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Developing and Exploiting Intelligent Approaches for Turbulent Drag Reduction

Developing and Exploiting Intelligent Approaches for Turbulent Drag Reduction
开发和利用减少湍流阻力的智能方法
批准号:
2281188
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2019
资助国家:
英国
项目状态:
未结题
起止时间:
2019 至 --

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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 inhibited the realisation of any functional and economical fluid-flow control strategies which can reduce turbulent skin-friction drag forces of industrial air flows of interest with net-energy savings.This research project aims to develop, implement and exploit machine intelligence paradigms to enable novel approaches to tame wall-turbulence with net-energy savings. This new form of intelligent fluid-flow control will be used to develop next-generation control strategies that can rapidly and autonomously optimise an aerodynamic surface with minimal power input. These newly developed machine intelligence paradigms will be used to reduce turbulent skin-friction drag forces in a series of advanced wind tunnel experiments at Newcastle University. Detailed single-point velocity measurements will be acquired using hot-wire anemometry, whilst simultaneously measuring instantaneous and global skin-friction drag forces downstream of control with flush-mounted hot-film probes and a skin-friction drag balance, respectively. In a separate set of experiments, complementary planar velocity measurements will be acquired using particle image velocimetry to capture the developing turbulence flow structures downstream of control.
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