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With the increasing number of hybrid vehicles in the Chinese market, research on aftertreatment systems for hybrid vehicles has become very popular. China has currently implemented national on-road China 6 regulations for emission control of all gasoline and diesel vehicles, including hybrid ones. So far, there are few papers on the optimization of aftertreatment for hybrid gasoline vehicles. Due to the introduction of electric motors in hybrid vehicles, the engine starts frequently and leads to inconsistent stability of engine operating conditions and brings the challenge to emission control of engine exhaust. This article selects a highly popular hybrid gasoline vehicle in China for research, which is a dual-mode hybrid (DM hybrid) passenger car. There is an obvious correlation between the emissions between the driving pattern and the hybrid strategy.

Sub-23nm particles emission from the light-duty vehicle is widely discussed now and possible to be counted into the next stage emission legislation, such as Euro7. In this article, 16 China6 gasoline vehicles were tested over the WLTC and two surrogate RDE lab cycles for particulate number (PN) emission, the difference between PN23 (particle size >23nm) and PN10 (particle size>10nm) emission was analyzed. Testing results showed that the average PN10 emission increased 59% compared to PN23, which will bring great challenges for those vehicles to meet the future regulation requirement if sub-23nm particle is counted. The sub-23nm particles emission was proportional to the PN23 particles emission and generated mostly from the cold start or the transient engine conditions with rich combustion. Compared to the proposal of Euro 7, PN10 emission from some tested vehicles will need further two orders of magnitude reduction.

Aiming at the fault that the speed signal of the automatic transmission output shaft Hall-type speed sensor fluctuates abnormally due to the change of the air gap, the method of fault detection and diagnosis is proposed. Firstly, a limited low-pass filter module was designed according to the characteristics of the fault, and a good filter effect is achieved. Secondly, by comparing the signals before and after filtering, a residual generator is designed, and an adaptive dynamic threshold is designed by analyzing the causes and influencing factors of the residual, which can configure a reliable and effective threshold for the generated residual in real time, which improves the fault identification robustness and effectiveness. Then, a fault debounce method is designed to avoid frequent false alarms of occasional faults. Finally, simulation verification proves the effectiveness of the method.

An engine exhaust manifold undergoes repeated thermal expansion and contraction due to temperature variation. Thermomechanical fatigue (TMF) arises due to the boundary constraints on thermal expansion so that mechanical strain is introduced. Therefore, TMF evaluation is very important in engine design. In this work, the mechanical properties important for TMF assessment and modeling of a silicon (Si)- and molybdenum (Mo)-containing ductile cast iron used for exhaust manifold have been evaluated. Tensile, creep, isothermal low cycle fatigue (LCF), and TMF tests have been conducted. Parameters for material modeling, such as the viscoplastic constitutive model and the Neu-Sehitoglu TMF damage model, have been calibrated, validated, and used to evaluate the TMF life of the exhaust manifold.

One field-returned DPF loaded with a high amount of ash is examined using experimental and modeling approaches. The ash-related design factors are collected by coupling the inspection results from terahertz spectroscopy with a calibrated DPF model. The obtained ash packing density, ash layer permeability and ash distribution profile are then used in the simulation to assess the ash impact on DPF backpressure and regeneration behaviors. The following features have been observed during the simulation: 1 The ash packing density, ash layer permeability and ash distribution profile should be collected at the same time to ensure the accurate prediction of ash impact on DPF backpressure. Missing one ash property could mislead the measurement of the other two parameters and thus affects the DPF backpressure estimation. 2 The ash buildup would gradually increase the frequency for the backpressure-based active soot regeneration.

Diesel emission aftertreatment system is usually designed to meet stringent packaging constraints, rendering a difficult situation to achieve perfect flow distribution inside the catalytic unit. The non-uniform flow pattern leads to a mal-distribution of flow velocity, temperature, and gas species in catalyst unit. Some catalysts are exposed to harsh working environment, while the rest catalysts are underutilized. This lowers the efficiency of overall catalyst unit and thus requires an oversized system to meet emission requirements. The flow mal-distribution also accelerates the uneven catalyst degradation, lowering the system durability. Hence, a quantitative description of packaging impact on catalyst performance is critical to assess the system efficiency and durability. In the present work, a mapping method is developed to combine catalyst performance with computational fluid dynamics (CFD) simulation.

This study explores an Adaptive Injection Strategy (AIS) that employs multiple injections at both low and high pressures to reduce spray-wall impingement, control combustion phasing, and limit pressure rise rates in a Premixed Compression Ignition (PCI) engine. Previous computational studies have shown that reducing the injection pressure of early injections can prevent spray-wall impingement caused by long liquid penetration lengths. This research focuses on understanding the performance and emissions benefits of low and high pressure split injections through experimental parametric sweeps of a 0.48 L single-cylinder test engine operating at 2000 rev/min and 5.5 bar nominal IMEP. This study examines the effects of 2nd injection pressure, EGR, swirl ratio, and 1st and 2nd injection timing, for both single heat release and two-peak high temperature heat release cases. In order to investigate the AIS concept experimentally, a Variable Injection Pressure (VIP) system was developed.

A comparative analysis was made on the emissions from a 2004 and a 2007 heavy-duty diesel engine to determine how new engine and emissions technologies have affected the chemical compounds found in the exhaust gases. Representative samples were collected from a source dilution sampling system and analyzed for both criteria and unregulated gaseous and particulate emissions. Results have shown that the 2007 regulations compliant engine and emissions technology not only reduced the specifically regulated exhaust pollutants, but also significantly reduced the majority of unregulated chemical species. It is believed that these reductions were achieved through the use of engine optimization, aftertreatment system integration, and ultra-low sulfur diesel fuel.

A new search technique, called Non-Gradient Step-Controlled algorithm (NGSC), is presented. The NGSC was applied independently from pre-selected starting points and as a supplement to a Genetic Algorithm (GA) to optimize a HSDI diesel engine using split injection strategies. It is shown that the NGSC handles well the challenges of a complex response surface and factor high-dimensionality, which demonstrates its capability as an efficient and accurate tool to seek “local” convergence on complex surfaces. By directly tracking the change of a merit function, the NGSC places no requirement on response surface continuity / differentiability, and hence is more robust than gradient-dependent search techniques. The directional search mechanism takes factor interactions into consideration, and search step size control is adopted to facilitate search efficiency.

Multiple injection strategies have been revealed as an efficient means to reduce diesel engine NOx and soot emissions simultaneously, while maintaining or improving its thermal efficiency. Empirical soot models widely adopted in engine simulations have not been adequately validated to predict soot formation with multiple injections. In this work, a multiple-step phenomenological (MSP) soot model that includes particle inception, surface growth, oxidation, and particle coagulation was revised to better describe the physical processes of soot formation in diesel combustion. It was found that the revised MSP model successfully reproduces measured soot emission dependence on the start-of-injection timing, while the two-step empirical and the original MSP soot models were less accurate. The revised MSP model also predicted reasonable soot and intermediate species spatial profiles within the combustion chamber.

Low-temperature combustion concepts that utilize cooled EGR, early/retarded injection, high swirl ratios, and modest compression ratios have recently received considerable attention. To understand the combustion and, in particular, the soot formation process under these operating conditions, a modeling study was carried out using the KIVA-3V code with an improved phenomenological soot model. This multi-step soot model includes particle inception, surface growth, surface oxidation, and particle coagulation. Additional models include a piston-ring crevice model, the KH/RT spray breakup model, a droplet wall impingement model, a wall heat transfer model, and the RNG k-ε turbulence model. The Shell model was used to simulate the ignition process, and a laminar-and-turbulent characteristic time combustion model was used for the post-ignition combustion process.

Multiple injection strategies have been experimentally and computationally studied for simultaneously reducing diesel engine NOx and particulate emissions. However, injection strategies featuring three or more pulses per engine cycle have not been sufficiently studied previously. The large number of parameters to be considered, in addition to the complicated interactions among them, challenge the capability of experimental hardware, computational models, and optimization methods. In the present work, multiple injection strategies including up to five pulses per engine cycle, are computationally investigated to optimize High Speed Direct Injection (HSDI) diesel engine combustion and emissions at a single part-load operating condition. The KIVA-3V code coupled with a Genetic Algorithm were used as the modeling and optimization tools, respectively. It was found that widely separated injection with two-stage combustion appears to provide optimal HSDI diesel performance at part load.

A CAE modeling methodology has been developed for modeling laser and MIG (metal-inert-gas) weld separations. This new methodology can simulate weld failure in CAE aluminum vehicle crash analysis using a failure formulation derived from coupon test results. It is a generalized method and is intended to be applicable to any combination of the parameters such as thickness, material, and type of weld and impact speed. The method has been validated on the crash tests on straight and S-type rails with a hat section. The CAE prediction based on the modeling procedure correlates well with the test results for all the rail crush cases. The finite element analysis was conducted in RADIOSS environment. The welds are modeled using the beam-type spring element with the new weld damage parameters. The baseline curves for the spring element and the detailed projection equations developed are provided in this paper.

Ford designed and built a midsize family sedan for the PNGV (Partnership for a New Generation of Vehicle). The side impact performance of the aluminum vehicle and the current CAE capability was studied. The vehicle was tested according to the specifications of FMVSS 214. The results show the vehicle meet the federal safety requirements. The impact performances of the front and rear dummies were comparable to those of the steel counterpart. CAE analysis was conducted to develop the body component design and to predict the structural and dummy responses. The results show that without modeling of the joint (rivet and weld) separation, the accuracy of the CAE crash analysis for this aluminum vehicle was inadequate. When empirical separation criteria were incorporated to model the joint, analysis results correlated with the test. Further development of robust modeling methods for joint separation is needed to improve the prediction of aluminum structure crash responses.

Using finite element technique to model the human cervical spine can be found in a number of publications in the literature. These efforts have illustrated viable techniques and approaches for simulating the three-dimensional motion of the human cervical spine. However, these earlier studies also revealed difficulties due to insufficient geometric description for such a complex structure and the lack of experimental data for characterizing the mechanical behavior of the biological tissues in this anatomical region. Recent advancement of the computer technology has resulted in a large quantity of digital images of the human anatomical structure with high precision. In addition, new experimental techniques have also produced new test data on human biological tissue properties. In this study, we developed a finite element representation of the human cervical spine using detailed 3D anatomical data.