Project description
A new look at mid-level turbulence
Turbulence has been extensively studied for over a century, leading to models that help in designing systems involving high-speed fluid flows. However, only the extreme ends of turbulence (highly strained, rapidly evolving flows, and lowly strained, slowly evolving flows) are well understood. The intermediate-strain regime, crucial for engineering and environmental applications, remains a mystery. With this in mind, the ERC-funded ONSET project aims to develop and validate a theory for this intermediate regime, based on the concept of ‘rapid self-similarity’. By leveraging new dissipation laws, machine learning and advanced fluid mechanics methods, ONSET seeks to enhance wind energy harvesting and improve unmanned aerial vehicles’ flight efficiency, with significant implications for both engineering and environmental science.
Objective
A century of exhaustive turbulence research has allowed the development of a wide range of turbulence closure models, analytical parametrisations and scaling laws, which enter virtually all design and modelling protocols that involve high Reynolds number flows. Closer inspection, however, reveals that only two extreme and polar-opposite turbulence regimes have been well-understood and modelled. When turbulence is highly-strained and evolves rapidly, or when it is lowly-strained and evolves slowly. The in-between regime of intermediate strain, perhaps the most relevant for engineering and environmental applications, remains obscure.
This proposal is about developing and validating a theory for the intermediate-strain turbulence regime, based on the conjecture that it is governed by a universal flow-behaviour, termed rapid self-similarity, which combines elements from both the high- and low-strain regimes. The proposed investigation is based on three developments. First, the accumulation of evidence in the literature that intermediate-strain turbulence dynamics may accept an analytical description known as the new dissipation law. Second, the development of machine learning techniques which allow the extraction of physical insights directly from data. Third, the attainment of mature experimental and numerical simulation methods in fluid mechanics, capable of resolving the spatio-temporal properties of turbulent flows.
The impact of ONSET is potentially very high, as it will improve the understanding and modelling of a wide range of applications of engineering and environmental science connected to intermediate-strain turbulence. ONSET will demonstrate that by focusing on two example applications: improvement of wind energy harvesting via enhanced wind farm flow modelling, and increase of Unmanned Aerial Vehicle flight efficiency and duration, by making use of UAV group aerodynamics.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencesphysical sciencesclassical mechanicsfluid mechanicsfluid dynamics
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
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Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
SW7 2AZ LONDON
United Kingdom