Description du projet
Un nouveau cadre de conception pour les turbomachines multi-étages plus écologiques de demain
Les turbomachines, qui comprennent notamment les turbines et les compresseurs, sont des équipements qui transfèrent de l’énergie par détente ou compression d’un fluide en mouvement continu grâce à des pales rotatives. Les turbomachines multi-étages exploitent plusieurs cycles de détente ou de compression en série pour générer une très grande différence de pression entre l’entrée et la sortie. Grâce au soutien du programme Actions Marie Skłodowska-Curie, le projet MENTOR élabore un cadre de réseau neuronal profond qui modélisera les effets de l’incertitude sur les performances des turbomachines multi-étages dans les phases avancées de la conception de ces machines. Les turbines et compresseurs optimisés, qui affichent des rendements plus élevés, contribueront aux objectifs de neutralité carbone et de durabilité de l’Europe.
Objectif
"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."
Champ scientifique
- natural sciencesmathematicsapplied mathematicsmathematical physics
- natural sciencesmathematicsapplied mathematicsdynamical systems
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Mots‑clés
Programme(s)
Régime de financement
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinateur
SW7 2AZ LONDON
Royaume-Uni