This paper aims to improve thermal efficiency of spark ignition engine by numerical calculation with detailed chemistry. Experimental results from a four-stroke-single-cylinder engine are compared with that of simulations. It is experimentally found that peak efficiency is achieved at lean-limit combustion under excess air ratio λ=1.6. Due to engine output power loss, further investigations are conducted under lean-boost operations. The best condition of the lean-boost mode is at λ=1.3 and 150 kPa boosted pressure (abs). To further improve the efficiency without power loss, simulations are conducted under lean-boost combustion with dilution rate, high engine swirl, and high knock resistant fuel.
In the present study, a simplified, one-way coupled, Discrete Droplet Method is proposed to model multiple rain-droplet motions over the vehicle wall. The Lagrangian method is employed to track the droplet, on which four forces are considered to act, i.e., aerodynamic drag, adhesion, viscous friction, and gravity. Based on the numerical experiment for one droplet, we assumed a “PATH” created by a droplet on the vehicle surface, meaning a wet region, on which the viscous friction was reduced once the droplet had passed. By taking this simple PATH effect into account, better agreements with the corresponding experiment were observed for multiple-droplet cases with different vehicle configurations and flow speeds. This method appears to have a good balance between accuracy and efficiency, and will potentially be applied to other engineering problems such as iced-aircraft.
This study proposes a driver assistance system that provides an appropriate pedal operation by considering
information on the pre-preceding vehicle. The system calculates the risk of collision by taking into account not only the preceding vehicle, but also the pre-preceding vehicle ahead of the preceding car. The computed numerical risk is then translated into the visual interface that assists the driver to make an appropriate acceleration and deceleration in carfollowing. Several participants who participated in driving simulator experiments were instructed to follow a preceding as well as a visible pre-preceding vehicles with and without the driver assistance system. It was found that the assistance system succeeded in reducing the relative velocity with the pre-preceding vehicle and in eliminating the unnecessary acceleration and deceleration of the following vehicle. Safety and fuel economy were also significantly improved by introducing the proposed system.
To improve the accuracy of Injury Severity Prediction in the event of vehicle crash, a new algorithm is proposed using the US vehicle accident database (NASS-CDS). This proposed algorithm work over the base algorithm (introduced by kononen et al) in which, some of the additional variables were introduced and some of the existing variable’s classifications were modified. Results suggest that the proposed algorithm has some advantage over the base algorithm.
A model-based approach for the design and development of electrically heated catalyst (EHC) systems is presented. Based on experimental analysis of the EHC heat-up and catalytic behaviour, a detailed physico-chemical EHC model is developed. Within a total system simulation environment, different heat-up measures are compared for SULEV30 compliance. The simultaneous reduction of HC, NOx and GHG is revealed to be challenging, HC being the limiting factor for engine based thermal management. Regarding total energy consumption, a combined EHC and late PoI approach is favoured to meet emission limits. The developed operation strategy is presented in detail and confirmed by experimental findings.
The wheel speed sensor is comprised of two parts: rotary and fixed. Conventionally, the wheel speed sensor is mainly utilized to measure the vehicle velocity and acceleration. Because the rotary part and the fixed part are separate, the wheel speed varies with inducing factors, such as body pitching, suspension deflecting and wheel load fluctuating. Therefore, the wheel speed variation could be utilized to estimate vehicle attitude states. In this paper, correlations between the wheel speed variation and the inducing factors were formulated. The wheel speed could be described by a summation of four variations: body pitching, suspension deflecting, wheel load fluctuating, and driving torque. The summation correlation function was confirmed using actual vehicle tests. Based on the correlation function and a vehicle model, an estimation algorithm using a Kalman filter to calculate the body pitching velocity was proposed. In a test vehicle using the proposed algorithm, the estimation pitch velocity value and the measured value were in good agreement.