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Turbomachinery: Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification
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Proceedings Papers

Multistage Turbomachinery Optimization for High Temperature Heat Pumps With the Reverse Rankine Cycle

Open Access

Robert Schaffrath, Panagiotis Stathopoulos, Andreas Schmitz, Eberhard Nicke
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A023, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-128083
https://doi.org/10.1115/GT2024-128083
View Paper titled, Multistage Turbomachinery Optimization for High Temperature Heat Pumps With the Reverse Rankine Cycle
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Topics: Compressors, Heat pumps, High temperature, Optimization, Rankine cycle, Turbomachinery
Proceedings Papers

Advancing Airfoil Design: A Physics-Inspired Neural Network Model

Available to Purchase

Can Unlusoy, Bill Maier, Khalil Al Handawi, Tittu Varghese Mathew, Ravichandra Srinivasan, Mathieu Salz, Michael Kokkolaras
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A010, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-122682
https://doi.org/10.1115/GT2024-122682
View Paper titled, Advancing Airfoil Design: A Physics-Inspired Neural Network Model
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Topics: Airfoils, Artificial neural networks, Design, Neural network models, Physics
Proceedings Papers

Twofold Adaptive Design Space Reduction for Constrained Bayesian Optimization of Transonic Compressor Blades

Open Access

Lisa Pretsch, Ilya Arsenyev, Elena Raponi, Fabian Duddeck
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A005, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-121848
https://doi.org/10.1115/GT2024-121848
View Paper titled, Twofold Adaptive Design Space Reduction for Constrained Bayesian Optimization of Transonic Compressor Blades
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Topics: Blades, Compressors, Design, Optimization, Principal component analysis, Shape optimization
Proceedings Papers

Non-Parametric Surrogate Model (NPSM) for High-Dimensional Aerodynamics Optimization

Available to Purchase

Jiajun Cao, Liping Xu
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A020, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-126830
https://doi.org/10.1115/GT2024-126830
View Paper titled, Non-Parametric Surrogate Model (NPSM) for High-Dimensional Aerodynamics Optimization
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Topics: Aerodynamics, Optimization, Convolutional neural networks, Flow (Dynamics), Graph neural networks, Blades, Design, Errors, Feedback, Geometry
Proceedings Papers

Data-Driven Aerodynamic Optimization of a 15-Stage Compressor With Tandem Outlet Guide Vanes

Available to Purchase

Yijie Liu, Jiang Chen, Yi Liu, Hang Xiang
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A011, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-122715
https://doi.org/10.1115/GT2024-122715
View Paper titled, Data-Driven Aerodynamic Optimization of a 15-Stage Compressor With Tandem Outlet Guide Vanes
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Topics: Compressors, Gas turbines, Guide vanes, Optimization, Parametrization
Proceedings Papers

Robust Optimization of a Compressor Blade Through Combination of an Adjoint-Based Multistart Approach and Global Gaussian Process Regression

Available to Purchase

Aryan Karimian, Robin Schmidt, Christian Janke
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A002, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-121351
https://doi.org/10.1115/GT2024-121351
View Paper titled, Robust Optimization of a Compressor Blade Through Combination of an Adjoint-Based Multistart Approach and Global Gaussian Process Regression
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Topics: Blades, Compressors, Computational fluid dynamics, Design, Optimization, Rotors, Uncertainty
Proceedings Papers

Multidisciplinary Design Optimization With Multiple Degrees of Freedom for an Axial Compressor Based on Data-Driven

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Chuwei Luo, Jiang Chen, Yi Liu, Hang Xiang
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A031, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-129265
https://doi.org/10.1115/GT2024-129265
View Paper titled, Multidisciplinary Design Optimization With Multiple Degrees of Freedom for an Axial Compressor Based on Data-Driven
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Topics: Compressors, Deformation, Degrees of freedom, Design, Evolutionary algorithms, Optimization
Proceedings Papers

Centrifugal Compressor Performance and Flow Path Generation With Artificial Intelligence

Available to Purchase

Valentyn Barannik, Maksym Burlaka, Bohdan Lysianskyi, Leonid Moroz
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A030, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-129190
https://doi.org/10.1115/GT2024-129190
View Paper titled, Centrifugal Compressor Performance and Flow Path Generation With Artificial Intelligence
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Topics: Artificial intelligence, Artificial neural networks, Compressors, Flow (Dynamics)
Proceedings Papers

Design of a Relational Database for Turbine Center Frames With Application for Geometry Optimization

Available to Purchase

Marian Staggl, Wolfgang Sanz, Thomas Sterner
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A019, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-126557
https://doi.org/10.1115/GT2024-126557
View Paper titled, Design of a Relational Database for Turbine Center Frames With Application for Geometry Optimization
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Topics: Artificial intelligence, Databases, Design, Geometry, Optimization, Relational databases, Turbines
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
Proceedings Papers

Impact of Manufacturing Variability From Different Compressor Stages on Aerodynamic Uncertainty: An Interchangeability Study

Available to Purchase

Mingzhi Li, Xianjun Yu, Dejun Meng, Guangfeng An, Baojie Liu
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A024, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-128577
https://doi.org/10.1115/GT2024-128577
View Paper titled, Impact of Manufacturing Variability From Different Compressor Stages on Aerodynamic Uncertainty: An Interchangeability Study
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Topics: Compressors, Design, Manufacturing, Uncertainty
Proceedings Papers

Feasibility Study of Decentralized Lubrication System Design Through Branch and Bound and Genetic Algorithm for Turbomachinery Trains

Available to Purchase

Yuzheng Chen, Bill Maier, Andy Buckenberger, Yaoyao Fiona Zhao
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A013, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-123728
https://doi.org/10.1115/GT2024-123728
View Paper titled, Feasibility Study of Decentralized Lubrication System Design Through Branch and Bound and Genetic Algorithm for Turbomachinery Trains
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Topics: Design, Genetic algorithms, Industrial lubrication systems, Optimization, Trains, Turbomachinery
Proceedings Papers

Probabilistic Analysis of Leading Edge Erosion Behavior of a High-Pressure Compressor in a Turbofan Engine

Available to Purchase

Andreas Türke, Lukas Schlüter, Matthias Voigt, Robin Schmidt, Andreas Vogel, Ronald Mailach
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A007, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-122239
https://doi.org/10.1115/GT2024-122239
View Paper titled, Probabilistic Analysis of Leading Edge Erosion Behavior of a High-Pressure Compressor in a Turbofan Engine
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Topics: Compressors, Engines, Erosion, High pressure (Physics), Turbofans, Blades, Geometry, Particulate matter, Dust, Wear
Proceedings Papers

A Real Time AI-Based Strategy for the Design of a Low-Pressure Turbine Profile

Available to Purchase

Juri Bellucci, Angelo Alberto Granata, Mattia Silei, Matteo Giovannini, Ennio Spano, Andrea Notaristefano, Davide Lengani
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A026, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-128809
https://doi.org/10.1115/GT2024-128809
View Paper titled, A Real Time AI-Based Strategy for the Design of a Low-Pressure Turbine Profile
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Topics: Design, Pressure, Turbines
Proceedings Papers

Digital Value Tool for Gas Turbine Digitized Cross Section

Available to Purchase

Marigrazia Moscardini, Valentina Filice, Gianni Cini, Libero Tapinassi, Matteo Berti, Riccardo Brogelli, Paolo Del Turco, Giacomo Ragni
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A014, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-124314
https://doi.org/10.1115/GT2024-124314
View Paper titled, Digital Value Tool for Gas Turbine Digitized Cross Section
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Topics: Gas turbines
Proceedings Papers

Multi-Fidelity Aeromechanical Design Framework for High Flow Speed Multistage Axial Compressors

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Rémy Nigro, Lieven Baert, Florence Nyssen, Jean de Cazenove, Joachim Dominique, Ingrid Lepot, Monica Veglio, Rémy Princivalle
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A008, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-122244
https://doi.org/10.1115/GT2024-122244
View Paper titled, Multi-Fidelity Aeromechanical Design Framework for High Flow Speed Multistage Axial Compressors
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Topics: Compressors, Design, Flow (Dynamics), Optimization, Architecture, Aviation, Carbon, Cycles, Dynamics (Mechanics), Emissions
Proceedings Papers

Generative Model Based Parameterization for More Efficient Aerodynamic Optimization of Non-Axisymmetric Endwall

Available to Purchase

Cunxi Li, Liming Song, Zhendong Guo, Zhao Yang, Jun Li, Zhenping Feng
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A025, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-128792
https://doi.org/10.1115/GT2024-128792
View Paper titled, Generative Model Based Parameterization for More Efficient Aerodynamic Optimization of Non-Axisymmetric Endwall
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Topics: Optimization, Parametrization
Proceedings Papers

Uncertainty Quantification Analysis on the Cooling Flow Rate of the Cooled Turbine in a Novel H2/O2 Cycle

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Bangyan Ma, Lei Shi, Xiaocheng Zhu, Zhaohui Du
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A015, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-124431
https://doi.org/10.1115/GT2024-124431
View Paper titled, Uncertainty Quantification Analysis on the Cooling Flow Rate of the Cooled Turbine in a Novel H2/O2 Cycle
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Topics: Coolants, Cooling, Cycles, Flow (Dynamics), Hydrogen fuels, Turbines, Uncertainty quantification
Proceedings Papers

Data-Driven Inverse Design Method for Turbomachinery

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Kwok Kai So, Luis Salamanca, Firat Ozdemir, Fernando Perez-Cruz
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A004, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-121752
https://doi.org/10.1115/GT2024-121752
View Paper titled, Data-Driven Inverse Design Method for Turbomachinery
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Topics: Design methodology, Turbomachinery, Design, Artificial neural networks, Optimization, Physics, Thrust bearings, Turbines
Proceedings Papers

Application of Deep Learning for Fan Rotor Blade Performance Prediction in Turbomachinery

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Jean Fesquet, Michael Bauerheim, Ludovic Rojda, Yannick Bousquet, Nicolas Binder
Proc. ASME. GT2024, Volume 12D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V12DT34A021, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-127348
https://doi.org/10.1115/GT2024-127348
View Paper titled, Application of Deep Learning for Fan Rotor Blade Performance Prediction in Turbomachinery
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Topics: Blades, Deep learning, Rotors, Turbomachinery, Artificial neural networks, Geometry, Optimization, Boundary layers, Computation, Deep neural networks