H2020Индивидуална стипендия2021–2023

ConvExt · EXTreme events in CONVection: advanced measurements and data-driven prediction

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

Период: 2021-06-01 → 2023-05-31
Финансиране от ЕС: 162 806 €
Участници: 1
Схема: MSCA-IF

Линиите свързват координатора с партньорите.

Накратко на български

Силните ветрови пориви се анализират чрез експерименти с турбулентни течности и методи за машинно обучение. Това помага за по-доброто разбиране на резките промени в производството на електроенергия от вятърните турбини.

Този кратък обзор е генериран от изкуствен интелект

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

Резултати накратко

EXTreme events in CONVection: advanced measurements and data-driven prediction

Due to the need of an environmentally sustainable energy production, the generation of electricity from wind power is expanding worldwide, with Europe being the second for installed capacity after Asia. The optimal placement of wind turbines requires the knowledge of wind conditions at any place of a terrain down to millimeter scales. Large wind fluctuations at small scales, also called wind gusts are extreme events responsible for the phenomenon of intermittency in atmospheric turbulence. Intermittency is not considered by current flow models of atmospheric turbulence, even if it may have important consequences for wind turbines operating conditions. For example, it was observed that the electrical power fed into the grid by a wind farm may change by 50% within two minutes because of wind gusts. This project aims at understanding and predicting extreme events of energy dissipation from experiments in Rayleigh-Bénard convection (RBC), a turbulent fluid layer that is uniformly heated from below and cooled from above. RBC is considered as a paradigm for atmospheric turbulence. Measurements of extreme events of energy dissipation are performed using Particle Image Velocimetry (PIV). Machine Learning (ML) methods are an efficient and unbiased way to process a large amount of data effectively. ML has been rarely applied to the detection of extreme events in fluid turbulence. In this project, by using Recurrent Neural Networks (a particular king of ML method), a large amount of data from experiments and simulations is processed to construct a model. The predicted extreme events are large wind fluctuations at small scales, wind gusts, which may have important consequences for wind turbines operating conditions. In summary, this project aims at understanding and predicting extreme events of energy dissipation in atmospheric turbulence, by: (1) advanced measurements of extreme events in a simplified model experiment, (2) the comparison with numerical simulations, (3) the development of ML data-driven models from experimental data.

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

Цел на проекта

Wind storms, hurricanes, and heat waves, are atmospheric extreme events with a huge societal impact and significant economic costs. Thus, their correct identification is important, e.g. for off-shore wind power generation. This project is a fundamental study on hydrodynamics turbulence, whose results will provide a methodological basis for innovation in wind energy technology. Extreme atmospheric convection events are characterized by large local amplitudes of the rate at which turbulent kinetic energy is dissipated, a central quantity that cannot be predicted from the highly nonlinear mathematical equations of fluid motion. This project aims at understanding the formation and predicting such extreme events of energy dissipation in Rayleigh-Bénard convection (RBC), a paradigm for atmospheric motion. Advanced high resolution measurements of the small-scale velocity field and its gradients will therefore be performed in a pressurized convection chamber at TU Ilmenau which allows to downscale turbulence and to use Particle Image Velocimetry for flows at Rayleigh numbers up to a million or higher. By combination of measured kinetic energy dissipation rate in the bulk and wall shear stresses in the boundary layer, we will identify the advection patterns that generate the extreme dissipation events. The present experimental analysis will be complemented by existing training data records of high-resolution direct numerical simulations of the same flows. They serve to develop data-driven methods and algorithms, such as recurrent neural networks, to predict such extreme events in experimental analyses. The goal of this project is to advance our understanding of the dynamic evolution of such extreme events in a RBC flow and to develop reliable tools to predict the events. This research objective will be reached in a multidisciplinary way by a combination of high resolution optical flow measurements with the data-driven modeling and data analytics by machine learning.

Оригинален текст от CORDIS (на английски).

Участници

TECHNISCHE UNIVERSITAET ILMENAU · IlmenauКоординаторГермания