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This study experimentally investigates the impact of Miller cycle strategies on the combustion process, emissions, and thermal efficiency in heavy-duty diesel engines. The experiments were conducted at constant engine speed, load, and engine-out NOx (1160 rev/min, 1.76 MPa net IMEP, 4.5 g/kWh) on a single cylinder research engine equipped with a fully-flexible hydraulic valve train system. Early Intake Valve Closing (EIVC) and Late Intake Valve Closing (LIVC) timing strategies were compared to a conventional intake valve profile. While the decrease in effective compression ratio associated with the use of Miller valve profiles was symmetric around bottom dead center, the decrease in volumetric efficiency (VE) was not. EIVC profiles were more effective at reducing VE than LIVC profiles. Despite this difference, EIVC and LIVC profiles with comparable VE decrease resulted in similar changes in combustion and emissions characteristics.

This paper describes a novel design and verification process for analytical methods used in the development of advanced combustion strategies in internal combustion engines (ICE). The objective was to improve brake thermal efficiency (BTE) as part of the US Department of Energy SuperTruck program. The tools and methods herein discussed consider spray formation and injection schedule along with piston bowl design to optimize combustion efficiency, air utilization, heat transfer, emission, and BTE. The methodology uses a suite of tools to optimize engine performance, including 1D engine simulation, high-fidelity CFD, and lab-scale fluid mechanic experiments. First, a wide range of engine operating conditions are analyzed using 1-D engine simulations in GT Power to thoroughly define a baseline for the chosen advanced engine concept; secondly, an optimization and down-select step is completed where further improvements in engine geometries and spray configurations are considered.

A novel mechanical method of achieving a rapid switch between stoichiometric and lean conditions for SI engines is explored. Two and three throttle configurations, a switch strategy which employs a standard intake manifold and an assembly of pipes and throttle(s), are investigated numerically by using a one-dimensional engine simulation program based on the method of characteristics. The results indicate that it is possible to achieve rapid AFR switch without a torque jump, i.e. unperceptible to the driver.

A fracture mechanics based failure prediction strategy for load-carrying composite structures was proposed. This strategy relies on the knowledge of failure modes and local structural details to predict failure based on coupon level test data. The methodology presented here effectively predicted structural failure of a composite hat stringer based on fracture toughness test data. In addition, ply waviness was identified as a critical factor influencing the delamination failure load. The finite element modeling (FEM) technique was used to model the skin-flange region, which included ply waviness effect. The finite element analysis results were used to calculate total strain energy release rate and its Mode I and Mode II components. The finite element analysis predicted unstable delamination growth for positive waviness angles and stable delamination growth for negative waviness angles.

Controlled Auto-Ignition (CAI) combustion has been achieved in a production type 4-stroke multi-cylinder gasoline engine. The engine was based on a Ford 1.7L Zetec-SE 16V engine with a compression ratio of 10.3, using substantially standard components modified only in design dimensions to control the gas exchange process in order to significantly increase the trapped residuals. The engine was also equipped with Variable Cam Timing (VCT) on both the intake and exhaust camshafts. It was found that the largely increased trapped residuals alone were sufficient to achieve CAI in this engine and with VCT, a range of loads between 0.5 and 4 bar BMEP and engine speeds between 1000 and 3500 rpm were mapped for CAI fuel consumption and exhaust emissions. The measured CAI results were compared with those of Spark Ignition (SI) combustion in the same engine but with standard camshafts at the same speeds and loads.

Controlled Auto-Ignition (CAI) combustion was realised in a production type 4-stroke 4-cylinder gasoline engine without intake charge heating or increasing compression ratio. The CAI engine operation was achieved using substantially standard components modified only in camshafts to restrict the gas exchange process The engine could be operated with CAI combustion within a range of load (0.5 to 4 bar BMEP) and speed (1000 to 3500 rpm). Significant reductions in both specific fuel consumption and CO emissions were found. The reduction in NOx emission was more than 93% across the whole CAI range. Though unburned hydrocarbons were higher under the CAI engine operation. In order to evaluate the potential of the CAI combustion technology, the European NEDC driving cycle vehicle simulation was carried out for two identical vehicles powered by a SI engine and a CAI/SI hybrid engine, respectively.

Vehicle weight reduction has become one of the essential research areas in the automotive industry. It is important to perform design optimization of Body-in-White (BIW) at the concept design phase so that to reduce the development cost and shorten the time-to-market in later stages. Finite Element (FE) models are commonly used for vehicle design. However, even with increasing speed of computers, the simulation of FE models is still too time-consuming due to the increased complexity of models. This calls for the development of a systematic and efficient approach that can effectively perform vehicle weight reduction, while satisfying the stringent safety regulations and constraints of development time and cost. In this paper, an efficient BIW weight reduction approach is proposed with consideration of complex safety and stiffness performances. A parametric BIW FE model is first constructed, followed by the building of surrogate models for the responses of interest.

With the rapid development of autonomous driving technologies, the proportion of autonomous vehicles (AVs) will increase and influence road capacity. In this study, the simulation of mixed traffic flow was studied using an improved cellular automata model. Safety inter-vehicle spacing, the length of vehicles and reaction time are introduced into the cellular automata model. We delete the acceleration, deceleration and randomization rule for ideal conditions. Numerical simulations are utilized to analyze road capacity with different proportions of AVs. Road capacity is about 2200 pcu/h/lane for pure manual vehicle (MV) traffic flow and about 3600 pcu/h/lane for pure AV traffic flow. The capacity increases by 19.2% when there are 50% AVs in the traffic flow. And the capacity increases by around 63.6% due to the pure AV traffic flow.

One promising method for reducing fuel consumption and emissions, particularly in heavy duty trucks, is platooning. As the distance between vehicles decreases, the following vehicles will experience less aerodynamic drag on the front of the vehicle. However, reducing the velocity of the air contacting the front of the vehicle could have adverse effects on the temperature of the engine. To compensate for this effect, the energy consumption of the engine cooling system might increase, ultimately limiting the overall improvements obtained with platooning. Understanding the coupling between drag reduction and engine cooling load requirement is key for successfully implementing platooning strategies. Additionally, in a Connected and Automated Vehicle (CAV) environment, where information of the future engine load becomes available, the operation of the cooling system can be optimized in order to achieve the maximum fuel consumption reduction.

A semi-empirical model of fuel transport in the intake manifold of spark ignition engines, which assumes a fraction of injected fuel deposits onto the port walls and describes the detailed fuel film phenomena, is proposed. The model is applied in the throttle ramp transients during which both the air and the fuel flow change significantly. The predicted air fuel ratio excursions, engine torque etc, are in good agreement with the experimental data. Also simulated is another kind of transience, which has only an air flow jump, i.e. with fuelling rate constant, when the engine jumps between stoichiometric and lean running. The results are again in satisfactory agreement with experiment.

Parking-slot detection plays a critical role in the self-parking system for autonomous driving. To enhance the complexity of the environmental situations in parking-slot datasets and reduce the difficulty of manual annotation, we design several data synthesis methods to generate new parking-slots under different situations. Methods introduced in this paper include synthesizing parking-slots in AVM (around view monitor) images, generating parking-slots in fisheye images and adding 2D symbols inside parking-slots to form special ones. To test the influence of our synthetic data, we conduct a series of experiments on different tasks. In the parking-slot detection experiments, we design a novel two-stage parking-slot detection method. We use YOLOv7 as the object detector and different from previous methods, we detect the complete parking-slots and marking points at the same time. Then we match marking points and give them a certain order in the second stage.

The development of autonomous driving generally requires enormous annotated data as training input. The availability and quality of annotated data have been major restrictions in industry. Data synthesis techniques are then being developed to generate annotated data. This paper proposes a 2D data synthesis pipeline using original background images and target templates to synthesize labeled data for model training in autonomous driving. The main steps include: acquiring templates from template libraries or alternative approaches, augmenting the obtained templates with diverse techniques, determining the positioning of templates in images, fusing templates with background images to synthesize data, and finally employing the synthetic data for subsequent detection and segmentation tasks. Specially, this paper synthesizes traffic data such as traffic signs, traffic lights, and ground arrow markings in 2D scenes based on the pipeline.