Aerodynamic Methods of Maintaining Advanced Driver-Assistance System Sensor Operability in Adverse Conditions

表面污染或污垢的影响在汽车行业一直是一个长期存在的问题，研究可以追溯到20世纪60年代。到目前为止，污染物积累的影响在很大程度上是表面上的。然而，随着自动驾驶和其他先进的驾驶员辅助系统(ADAS)的出现，表面污染可能会产生更严重的影响。ADAS依赖传感器来监控其运行的外部环境，表面污染可能会阻碍这一能力，限制功能并增加道路交通事故的可能性。拉夫堡大学之前的研究表明，使用高保真(利用拉格朗日粒子跟踪的分离涡流模拟)数值方法进行的实验研究和计算模拟之间可以找到非常好的一致性水平。这些研究在很大程度上集中在SUV类型几何形状的后部污染问题上。该项目将考虑在车辆周围的ADAS传感器位置上和在其前面进行喷雾。这将包括考虑受污染的、高度湍流的、其他车辆的尾迹。该项目的第一个目标将是使用实验和计算研究，以找出基线配置的传感器条件下可预期的污染水平。利用这些数据，目的是量化污染物积聚的影响，并确定可能的缓解空气动力学设计。工作的主要内容将是调查空气动力学控制方法的使用，以减少道路喷雾对ADAS系统的影响。数据分析方法，如适当的正交分解(POD)技术，将被用来识别造成污染的主要流动结构，并强调减少颗粒物夹带到车辆尾流中的机理。这些将分为两类；第一类是研究减少车轮带入车辆尾流的喷雾量的方法；第二类将考虑产生局部高能气流的主动装置，如空气屏障，以帮助保护传感器免受撞击污染物的影响。方法将在模拟中开发，然后在实验中进行测试。除了考虑减少污染的有效性外，还将确定能源使用或相当于车辆阻力增加的“成本”。

The impacts of surface contamination, or soiling, have been a long-standing issue in the automotive industry, with research going back as far as the 1960s. Until now, the impact of contaminant build-up has been largely cosmetic. However, with the advent of autonomy and other advanced driver-assistance systems (ADAS), surface contamination may have more serious repercussions. ADAS rely on sensors to monitor the external environment in which they operate and surface contamination risks obstructing this ability, limiting functionality and increasing the likelihood of road traffic accidents.Previous studies at Loughborough University have shown that very good levels of agreement can be found between experimental studies and computational simulations using a high-fidelity (Detached Eddy Simulation with Lagrangian particle tracking) numerical method. These studies have largely concentrated on the soiling of the rear of SUV type geometries. This project will consider the spray on, and suspended in front of, ADAS sensor locations around the vehicle. This will include consideration of the contaminated, high turbulent, wake of other vehicles. The first aim of the project will be to use experimental and computational studies to find the level of contamination that can be expected at sensor conditions for a baseline configuration. Using this data, the intent is to quantify the effects of contaminant build-up and identify possible mitigatory aerodynamic designs.The main body of the work will be to investigate the use of aerodynamic control methods to reduce the impact of road spray on ADAS systems. Data analysis methods such as proper orthogonal decomposition (POD) techniques will be used to identify the main flow structures responsible for contamination and highlight mechanisms to reduce particle entrainment into vehicle wakes.These will fall into two categories; the first will be to investigate methods of reducing the amount of spray that is entrained into the vehicle wake from the wheels, the second will consider active devices, such as air barriers, that generate streams of local, high-energy air, to help shield sensors from impinging contaminants. Methods will be developed in simulation before tested in experiment. As well as considering the effectiveness in terms of contamination reduction, the 'cost' in terms of energy use or equivalently vehicle drag increase will be determined.