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
• 金额: --
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2281188
• 关键词: Developing; Exploiting; Intelligent; Approaches; Turbulent

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.

每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就会产生一层薄薄的湍流。这个所谓的壁面湍流区域产生了一种阻力，称为表面摩擦阻力，这种阻力占车辆能耗的一半以上。控制该地区的湍流可以减少表面摩擦阻力，从而减少车辆的能源消耗，从而减少运输排放，从而通过改善空气质量节省经济并带来更广泛的健康和环境效益。从实际情况来看，一架远程商用飞机所经历的湍流表面摩擦阻力只要减少3%，每架飞机每年就能节省120万英镑的喷气燃料，并防止每年排放3000吨二氧化碳。目前全球约有23,600架飞机在服役。主动壁面湍流控制被视为一项关键的上游技术，目前技术成熟度非常低，但有可能实现车辆性能的阶段性变化。然而，尽管具有这一意义，并且已经进行了50多年的研究，但壁面湍流的复杂性已经抑制了任何功能和经济的流体流动控制策略的实现，这些策略可以减少工业气流的湍流表面摩擦阻力，从而节省净能源。该研究项目旨在开发、实施和利用机器智能范式，以实现驯服壁面湍流并节省净能源的新方法。这种新形式的智能流体流动控制将用于开发下一代控制策略，能够以最小的功率输入快速自主地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先进风洞实验中用于减少湍流皮肤摩擦阻力。详细的单点速度测量将使用热线风速仪获得，同时分别使用嵌入式热膜探头和表面摩擦阻力平衡测量控制下游的瞬时和全局表面摩擦阻力。在一组单独的实验中，将使用粒子图像测速法获得互补的平面速度测量，以捕获控制下游发展中的湍流流动结构。