The detailed investigation of post-oxidation phenomena based on gas emissions, PM and gas temperature were performed on a turbocharged DISI engine. In post-oxidation phenomena, rich air-fuel mixture is used inside cylinder and this rich excursion gives rise to the production of CO, THC, PM and H2 emissions. Then the scavenged O2 and emitted H2 play a key role for post-oxidation reaction (exothermic) with CO, THC and PM in exhaust manifold and in-turn can aid in increasing the temperature along with overcoming of decrease in temperature by heat absorption reaction. The actuation time delay of reaction was also investigated with emissions and temperature with fast response devices in transient mode.
Understanding the level of environmental risk using vehicle-mounted camera traffic scenes is useful in advanced driver assistance systems (ADAS) to improve vehicle safety. We propose a fast, memory-efficient computer vision based environmental risk perception method using a weakly supervised convolutional neural network-based classifier. We use traffic scenes from Berkley deep drive dataset to evaluate the proposed method. Experimental results demonstrate that the proposed method correctly classifies required driver attention levels by considering multiple environmental risk factors. Further, we use class activation mapping to demonstrate that the proposed network is capable of identifying the underlying environmental risk factors.
To improve internal combustion engine cooling systems, it is required to utilize the nucleate boiling heat transfer. Previous studies revealed it is affected by surface roughness, flow velocity, and degree of subcooling. This study investigated the heat transfer mechanisms (latent heat transport mechanism, sensible heat transport mechanism, and bubble agitation mechanism) when changing the above parameters. It was found that the increase in flow velocity and degree of subcooling increased the heat transfer due to the latent and sensible heat transport, while the increase in surface roughness increased the heat transfer due to the sensible heat transport and bubble agitation.
In order to investigate the difference of SPN emission from G-DI vehicles, which is caused by sampling locations, ninety-six tests were conducted at three vehicle benches with each two particle counters; the one was deployed at a CVS tunnel in compliance with UNECE Regulation 83 whereas the other was equipped at tailpipe with a pitot-type exhaust flow meter considered as a practice for RDE frontloading developments. As a result, the differences of all tests were within ± 30 %. This was attributed to internal particle processes as well as misalignment of signals between particle number concentration and exhaust flow rate.
The paper reviews 3D Gesture Recognition technologies, that can be potentially used in future automotive applications. The purpose of the article is to find the best 3D gesture recognition technology for mass scale adaptation in future vehicles. Described gesture recognition technologies are divided into six categories: Vision Systems, Wearable sensors, Infrared, Electric field, Radar and Ultrasonic Waves. The article identifies principles of operation for each of chosen methods, compares advantages and disadvantages of the technologies.