[EnAble]: Developing and Exploiting Intelligent Approaches for Turbulent Drag Reduction

当空气流过商用飞机或高速列车时，在靠近车辆表面的地方会产生一层薄薄的湍流。这个所谓的壁湍流区域产生称为表面摩擦阻力的阻力，该阻力占飞行器能量消耗的一半以上。抑制该区域的湍流可降低表面摩擦阻力，从而降低车辆的能耗，从而减少运输排放，通过改善空气质量实现经济节约和更广泛的健康和环境效益。将这一点放在背景下，一架远程商用飞机所经历的湍流表面摩擦阻力仅减少3%，每年每架飞机将节省120万英镑的喷气燃料，并防止每年释放3，000吨二氧化碳。目前，全球约有23，600架飞机在服役。主动壁湍流控制被认为是一项关键的上游技术，目前处于非常低的技术准备水平，有可能带来车辆性能的阶跃变化。然而，尽管有这种重要性和超过50年的研究，壁湍流的复杂性已经阻止了任何功能和经济的流体流动控制策略的实现，这些策略可以减少感兴趣的工业气流的湍流表面摩擦阻力。EnAble项目旨在开发，实施和利用机器智能范例，以实现壁湍流控制的新方法。这种新形式的智能流体流量控制将用于开发实用的壁湍流控制策略，这些策略可以快速自主地优化气动表面，同时以最小的功率输入适应流速的变化。这种新的能力将开辟机会，发现新的方法来驯服壁湍流，并利用最新的减阻机制，以产生显着水平的湍流表面摩擦阻力减少。

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.