H2020Индивидуална стипендия2022–2024

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

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2022-07-01 → 2024-06-30
Финансиране от ЕС: 212 934 €
Участници: 1
Схема: MSCA-IF

Линиите свързват координатора с партньорите.

Накратко на български

Индустриалните турбини се анализират чрез невронни мрежи, за да се предвиди как неочаквани промени в реалните условия влияят на работата им. Това помага за повишаване на ефективността на енергийните машини и намаляване на вредните емисии в Европа.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

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

Reduction of greenhouse gas emissions depends on not only discovering new energy resource but also enhancing the performance of existing machines. Turbomachinery is the key part at the heart of the current energy conversion device and it produces over 75% of European electricity today. However, a serious problem also occurs that deterministic turbomachinery designs always bear the risk of generating obvious performance degradation in real-world conditions. A major reason behind it is that, in the real configuration, the uncertainties of turbomachinery become a critical point. However, the conventional methods become ineffective in handling the high dimensionality (HD) problems, since the number of sample data needed raises exponentially with the increasing number of input variables, i.e., the curse of dimensionality. This project aims to develop an affordable Multi-fidelity dEep neural Network uncerTainty quantificatiOn and Robust optimization (MENTOR) framework for handling the industrial bottle-neck HD uncertainty problems. The methodologies developed in MENTOR project are quite meaningful for achieving the goal of Europe’s “Green Deal”, eventually contributing to building a clean and efficient energy landscape in the EU.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

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.""

Оригинален текст от CORDIS (на английски).

Участници

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE · LondonКоординаторОбединеното кралство