A Novel and Affordable Multi-Fidelity Deep Neural Network Uncertainty Quantification/Robust Optimization Design Framework for Industrial Turbomachinery

Project Information

MENTOR

Grant agreement ID: 101029472

DOI

10.3030/101029472

Project terminated on 30 April 2024

EC signature date 12 April 2021

Start date 1 July 2022

End date 30 June 2024

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost

€ 212 933,76

EU contribution

€ 212 933,76

Coordinated by

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE
United Kingdom

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Publications

Peer reviewed articles (4)

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

Author(s): Zhihui Li, Francesco Montomoli, Nicola Casari, Michele Pinelli
Published in: Journal of Turbomachinery, Issue Nov 2023, 145(11): 111009 (9 pages), 2023, ISSN 0889-504X
Publisher: ASME
DOI: 10.1115/1.4063391

Aleatory uncertainty quantification based on multi-fidelity deep neural networks

Author(s): Zhihui Li, Francesco Montomoli
Published in: Reliability Engineering & System Safety, Issue Volume 245, May 2024, 109975, 2024, ISSN 0951-8320
Publisher: Elsevier BV
DOI: 10.1016/j.ress.2024.109975

Investigation of Compressor Cascade Flow Using Physics-Informed Neural Networks with Adaptive Learning Strategy

Author(s): Zhihui Li, Francesco Montomoli and Sanjiv Sharma
Published in: AIAA Journal, 2024, ISSN 1533-385X
Publisher: American Institute of Aeronautics and Astronautics
DOI: 10.2514/1.j063562

Numerical Simulations and Design Optimization of Compressor Cascade Flow Using One Equation and Wray-Agarwal Turbulence Model

Author(s): Zhihui Li, Ramesh K. Agarwal
Published in: International Journal of Computational Fluid Dynamics, Issue 36.8 (2022): 705-718, 2022, ISSN 1061-8562
Publisher: Taylor & Francis
DOI: 10.1080/10618562.2023.2187050

Conference proceedings (1)

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

Author(s): Zhihui Li, Francesco Montomoli, Nicola Casari, Michele Pinelli
Published in: ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition, Issue GT2023-101698, V13DT34A007, 2023, ISBN 978-0-7918-8711-0
Publisher: The American Society of Mechanical Engineers
DOI: 10.1115/gt2023-101698