Search Results

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.

A road vehicle’s cornering motion is known to be a compound motion composed mainly of forward, sideslip and yaw motions. But little is known about the aerodynamics of cornering because little study has been conducted in this field. By clarifying and understanding a vehicle’s aerodynamic characteristics during cornering, a vehicle’s maneuvering stability during high-speed driving can be aerodynamically improved. Therefore, in this study, the aerodynamic characteristics of a vehicle’s cornering motion, i.e. the compound motion of forward, sideslip and yaw motions, were investigated. We also considered proposing an aerodynamics evaluation method for vehicles in dynamic maneuvering. Firstly, we decomposed cornering motion into yaw and sideslip motions. Then, we assumed that the aerodynamic side force and yaw moment of a cornering motion could be expressed by superposing linear expressions of yaw motion parameters and those of sideslip motion parameters, respectively.

A numerical method specially designed to predict unsteady aerodynamics of road vehicle was developed based on unstructured Large-Eddy Simulation (LES) technique. The code was intensively optimized for the Earth Simulator in Japan to deal with the excessive computational resources required for LES, and could treat numerical meshes of up to around 120 million elements. Moving boundary methods such as the Arbitrary Lagrangian-Eulerian (ALE) or the sliding method were implemented to handle dynamic motion of a vehicle body during aerodynamic assessment. The method can also model a gusty crosswind condition. The method was applied to three cases in which unsteady aerodynamics are expected to be crucial.