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A novel computer vision technique for rapid measurement of surface coordinates is presented. The technique is based on the marriage of a digital fringe projection technique and a fringe-phase extraction algorithm. A digitally controlled video signal in the form of linear and parallel fringes of cosinusoidal intensity variation is projected onto an object. The fringe pattern is perturbed by the three-dimensional object surface with fringe-phase containing information on the depth of the object. A phase extraction algorithm is used to determine the fringe-phase distribution, from which the three-dimensional surface coordinates are determined. The theoretical basis of this technique and some experimental results are presented in this paper.

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.

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).

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.

A new method is demonstrated for rating the “severity” of a hypoid gear set duty cycle (revolutions at torque) using the intercept of T-N curve to support gearset selection and sizing decision across vehicle programs. Historically, it has been customary to compute a cumulative damage (using Miner's Rule) for a rotating component duty cycle given a T-N curve slope and intercept for the component and failure mode of interest. The slope and intercept of a T-N curve is often proprietary to the axle manufacturer and are not published. Therefore, for upfront sizing and selection purposes representative T-N properties are used to assess relative component duty cycle severity via cumulative damage (non-dimensional quantity). A similar duty cycle severity rating can also be achieved by computing the intercept of the T-N curve instead of cumulative damage, which is the focus of this study.

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.

Our recent work has shown that representation of systems using a reliability block diagram can be used as a decision making tool. In decision making, we called these block diagrams decision topologies. In this paper, we generalize the results and show that decision topologies can be used to make many engineering decisions and can in fact replace decision analysis for most decisions. We also provide a meta-proof that the proposed method using decision topologies is entirely consistent with decision analysis at the limit. The main advantages of the method are that (1) it provides a visual representation of a decision situation, (2) it can easily model tradeoffs, (3) it can incorporate binary attributes, (4) it can model preferences with limited information, and (5) it can be used in a low-fidelity sense to quickly make a decision.

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.

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.

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.

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.

In the development of an FAW SUV, one of the goals is to achieve a state of the art drag level. In order to achieve such an aggressive target, feedback from aerodynamics has to be included in the early stage of the design decision process. The aerodynamic performance evaluation and improvement is mostly based on CFD simulation in combination with some wind tunnel testing for verification of the simulation results. As a first step in this process, a fully detailed simulation model is built. The styling surface is combined with engine room and underbody detailed geometry from a similar size existing vehicle. From a detailed analysis of the flow field potential areas for improvement are identified and five design parameters for modifying overall shape features of the upper body are derived. In a second step, a response surface method involving design of experiments and adaptive sampling techniques are applied for characterizing the effects of the design changes.

Spark ignition engines utilize catalytic converters to reform harmful exhaust gas emissions such as carbon monoxide, unburned hydrocarbons, and oxides of nitrogen into less harmful products. Aftertreatment devices require the use of expensive catalytic metals such as platinum, palladium, and rhodium. Meanwhile, tightening automotive emissions regulations globally necessitate the development of high-performance exhaust gas catalysts. So, automotive manufactures must balance maximizing catalyst performance while minimizing production costs. There are thousands of different recipes for catalytic converters, with each having a different effect on the various catalytic chemical reactions which impact the resultant tailpipe gas composition. In the development of catalytic converters, simulation models are often used to reduce the need for physical parts and testing, thus saving significant time and money.

Adhesively bonded joints have been applied in the automotive industry for the past few decades due to their advantages such as higher fatigue resistance, light weight, capability of joining dissimilar materials, good energy absorption, and high torsional stiffness for overall body structure. They also provide an effective seal against noise and vibration at a low cost. There exists the challenge of defining the fatigue characteristics of adhesive joints under cyclic loading conditions, and conventional methods have limitations in detecting the crack initiation of a bonded joint. This study introduces a method of detecting crack initiation by using the frequency method. It is found that stiffness change in the system is highly correlated to change in natural frequencies. By monitoring the change in natural frequencies, the crack initiation can be detected.

With the increased market share of electric vehicles, the demand for energy-efficient routing algorithms specifically optimized for electric vehicles has increased. Traditional routing algorithms are focused on optimizing the shortest distance or the shortest time in finding a path from point A to point B. These traditional methods have been working well for fossil fueled vehicles. Electric vehicles, on the other hand, require different route optimization techniques. Negative edge costs, battery power limits, battery capacity limits, and vehicle parameters that are only available at query time, make the task of electric vehicle routing a challenging problem. In this paper, we present an ant colony based, energy-efficient routing algorithm that is optimized and designed for electric vehicles. Simulation results show improvements in the energy consumption of electric vehicles when applied to a start-to-destination routing problem.

An application of variation simulation for predicting vehicle interior driveline vibration is presented. The model, based on a “Monte Carlo”-style approach, predicts the noise, vibration and harshness (NVH) response of the vehicle driveline based on distributions of imbalance and runout derived from manufacturing production variation (the forcing function) and the vehicle's sensitivity to the forcing function. The model is used to illustrate the change in vehicle interior vibration that results when changes are made to production variation for runout and imbalance of driveline components, and how those same changes result in different responses based on vehicle sensitivity.