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The thermal efficiency of spark-ignition engines can be enhanced by increasing the rate of exhaust gas recirculation (EGR) such that the low temperature combustion regime could be achieved. However, there is an upper limit on the amount of EGR rate, beyond which flame speed becomes slow and unstable, and local quenching starts to hurt the combustion stability, efficiency, and emission. To resolve this issue, the concept of dedicated EGR has been proposed previously to be an effective way to enhance flame propagation under lean burn condition with even higher levels of EGR with reformate hydrogen and carbon monoxide. In this study, the effects of thermochemical fuel reforming on the reformate composition under rich conditions (1.0 < ϕ < 2.0) have been studied using detailed chemistry for iso-octane, as the representative component for gasoline.

A dimensionless relationship that estimates the maximum bearing load of a 4-cylinder 4-stroke in-line engine has been found. This relationship may assist the design engineer in choosing a desired counterweight mass. It has been demonstrated that: 1) the average bearing load increases with engine speed and 2) the maximum bearing load initially decreases with engine speed, reaches a minimum, then increases quickly with engine speed. This minimum refers to a transition speed at which the contribution of the inertia force overcomes the contribution of the maximum pressure force to the maximum bearing load. The transition speed increases with an increase of counterweight mass and is a function of maximum cylinder pressure and the operating parameters of the engine.

Flame speed data reported in most literature are acquired in conventional apparatus such as the spherical combustion bomb and counterflow burner, and are limited to atmospheric pressure and ambient or slightly elevated unburnt temperatures. As such, these data bear little relevance to internal combustion engines and gas turbines, which operate under typical pressures of 10-50 bar and unburnt temperature up to 900K or higher. These elevated temperatures and pressures not only modify dominant flame chemistry, but more importantly, they inevitably facilitate pre-ignition reactions and hence can change the upstream thermodynamic and chemical conditions of a regular hot flame leading to modified flame properties. This study focuses on how auto-ignition chemistry affects flame propagation, especially in the negative-temperature coefficient (NTC) regime, where dimethyl ether (DME), n-heptane and iso-octane are chosen for study as typical fuels exhibiting low temperature chemistry (LTC).

Designing a vehicle for superior crash safety performance in consumer rating tests such as US-NCAP is a compelling target in the design of passenger vehicles. In today’s context, there is also a high emphasis on making a vehicle as lightweight as possible which calls for an efficient design. In modern vehicle design, these objectives can only be achieved through Computer-Aided Engineering (CAE) for which a detailed CAD (Computer-Aided Design) model of a vehicle is a pre-requisite. In the absence of the latter (i.e. a matured CAD model) at the initial and perhaps the most crucial phase of vehicle body design, a rational approach to design would be to resort to a knowledge-based methodology which can enable crash safety assessment of an assumed design using artificial intelligence techniques such as neural networks.

Selecting the right transportation platform is challenging, whether it is at a personal level or at an organizational level. In settings where predominantly the functional aspects rule the decision making process, defining the mobility of a vehicle is critical for comparing different offerings and making acquisition decisions. With the advent of intelligent vehicles, exhibiting partial to full autonomy, this challenge is exacerbated. The same vehicle may traverse independently and with greater tolerance for acceleration than human occupied vehicles, while, at the same time struggle with obstacle avoidance. The problem presents itself at the individual vehicle sensing level and also at the vehicle/fleet level. At the sensing and information level, one can be looking at issues of latency, bandwidth and optimal information fusion from multiple sources including privileged sensing. At the overall vehicle level, one focuses more on the ability to complete missions.

With the emergence of intelligent ground vehicles, an objective evaluation of vehicle mobility has become an even more challenging task. Vehicle mobility refers to the ability of a ground vehicle to traverse from one point to another, preferably in an optimal way. Numerous techniques exist for evaluating the mobility of vehicles on paved roads, both quantitatively and qualitatively, however, capabilities to evaluate their off-road performance remains limited. Whereas a vehicle’s off-road mobility may be significantly enhanced with intelligence, it also introduces many new variables into the decision making process that must be considered. In this paper, we present a decision analytic framework to accomplish this task. In our approach, a vehicle’s mobility is modeled using an operator’s preferences over multiple mobility attributes of concern. We also provide a method to analyze various operating scenarios including the ability to mitigate uncertainty in the vehicles inputs.

Tubular hydroforming is a novel process that has recently gained much attention due to its cost-effective application in the automotive industry. Hydroformed automotive parts have high strength to weight ratio and have good repeatability with high dimensional accuracy. At this time, there is little experience in modeling the hydroforming process to better understand its application and researchers have tried using stamping simulation software to analyze the process. Unlike conventional sheet stamping which is a displacement driven process, tubular hydroforming is a force driven process and its success is governed by the nature of internal pressurization. Hence, a new three-dimensional finite element model using a computationally efficient 6-noded shell element has been developed. A simple pressure prediction model has been developed and integrated into the formulation for effective control of the process.

Lane line detection is a very critical element for Advanced Driver Assistance Systems (ADAS). Although, there has been significant amount of research dedicated to the detection and localization of lane lines in the past decade, there is still a gap in the robustness of the implemented systems. A major challenge to the existing lane line detection algorithms stems from coping with bad weather conditions (e.g. rain, snow, fog, haze, etc.). Snow offers an especially challenging environment, where lane marks and road boundaries are completely covered by snow. In these scenarios, on-board sensors such as cameras, LiDAR, and radars are of very limited benefit. In this research, the focus is on solving the problem of improving robustness of lane line detection in adverse weather conditions, especially snow. A framework is proposed that relies on using Vehicle-to-Infrastructure (V2I) communication to access reference images stored in the cloud.

When performing trajectory planning for robotic applications, there are many aspects to consider, such as the reach conditions, joint and end-effector velocities, accelerations and jerk conditions, etc. The reach conditions are dependent on the end-effector orientations and the robot kinematic structure. The reach condition feasibility is the first consideration to be addressed prior to optimizing a solution. The ‘functional’ work space or work window represents a region of feasible reach conditions, and is a sub-set of the work envelope. It is not intuitive to define. Consequently, 2D solution approaches are proposed. The 3D travel paths are decomposed to a 2D representation via radial projections. Forward kinematic representations are employed to define a 2D boundary curve for each desired end effector orientation.

Dang Van (Dang Van et al., 1982 and Dang Van, 1993) states that for an infinite lifetime (near fatigue limit), crack nucleation in slip bands may occur at the most unfavorable oriented grains, which are subject to plastic deformation even if the macroscopic stress is elastic. Since the residual stresses in these plastically deformed grains are induced by the restraining effect of the adjacent grains, it is assumed that the residual stresses are stabilized at a mesoscopic level. These stresses are currently approximated by the macroscopic hydrostatic stress defined by the normal stresses to the faces of an octahedral element oriented with the faces symmetric to the principal axis; mathematically they are equal to each other and they are the average of the principal stresses.

Fatigue life estimation, reliability and durability are important in acquisition, maintenance and operation of vehicle systems. Fatigue life is random because of the stochastic load, the inherent variability of material properties, and the uncertainty in the definition of the S-N curve. The commonly used fatigue life estimation methods calculate the mean (not the distribution) of fatigue life under Gaussian loads using the potentially restrictive narrow-band assumption. In this paper, a general methodology is presented to calculate the statistics of fatigue life for a linear vibratory system under stationary, non-Gaussian loads considering the effects of skewness and kurtosis. The input loads are first characterized using their first four moments (mean, standard deviation, skewness and kurtosis) and a correlation structure equivalent to a given Power Spectral Density (PSD).

Real-time measurement or estimation of crank-angle-resolved engine cylinder pressure may become commonplace in the next generation of engine controllers to optimize spark, valve timing, or compression ratio. Toward the development of a real-time cylinder pressure estimator, this work presents a crank-angle-resolved engine cylinder pressure estimation model that could accept inputs such as speed, manifold pressure and throttle position, and deliver crank-angle resolved cylinder pressure in real-time, at engine speeds covering the useful operating range of most engines. The model was validated by comparing simulated cylinder pressure with thirteen sets of cylinder pressure data, from two different commercial engines from two different OEMs. Estimated pressures were compared against the actual measured pressure traces. The average relative error is about 3% while the maximum relative error is 5%. Both can be improved with further tuning.

Mobility assessment for combat vehicles is often a great challenge for the military due to various subjective attributes. The attributes' characteristics vary significantly depending on the vehicle type and its operating environments such as terrain, weather, and human factors. A clear definition and relationship between multiple attributes including human factors is necessary to assess mobility. To the best of authors' knowledge, many existing mobility assessment techniques use complex analytical methods and focus on individual attributes. In this paper, for the first time, the authors propose a novel approach to define vehicle mobility and its influencing attributes using qualitative linguistic fuzzy variables, which are defined as having values between 0 and 1. The authors also propose a fuzzy logic mobility (FLM) model and a simulation approach to assess a combat vehicle's mobility.

Due to the variation of compartment design and occupant’s posture in self-driving cars, there is a new and major challenge for occupant protection. In particular, the studies on occupant restraint systems used in the self-driving car have been significantly delayed compared to the development of the autonomous technologies. In this paper, a numerical study was conducted to investigate the effectiveness of three typical restraint systems on the driver protection in three different scenarios.

Shearography has been proved to be highly effective for nondestructive testing (NDT), especially for NDT of composite materials used in the automotive and aerospace engineering. While its application in material testing and material research has already achieved more and more acceptance in research and industry, its applications are mainly limited to the inspection and testing of an object surface which can directly be observed by a shearographic camera. Its application is mainly limited to inspect and test an object surface which can directly be observed by a shearographic camera. It is impossible to inspect an internal surface of a container. If the reflected light of the surface, which has to be examined, can’t reach the shearographic camera there is still no inspection possible. This paper presents the development of a rigid shearographic endoscope. The development enabled shearographic inspection on both external and internal surfaces of objects.

Significant A/F ratio excursion may occur during some engine transient operations, especially for transient periods of throttle tip in or tip out. A/F ratio excursion results in excessive emissions, extra fuel consumption, driveability deterioration and three-way-catalyst (TWC) efficiency drop. Simple two-parameter (X, τ) wall wetting models have traditionally been used to describe this transient A/F ratio excursion phenomenon. The transient fuel control techniques are utilized for this model to be applicable across vehicles, engines, fuel types and ambient conditions, so as to compensate for the A/F ratio excursion with the extra compensation fuel. More complicated model structures must be further expanded and model dependence on various environment conditions must be established to achieve a precise model. In this paper, a simple linear approach is proposed for transient fuel control, using least squares estimation.

It is recognized that there is a dearth of studies that provide a comprehensive understanding of vehicle-occupant system dynamics for various road conditions, sitting occupancies and vehicle velocities. In the current work, an in-house-developed 50 degree-of-freedom (DOF) multi-occupant vehicle model is employed to obtain the vehicle and occupant biodynamic responses for various cases of vehicle velocities and road roughness. The model is solved using MATLAB scripts and library functions. Random road profiles of Classes A, B, C and D are generated based on PSDs (Power Spectral Densities) of spatial and angular frequencies given in the manual ISO 8608. A study is then performed on vehicle and occupant dynamic responses for various combinations of sitting occupancies, velocities and road profiles. The results obtained underscore the need for considering sitting occupancies in addition to velocity and road profile for assessment of ride comfort for a vehicle.

Experimental studies of soot particles showed that the intensity ratio of amorphous and graphite layers measured by Raman spectroscopy correlates to soot oxidation reactivities, which is very important for regeneration of the diesel particulate filters and gasoline particulate filters. This physical mechanism is absent in all soot models. In the present paper, a novel two-layer soot model was proposed that considers the amorphous and graphite layers in the soot particles. The soot model considers soot inception, soot surface growth, soot oxidation by O2 and OH, and soot coagulation. It is assumed that amorphous-type soot forms from fullerene. No soot coagulation is considered in the model between the amorphous- and graphitic-types of soot. Benzene is taken as the soot precursor, which is formed from acetylene. The model was implemented into a commercial CFD software CONVERGE using user defined functions. A diesel engine case was simulated.

High-speed video of combustion processes and cylinder pressure traces were obtained from a single-cylinder optical-accessible engine with a production four-valve cylinder head to study the mixture formation and flame propagation characteristics at near-stoichiometric start condition. Laser-sheet Mie-scattering images were collected for liquid droplet distributions inside the cylinder to correlate the mixture formation process with the combustion results. A dual-stream (DS) injector and a quad-stream (QS) injector were used to study the spray dispersion effect on engine starting, under different injection timings, throttle valve positions, engine speeds, and intake temperatures. It was found that most of the fuel under open-valve injection (OVI) conditions entered the cylinder as droplet mist. A significant part of the fuel droplets hit the far end of the cylinder wall at the exhaust-valve side.

In the dense fabric of urban areas, electric scooters have rapidly become a preferred mode of transportation. As they cater to modern mobility demands, they present significant safety challenges, especially when interacting with pedestrians. In general, e-scooters are suggested to be ridden in bike lanes/sidewalks or share the road with cars at the maximum speed of about 15-20 mph, which is more flexible and much faster than pedestrians and bicyclists. Accurate prediction of pedestrian movement, coupled with assistant motion control of scooters, is essential in minimizing collision risks and seamlessly integrating scooters in areas dense with pedestrians. Addressing these safety concerns, our research introduces a novel e-Scooter collision avoidance system (eCAS) with a method for predicting pedestrian trajectories, employing an advanced Long short-term memory (LSTM) network integrated with a state refinement module.