Developing and Exploiting Intelligent Approaches for Turbulent Drag Reduction

每当空气在商用飞机或高速列车上方流动时，在靠近车辆表面的地方都会产生一层薄薄的湍流。这一所谓的壁面湍流区域会产生一种被称为表面摩擦阻力的阻力力，该阻力占车辆能源消耗的一半以上。驯服该地区的湍流可以减少表面摩擦阻力，进而减少车辆的能源消耗，从而减少交通排放，从而通过改善空气质量来节省经济成本和更广泛的健康和环境效益。考虑到这一点，仅将一架远程商用飞机经历的湍流表面摩擦阻力降低3%，每年每架飞机就可以节省120万GB的喷气燃料，并防止每年排放3,000吨二氧化碳。目前，世界各地约有23,600架现役飞机。主动壁面湍流控制被视为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段性变化。然而，尽管有如此重要的意义，而且经过50多年的研究，壁面湍流的复杂性已经阻碍了任何实用和经济的流体流动控制策略的实现，这些策略可以通过净节能来减少感兴趣的工业气流的湍流摩阻阻力。本研究项目旨在开发、实施和开发机器智能范例，以实现通过净节能来驯服壁面湍流的新方法。这种新形式的智能流体流量控制将被用于开发下一代控制策略，该策略可以快速、自主地以最小的功率输入优化空气动力学表面。这些新开发的机器智能范例将在纽卡斯尔大学的一系列高级风洞实验中用于减少湍流表面摩擦阻力。详细的单点速度测量将使用热线风速仪获得，同时分别使用平齐安装的热膜探头和表面摩擦阻力天平测量控制下游的瞬时和全局表面摩擦阻力。在另一组单独的实验中，将使用粒子图像测速技术获取互补的平面速度测量，以捕捉控制装置下游不断发展的湍流结构。

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.