Driven Turbulence Closures in Coolant Systems
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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

A Design for Fouling Oriented Optimization of an HPT Nozzle

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R. Friso, N. Casari, A. Suman, M. Pinelli, Francesco Montomoli
Proc. ASME. GT2019, Volume 2D: Turbomachinery, V02DT47A014, June 17–21, 2019
Publisher: American Society of Mechanical Engineers
Paper No: GT2019-91627
https://doi.org/10.1115/GT2019-91627
View Paper titled, A Design for Fouling Oriented Optimization of an HPT Nozzle
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Topics: Design, Nozzles, Optimization, Blades, Shapes, Airfoils, Artificial neural networks, Engines, Erosion, Fuels
Proceedings Papers

Transient Loading on Turbomachinery Packages due to Pressure Waves Caused by Accidental Deflagration Events

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Hannes L. Gauch, Valentina Bisio, Stefano Rossin, Francesco Montomoli, Vito L. Tagarielli
Proc. ASME. GT2019, Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy, V009T27A021, June 17–21, 2019
Publisher: American Society of Mechanical Engineers
Paper No: GT2019-90942
https://doi.org/10.1115/GT2019-90942
View Paper titled, Transient Loading on Turbomachinery Packages due to Pressure Waves Caused by Accidental Deflagration Events
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Topics: Explosions, Pressure, Transients (Dynamics), Turbomachinery, Waves, Shock waves, Design, Industrial design, Modeling, Stress
Proceedings Papers

On the Effect of Inflow Disturbances on the Flow Past a Linear LPT Vane Using Spectral/hp Element Methods

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Andrea Cassinelli, Hui Xu, Francesco Montomoli, Paolo Adami, Raul Vazquez Diaz, Spencer J. Sherwin
Proc. ASME. GT2019, Volume 2C: Turbomachinery, V02CT41A032, June 17–21, 2019
Publisher: American Society of Mechanical Engineers
Paper No: GT2019-91622
https://doi.org/10.1115/GT2019-91622
View Paper titled, On the Effect of Inflow Disturbances on the Flow Past a Linear LPT Vane Using Spectral/hp Element Methods
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Topics: Flow (Dynamics), Inflow, Cascades (Fluid dynamics), Blades, Computer software, Momentum, Turbulence, Boundary layers, Computational fluid dynamics, Design
Proceedings Papers

A Non-Dimensional Approach for Generalizing the Particle Impact Behavior of Gas Turbine Fouling

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Alessio Suman, Nicola Casari, Elettra Fabbri, Michele Pinelli, Luca di Mare, Francesco Montomoli
Proc. ASME. GT2019, Volume 2D: Turbomachinery, V02DT47A016, June 17–21, 2019
Publisher: American Society of Mechanical Engineers
Paper No: GT2019-91733
https://doi.org/10.1115/GT2019-91733
View Paper titled, A Non-Dimensional Approach for Generalizing the Particle Impact Behavior of Gas Turbine Fouling
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Topics: Gas turbines, Particle collisions, Particulate matter, Adhesion, Blades, Dimensional analysis, Erosion, Glues, Inertia (Mechanics), Temperature
Journal Articles

Gas Turbine Fouling Tests: Review, Critical Analysis, and Particle Impact Behavior Map

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Alessio Suman, Nicola Casari, Elettra Fabbri, Michele Pinelli, Luca di Mare, Francesco Montomoli
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. March 2019, 141(3): 032601.
Paper No: GTP-18-1525
https://doi.org/10.1115/1.4041282
Published Online: November 29, 2018
View Articletitled, Gas Turbine Fouling Tests: Review, Critical Analysis, and Particle Impact Behavior Map
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Topics: Particulate matter, Temperature, Viscosity