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Keyword machine learning
ORIGINAL ARTICLE
Physics guided machine learning modelling of compressor stall flutter
J. Glob. Power Propuls. Soc. 2024;8:295-309
ORIGINAL ARTICLE
High-fidelity CFD-trained machine learning to inform RANS-modelled interfacial turbulence
J. Glob. Power Propuls. Soc. 2023;7:269-281
ORIGINAL ARTICLE
Automated detection of hot-gas path defects by support vector machine based analysis of exhaust density fields
J. Glob. Power Propuls. Soc. 2021;Special Issue: Data-driven modelling and high-fidelity simulations:1-16
ORIGINAL ARTICLE
Towards digital design of gas turbines
Francesco Montomoli, Stelios Antorkas, Marco Pietropaoli, Audrey Gaymann, James Hammond, Yuri Frey Marioni, Niklas Isaksson, Michela Massini, Raul Vazquez-Diaz, Paolo Adami
J. Glob. Power Propuls. Soc. 2021;Special Issue: Data-driven modelling and high-fidelity simulations:1-12
REVIEW ARTICLE
A physics-informed machine learning framework for predictive maintenance applied to turbomachinery assets
Marzia Sepe, Antonino Graziano, Maciej Badora, Alessandro Di Stazio, Luca Bellani, Michele Compare, Enrico Zio
J. Glob. Power Propuls. Soc. 2021;Special Issue: Data-driven modelling and high-fidelity simulations:1-15
ORIGINAL ARTICLE
Editorial: Data-driven modelling and high-fidelity simulations
J. Glob. Power Propuls. Soc. 2021;Special Issue: Data-driven modelling and high-fidelity simulations:1-2
ORIGINAL ARTICLE
Estimating RANS model uncertainty using machine learning
J. Glob. Power Propuls. Soc. 2021;Special Issue: Data-driven modelling and high-fidelity simulations:1-14
ORIGINAL ARTICLE
RANS predictions of trailing-edge slot flows using heat-flux closures developed with CFD-driven machine learning
J. Glob. Power Propuls. Soc. 2021;Special Issue: Data-driven modelling and high-fidelity simulations:1-13
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| eISSN: | 2515-3080 |
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