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Ignition delay, using a rapid compression machine (RCM), is defined as the time period between the end of compression and the maximum rate of pressure rise due to combustion, at a given compressed condition of temperature and pressure. The same compressed conditions can be reached by a variety of combinations of compression ratio, initial temperature, initial pressure, diluent gas composition, etc. It has been assumed that the value of ignition delay, for a given fuel and at a given set of compressed conditions, would be the same, irrespective of the variety of the above-mentioned combinations that were used to achieve the compressed conditions. In this study, a range of initial conditions and compression ratios are studied to determine their effect on ignition delay time and to show how ignition delay time can differ even at the same compressed conditions.

High frequency variations in crankcase pressure have been observed in Inline-four cylinder (I4) engines and an understanding of the causes, frequency and magnitude of these variations is helpful in the design and effective operation of various engine systems. This paper shows through a review and explanation of the physics related to engine operation followed by comparison to measured vehicle data, the relationship between crankcase volume throughout the engine cycle and the observed pressure fluctuations. It is demonstrated that for a known or proposed engine design, through knowledge of the key engine design parameters, the frequency and amplitude of the cyclic variation in crankcase pressure can be predicted and thus utilized in the design of other engine systems.

The requirement of reduced emissions and improved fuel economy led the introduction of direct-injection (DI) spark-ignited (SI) engines. Dual-fuel injection system (direct-injection and port-fuel-injection (PFI)) was also used to improve engine performance at high load and speed. Ethanol is one of the several alternative transportation fuels considered for replacing fossil fuels such as gasoline and diesel. Ethanol offers high octane quality but with lower energy density than fossil fuels. This paper presents the combustion characteristics of a single cylinder dual-fuel injection SI engine with the following fueling cases: a) gasoline for PFI and DI, b) PFI gasoline and DI ethanol, and c) PFI ethanol and DI gasoline. For this study, the DI fueling portion varied from 0 to 100 percentage of the total fueling over different engine operational conditions while the engine air-to-fuel ratio remained at a constant level.

Internet worms have shown the capability to compromise millions of network hosts in a matter of seconds, thereby precluding human countermeasures. A worm over a vehicular ad hoc network (VANET) can, in addition to the well-known threats, pose a whole new class of traffic-related threats (ranging from congestion to large-scale accidents). To combat these automated adversaries, security patches can be distributed by good worms. An accurate VANET-based worm propagation model is essential to protect from malicious worms and to efficiently utilize good worms for distribution of security patches. This paper derives an approximate closed-form mathematical model of worm propagation over VANETs. Simulation results assert that the proposed model captures the VANET worm propagation dynamics with outstanding accuracy.

This paper focuses on the numerical investigation of the mixing and combustion of ethanol and gasoline in a single-cylinder 3-valve direct-injection spark-ignition engine. The numerical simulations are conducted with the KIVA code with global reaction models. However, an ignition delay model mitigates some of the deficiencies of the global one-step reaction model and is implemented via a two-dimensional look-up table, which was created using available detailed kinetics models. Simulations demonstrate the problems faced by ethanol operated engines and indicate that some of the strategies used for emission control and downsizing of gasoline engines can be employed for enhancing the combustion efficiency of ethanol operated engines.

Deep learning based approaches for object detection are heavily dependent on the nature of data used for training, especially for vehicles driving in cluttered urban environments. Consequently, the performance of Convolutional Neural Network (CNN) architectures designed and trained using data captured under clear weather and favorable conditions, could degrade rather significantly when tested under cloudy and rainy conditions. This naturally becomes a major safety issue for emerging autonomous vehicle platforms relying on CNN based object detection methods. Furthermore, despite a noticeable progress in the development of advanced visual deraining algorithms, they still have inherent limitations for improving the performance of state-of-the-art object detection. In this paper, we address this problem area by make the following contributions.

The development of connected-vehicle technology, which includes vehicle-vehicle and vehicle-infrastructure communications, opens the door for unprecedented active safety and driver-enhanced systems. In addition to exchanging basic traffic messages among vehicles for safety applications, a significantly higher level of safety can be achieved when vehicles and designated infrastructure-locations share their sensor data. In this paper, we propose a new system where cameras installed on multiple vehicles and infrastructure-locations share and fuse their visual data and detected objects in real-time. The transmission of camera data and/or detected objects (e.g., pedestrians, vehicles, cyclists, etc.) can be accomplished by many communication methods. In particular, such communications can be accomplished using the emerging Dedicated Short-Range Communications (DSRC) technology.