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

在不利条件下保持先进驾驶员辅助系统传感器可操作性的空气动力学方法

• 批准号: 2331444
• 金额: --
• 依托单位: Loughborough University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2331444
• 关键词: Aerodynamic; Methods; Maintaining; Advanced; Driver

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.

表面污染或污染的影响一直是汽车行业长期存在的问题，研究可以追溯到20世纪60年代。到目前为止，污染物积聚的影响主要体现在化妆品上。然而，随着自动驾驶和其他先进驾驶辅助系统（ADAS）的出现，表面污染可能会产生更严重的影响。ADAS依靠传感器来监测其运行的外部环境，而表面污染风险会阻碍这种能力，限制其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，实验研究和使用高保真度（拉格朗日粒子跟踪的分离涡流模拟）数值方法的计算模拟之间可以找到非常好的一致性。这些研究主要集中在SUV型几何形状后部的污染。该项目将考虑在车辆周围的ADAS传感器位置上喷洒和悬挂。这将包括考虑污染，高湍流，尾迹的其他车辆。该项目的第一个目标将是使用实验和计算研究来找到在基线配置的传感器条件下可以预期的污染水平。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。这项工作的主体将是研究使用空气动力学控制方法来减少道路喷雾对ADAS系统的影响。数据分析方法，如适当的正交分解（POD）技术，将被用于识别造成污染的主要流结构，并强调减少颗粒进入车辆尾迹的机制。这将分为两类；第一项研究将研究减少车轮进入车辆尾流的喷雾量的方法，第二项研究将考虑主动装置，如空气屏障，它可以产生局部高能量空气流，以帮助保护传感器免受污染物的影响。方法将在模拟中发展，然后在实验中测试。除了考虑减少污染方面的有效性外，还将确定能源使用方面的“成本”或等同的车辆阻力增加。