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.
Objective
"This fellowship aims to train a talented early career researcher and to contribute to the EU scientific excellence by developing an innovative Multi-fidelity dEep neural Network uncerTainty quantificatiOn and Robust optimization design (MENTOR) framework in order to handle the high dimensionality (HD) uncertain problems in the advanced multistage turbomachinery design process. The applicant is a highly dedicated and motivated young researcher and has been stimulated to propose this novel idea. He has been successively honoured with several prestigious awards including the National Scholarship for PhD Candidate, Excellent PhD Graduates of Beijing, Excellent Doctoral Dissertations Award of Beijing and ASME Young Engineer Turbo Expo Travel Award attributed to his excellent research achievement in cost-efficient uncertainty quantification (UQ) studies.
The traditional UQ methods can hardly control the computation cost for predicting the higher-order moments of multistage turbomachinery performance considering HD input uncertainties. The deep learning technology is a promising approximator in predicting the HD function. Integration of multi-fidelity (MF) methodology with deep neural network (DNN) can further combine their complementary merits. Thus, the novel MF-DNN method is proposed here and its effectiveness in handling a 60-dimensional test function has been preliminarily validated in the Incoming Researcher's recent work. Through this research fellowship, an affordable MENTOR framework will be finally established to investigate the multi-source uncertainty effects on multistage turbomachinery performance. This project has been carefully designed to match the applicant's profile with the strength of Imperial's UQ Lab, and thus will facilitate excellent two-way knowledge transfer and training activities. Successful completion of this fellowship will contribute to achieving the goal of the EU's ""Green Deal"" and will benefit the applicant's academic career prospect."
Fields of science
- natural sciencesmathematicsapplied mathematicsmathematical physics
- natural sciencesmathematicsapplied mathematicsdynamical systems
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Keywords
Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
SW7 2AZ LONDON
United Kingdom