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Salt Spray Test is being used since 1930’s to accelerate the corrosion testing of materials and to understand the longevity of applied coating. The sample in this kind of test is exposed to a salt mist in a controlled environment and its corrosion resistance is evaluated by measuring the corrosion rate. The Wet-Dry cycle in Salt Spray Test has the ability to simulate the drying and wetting which occurs in real driving scenario, leading to formation of a film of corrosion products which is useful in analyzing the kinetics of electrochemical reaction. Despite the advancement in severity of these tests to understand the atmospheric corrosion phenomena, they still consume time and resources. Secondly, sometimes these kind of tests do not consider into account the effect of Temperature, Humidity and other chemicals in play. Thus, numerical simulation plays a pivotal role in digitalizing the corrosion analysis to a certain extent.

The automotive world is progressing fast towards autonomous vehicles making sensors one of the critical components. There is a requirement for constant exchange of information between the vehicle and its surrounding environment, which is assisted by sensors such as Camera, LiDAR, and RADAR. However, exposure to harsh environmental conditions such as rain, dirt, snow, and bird droppings can hamper the functioning of the sensors and in turn interrupt accurate vehicle maneuvers. Sensor-cleaning mechanisms are required to be tested under various weather conditions and vehicle operating situations. Besides wind tunnel tests, digitalizing this whole process becomes important to take decision on design changes in early vehicle development stage. This work presents a digital methodology to test the LiDAR cleaning system in the advent of mud clearing at different vehicle speeds. The cleaning mechanism consists of a telescopic nozzle placed above the LiDAR translating back and forth.

Sunroof buffeting is examined experimentally and numerically in this paper. Despite the fact that some consider the simulation process for sunroof buffeting to be mature, there remain substantial uncertainties even in recently published methodologies. Capturing the frequencies and especially the sound pressure levels correctly is essential if CFD simulations are intended to be used during early stages of a car development process. Numerous experimental results of sunroof buffeting and the interior low-frequency characteristics of a 2013 Mercedes-Benz S-Class have been used to develop a simplified car model: a full-size S-Class model with slightly simplified geometries in the interior as well as at the exterior. To avoid the effects of numerous different materials in the interior, it is solely made from polyurethane and aluminum and built to maximize its structural rigidity and air-tightness.

The current automotive market is dynamic, leading to complex functionalities being incorporated into the control software of various components like engine, gearbox, battery, E-motor etc. This results in utilization of virtual environments for software testing to reduce the development time. The virtual platforms under the category X-in-the-Loop (XiL) e.g. Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) use simulated models to achieve a desired test goal. These component models must be rigorously validated to ensure the quality of XiL-Testing. Thus, it is essential to define a model release process that maintains model quality irrespective of the modeling approach used and the user. One of the challenges is to choose an appropriate Error Metric (EM) that sets criteria for model release. This paper proposes a combination of Theil’s Inequality Coefficient (TIC) and Unscaled Mean Bounded Relative Absolute Error (UMBRAE) as the EM.

The paper describes a predictive tool for the determination of air and coolant temperatures and heat exchange resulting from the operation of heat exchangers, e.g., radiator or air-conditioner condenser in the underhood of automotive engines. The paper describes a detailed computational model where both the fluid streams are numerically solved and the phase change of the refrigerant is taken into account in a condenser simulation. An actual underhood simulation with interactions with a radiator is presented. A numerical simulation for a condenser is also presented. Reasonable agreement is shown with the test data.

Computational models directly derived from data gained increased interest in recent years. Data-driven approaches have brought breakthroughs in different research areas such as image-, video- and audio-processing. Often denoted as Machine Learning (ML), today these approaches are not widely applied in the field of vehicle Noise, Vibration and Harshness (NVH). Works combining ML and NVH mainly discuss the topic with respect to psychoacoustics, traffic noise, structural health monitoring and as improvement to existing numerical simulation methods. Vehicle interior noise is a major quality criterion for today’s automotive customers. To estimate noise levels early in the development process, deterministic system descriptions are created by utilizing time-consuming measurement techniques. This paper examines whether pattern-recognizing algorithms are suitable to conduct the prediction process for a steering system.