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The aims of this study are to understand the mechanism of dew condensation and try to prevent it on automobile headlamp. In this case, it is necessary to build the computational model of the headlamp with all details accurately. In addition, the simulation framework for predicting the turbulent flow field has to be accompanied with the high temperature heat source and consider high accuracy of the wall heat transfer in the running environment of a vehicle. Moreover, it is a challenging task that using CFD (Computational Fluid Dynamics) to understand the mechanism of the flow field inside the light emitting diode (LED) headlamp with a rotating fan to cool the light sources because of the complicated internal structures and significant heat transfer. In this paper, the method of compressible turbulence has been constructed which based on hierarchical cartesian grid using the HPC (High Performance Computing) environment.

This paper describes a vision-based vehicle detection system for a blind spot warning function. This detection system has been designed to provide ample performance as a driving safety support system, while streamlining the image processing algorithm so that it can be processed using the computational power of an existing ECU. The procedure used by the system to detect a vehicle in a blind spot is as follows. The system consists of four functional components: obstacle detection, velocity estimation, vertical edge detection, and final classification. In obstacle detection, a predicted image is generated under the assumption that the road surface is a perfectly flat plane, and then an object is detected based on a histogram that is created by comparing the predicted image and an actually observed image. The velocity of the object is estimated by tracking the histogram over time, assuming that both the object and the host vehicle are traveling in the same direction.

In the present study, a challenge has been made to quantitatively determine the vapor phase concentration distributions in an evaporating multicomponent fuel spray using the LAS imaging technique. The theoretical considerations were particularly given when applying the LAS imaging technique to the multicomponent fuel spray and reconstructing the vapor concentration distributions from the spray images.

The present study investigated the aerodynamic pitching stability of sedan-type vehicles under the influence of A- and C-pillar geometrical configurations. The numerical method used for the investigation is based on the Large Eddy Simulation (LES) method. Whilst, the Arbitrary Lagrangian-Eulerian (ALE) method was employed to realize the prescribed pitching oscillation of vehicles during dynamic pitching and fluid flow coupled simulations. The trailing vortices that shed from the A-pillar and C-pillar edges produced the opposite tendencies on how they affect the aerodynamic pitching stability of vehicles. In particular, the vortex shed from the A-pillar edge tended to enhance the pitching oscillation of vehicle, while the vortex shed from the C-pillar edge tended to suppress it. Hence, the vehicle with rounded A-pillar and angular C-pillar exhibited a higher aerodynamic damping than the vehicle with the opposite A- and C-pillars configurations.