Automotive Science and Engineering

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Conventional compression ignition (CI) engines are known for their high thermal efficiency compared to spark ignited (SI) engines. Gasoline because of its higher ignition delay has much lower soot emission in comparison with diesel fuel. Using double injection strategy reduces the maximum heat release rate that leads to NOx emission reduction. In this paper, a numerical study of a gasoline fuelled heavy duty Caterpillar 3401 engine was conducted via three dimensional computational fluid dynamics (CFD) procedures and compared with experimental data. The model results show a good agreement with experimental data. To have a better design the effect of injection characteristics such as, the main SOI timing, injection duration and nozzle hole size investigated on combustion and emissions and an optimized point find. The results suggest an optimization in injection characteristics for simultaneous reduction of NOx and soot emissions with negligible change in IMEP.

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Vehicular ad-hoc network (VANET) is an important component of intelligent transportation systems, in which vehicles are equipped with on-board computing and communication devices which enable vehicle-to-vehicle communication. Consequently, with regard to larger communication due to the greater number of vehicles, stability of connectivity would be a challenging problem. Clustering technique as one of the most important data mining techniques is a possible method that can improve the stability of connectivity in VANET. Stable communication within a VANET leads to enhanced driver safety and better traffic management. Therefore, this paper presented a novel clustering algorithm based on ant system-based algorithm called IASC in order to provide a fast clustering algorithm with high accuracy and improve the stability of VANET topology. A comparative study was proposed to analogize the results of the proposed algorithm with six VANET clustering algorithms in the literature which were taken as benchmarks. Results revealed improvement in stability and overhead on VANET.

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In order to lowering level of emissions of internal combustion engines (ICEs), they should be optimally controlled. However, ICEs operate under numerous operating conditions, which in turn makes it difficult to design controller for such nonlinear systems. In this article, a generalized unique controller for idle speed control under whole loading conditions is designed. In the current study, instead of tedious time-consuming trial-and-error based methods, soft computing techniques are employed to tune a proportional-integral-derivative (PID) controller which controls idle speed of engine. Since model based design technique is employed, a mean value model (MVM) is taken advantage due to its evidenced merits. Moreover, a brief introduction to the selected meta-heuristics is given followed by a flowchart to show how the engine model is linked to the optimization algorithms. A set point of 750 rpm is fed to the system, and the weighted sum of the three characteristics of mean squared error, control energy, and percent overshoot of the control system is set to the problem objective function to be minimized. It is evidenced that of all the examined meta-heuristics, Bees Algorithm (BA) converges to a better solution. Finally, to consider the effectiveness of the developed optimal controllers in disturbance rejection, they are implemented to the engine MVM model. The results of the research indicate, all the four optimally designed control systems, albeit the intermediate superiority, are of conspicuous success in compensating for the input disturbances of the load torque.