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

Project description

A novel framework supports the design of tomorrow’s greener multistage turbomachinery

Turbomachinery is equipment that transfers energy through the expansion or compression of a continuously moving fluid via rotating blades, and it includes turbines and compressors. Multistage turbomachinery harnesses several cycles of such expansion or compression in a series to achieve a very high pressure difference from inlet to outlet. With the support of the Marie Skłodowska-Curie Actions programme, the MENTOR project is developing a deep neural network framework that will model uncertainty effects on multistage turbomachinery performance in the advanced phases of multistage turbomachinery design. Optimised turbines and compressors with greater efficiencies will support Europe’s carbon-neutrality and sustainability goals.

Fields of science

• natural sciences › mathematics › applied mathematics › mathematical physics
• natural sciences › mathematics › applied mathematics › dynamical systems
• natural sciences › computer and information sciences › artificial intelligence › machine learning › deep learning
• natural sciences › computer and information sciences › artificial intelligence › computational intelligence

Programme(s)

• H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme
• H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility

Topic(s)

• MSCA-IF-2020 - Individual Fellowships

Call for proposal

H2020-MSCA-IF-2020

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Funding Scheme

MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)

Coordinator

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE

Net EU contribution

€ 212 933,76

Address

South Kensington Campus Exhibition Road
SW7 2AZ London
United Kingdom

Region

London › Inner London — West › Westminster

Activity type

Higher or Secondary Education Establishments

Links

Other funding

€ 0,00