[EnAble]: Developing and Exploiting Intelligent Approaches for Turbulent Drag Reduction
[EnAble]: Developing and Exploiting Intelligent Approaches for Turbulent Drag Reduction
批准号:
EP/T020946/1
负责人:
Richard Whalley
金额:
$79.9万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2021
资助国家:
英国
项目状态:
未结题
起止时间:
2021 至 --
中文摘要
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英文摘要
Whenever air flows over a commercial aircraft or a high-speed train, a thin layer of turbulence is generated close to the surface of the vehicle. This region of so-called wall-turbulence generates a resistive force known as skin-friction drag which is responsible for more than half of the vehicle's energy consumption. Taming the turbulence in this region reduces the skin-friction drag force, which in turn reduces the vehicle's energy consumption and thereby reduces transport emissions, leading to economic savings and wider health and environmental benefits through improved air quality. To place this into context, just a 3% reduction in the turbulent skin-friction drag force experienced by a single long-range commercial aircraft would save £1.2M in jet fuel per aircraft per year and prevent the annual release of 3,000 tonnes of carbon dioxide. There are currently around 23,600 aircraft in active service around the world. Active wall-turbulence control is seen as a key upstream technology currently at very low technology readiness level that has the potential to deliver a step change in vehicle performance. Yet despite this significance and well over 50 years of research, the complexity of wall-turbulence has prevented the realisation of any functional and economical fluid-flow control strategies which can reduce the turbulent skin-friction drag forces of industrial air flows of interest. The EnAble project aims to develop, implement and exploit machine intelligence paradigms to enable a new approach to wall-turbulence control. This new form of intelligent fluid-flow control will be used to develop practical wall-turbulence control strategies that can rapidly and autonomously optimise the aerodynamic surface with minimal power input whilst being adaptive to changes in flow speed. This new capability will open up the opportunity to discover new ways to tame wall-turbulence and exploit the latest drag reduction mechanisms to generate significant levels of turbulent skin-friction drag reduction.
期刊论文(8)
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DOI:
--
发表时间:
2022
期刊:
影响因子:
--
作者:
[Diessner, M.]
通讯作者:
Diessner, M.
DOI:
10.1007/s10494-023-00408-3
发表时间:
2023-03
期刊:
Flow, Turbulence and Combustion
影响因子:
--
作者:
[Joseph O’Connor;Mike Diessner;Kevin Wilson;R. Whalley;A. Wynn;S. Laizet]
通讯作者:
Joseph O’Connor;Mike Diessner;Kevin Wilson;R. Whalley;A. Wynn;S. Laizet
Investigating Bayesian optimization for expensive-to-evaluate black box functions: Application in fluid dynamics
研究评估昂贵的黑盒函数的贝叶斯优化:在流体动力学中的应用
DOI:
10.3389/fams.2022.1076296
发表时间:
2022
期刊:
Frontiers in Applied Mathematics and Statistics
影响因子:
1.4
作者:
[Diessner M]
通讯作者:
Diessner M
A Bayesian optimisation framework for drag reduction and net energy saving in a turbulent boundary layer using wall-normal blowing.
使用壁法向吹气在湍流边界层中实现减阻和净节能的贝叶斯优化框架。
DOI:
--
发表时间:
2022
期刊:
影响因子:
--
作者:
[O'connor, J.]
通讯作者:
O'connor, J.
Flow physics of a turbulent boundary layer actuated via wall-normal blowing in different configurations
通过不同配置的壁法向吹动驱动的湍流边界层的流动物理
DOI:
--
发表时间:
2022
期刊:
影响因子:
--
作者:
[O'connor, J.]
通讯作者:
O'connor, J.
共 7 条
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