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A kinetic carbon monoxide (CO) emission model is developed to simulate engine out CO emissions for conventional diesel combustion. The model also incorporates physics governing CO emissions for low temperature combustion (LTC). The emission model will be used in an integrated system level model to simulate the operation and interaction of conventional and low temperature diesel combustion with aftertreatment devices. The Integrated System Model consists of component models for the diesel engine, engine-out emissions (such as NOx and Particulate Matter), and aftertreatment devices (such as DOC and DPF). The addition of CO emissions model will enhance the capability of the Integrated System Model to predict major emission species, especially for low temperature combustion. In this work a CO emission model is developed based on a two-step global kinetic mechanism [8].

Two different types of mesh used for diesel combustion with the KIVA-4 code are compared. One is a well established conventional KIVA-3 type polar mesh. The other is a non-polar mesh with uniform size throughout the piston bowl so as to reduce the number of cells and to improve the quality of the cell shapes around the cylinder axis which can contain many fuel droplets that affect prediction accuracy and the computational time. This mesh is specialized for the KIVA-4 code which employs an unstructured mesh. To prevent dramatic changes in spray penetration caused by the difference in cell size between the two types of mesh, a recently developed spray model which reduces mesh dependency of the droplet behavior has been implemented. For the ignition and combustion models, the Shell model and characteristic time combustion (CTC) model are employed.

Permanent magnet AC generators are robust, inexpensive, and efficient compared to wound-field synchronous generators with brushless exciters. Their application in variable-speed applications is made difficult by the variation of the stator voltage with shaft speed. This paper presents the use of stator-side reactive power injection as a means of regulating the stator voltage. Design-oriented analysis of machine performance for this mode of operation identifies an appropriate level of machine saliency that enables excellent terminal voltage regulation over a specified speed and load range, while minimizing stator current requirements. This paper demonstrates that the incorporation of saliency into the permanent magnet generator can significantly reduce the size of the reactive current source that is required to regulate the stator voltage during operation over a wide range of speeds and loads.

Reactivity-controlled compression ignition (RCCI) has been shown to be capable of providing improved engine efficiencies coupled with the benefit of low emissions via in-cylinder fuel blending. Much of the previous body of work has studied the benefits of RCCI operation using high injection pressures (e.g., 500 bar or greater) with common rail injection (CRI) hardware. However, low-pressure fueling technology is capable of providing significant cost savings. Due to the broad market adoption of gasoline direct injection (GDI) fueling systems, a market-type prototype GDI injector was selected for this study. Single-cylinder light-duty engine experiments were undertaken to examine the performance and emissions characteristics of the RCCI combustion strategy with low-pressure GDI technology and compared against high injection pressure RCCI operation. Gasoline and diesel were used as the low-reactivity and high-reactivity fuels, respectively.

An operational scheme with fuel-lean and exhaust gas dilution in spark-ignited engines increases thermal efficiency and decreases NOx emission, while these operations inherently induce combustion instability and thus large cycle-to-cycle variation in engine. In order to stabilize combustion variations, the development of an advanced ignition system is becoming critical. To quantify the impact of spark-ignition discharge, ignitability tests were conducted in an optically accessible combustion vessel to characterize the flame kernel development of lean methane-air mixture with CO₂ simulating exhaust diluent. A shrouded fan was used to generate turbulence in the vicinity of J-gap spark plug and a Variable Output Ignition System (VOIS) capable of producing a varied set of spark discharge patterns was developed and used as an ignition source. The main feature of the VOIS is to vary the secondary current during glow discharge including naturally decaying and truncated with multiple strikes.

Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty.

A copula-based approach for model bias characterization was previously proposed [18] aiming at improving prediction accuracy compared to other model characterization approaches such as regression and Gaussian Process. This paper proposes an adaptive copula-based approach for model bias identification to enhance the available methodology. The main idea is to use cluster analysis to preprocess data, then apply the copula-based approach using information from each cluster. The final prediction accumulates predictions obtained from each cluster. Two case studies will be used to demonstrate the superiority of the adaptive copula-based approach over its predecessor.

To date, model validation metric is prominently designed for non-dynamic model responses. Though metrics for dynamic responses are also available, they are specifically designed for the vehicle impact application and uncertainties are not considered in the metric. This paper proposes the validation metric for general dynamic system responses under uncertainty. The metric makes use of the popular U-pooling approach and extends it for dynamic responses. Furthermore, shape deviation metric was proposed to be included in the validation metric with the capability of considering multiple dynamic test data. One vehicle impact model is presented to demonstrate the proposed validation metric.

Simulation based design optimization has become the common practice in automotive product development. Increasing computer models are developed to simulate various dynamic systems. Before applying these models for product development, model validation needs to be conducted to assess their validity. In model validation, for the purpose of obtaining results successfully, it is vital to select or develop appropriate metrics for specific applications. For dynamic systems, one of the key obstacles of model validation is that most of the responses are functional, such as time history curves. This calls for the development of a metric that can evaluate the differences in terms of phase shift, magnitude and shape, which requires information from both time and frequency domain. And by representing time histories in frequency domain, more intuitive information can be obtained, such as magnitude-frequency and phase-frequency characteristics.

Design optimization methods are commonly used for weight reduction subjecting to multiple constraints in automotive industry. One of the major challenges remained is to deal with a large number of design variables for large-scale design optimization problems effectively. In this paper, a new approach based on fuzzy rough set is proposed to address this issue. The concept of rough set theory is to deal with redundant information and seek for a reduced design variable set. The proposed method first exploits fuzzy rough set to screen out the insignificant or redundant design variables with regard to the output functions, then uses the reduced design variable set for design optimization. A vehicle body structure is used to demonstrate the effectiveness of the proposed method and compare with a traditional weighted sensitivity based main effect approach.

One of the major challenges in multiobjective, multidisciplinary design optimization (MDO) is the long computational time required in evaluating the new designs' performances. To shorten the cycle time of product design, a data mining-based strategy is developed to improve the efficiency of heuristic optimization algorithms. Based on the historical information of the optimization process, clustering and classification techniques are employed to identify and eliminate the low quality and repetitive designs before operating the time-consuming design evaluations. The proposed method improves design performances within the same computation budget. Two case studies, one mathematical benchmark problem and one vehicle side impact design problem, are conducted as demonstration.

Ultrasonic fatigue tests (testing frequency around 20 kHz) have been conducted on four different cast aluminum alloys each with a distinct composition, heat treatment, and microstructure. Tests were performed in dry air, laboratory air and submerged in water. For some alloys, the ultrasonic fatigue lives were dramatically affected by the environment humidity. The effects of different factors like material composition, yield strength, secondary dendrite arm spacing and porosity were investigated; it was concluded that the material strength may be the key factor influencing the environmental humidity effect in ultrasonic fatigue testing. Further investigation on the effect of chemical composition, especially copper content, is needed.

The paper compares two different design concepts for a range extender engine rated at 30 kW at 4500 rpm. The first project is a conventional 4-Stroke SI engine, 2-cylinder, 2-valve, equipped with port fuel injection. The second is a new type of 2-Stroke loop scavenged SI engine, featuring a direct gasoline injection and a patented rotary valve for enhancing the induction and scavenging processes. Both power units have been virtually designed with the help of CFD simulation. Moreover, for the 2-Stroke engine, a prototype has been also built and tested at the dynamometer bench, allowing the authors to make a reliable theoretical comparison with the well assessed 4-Stroke unit.

This paper proposes the CFD simulation of the oil splashing within the discs’ chamber of a novel concept for axle dry braking system in off-highway vehicles. The system completely removes the lubricating oil from the discs’ chamber during the not-engaged configuration of the friction plates and it quickly restore it at the beginning of the braking stage when the discs’ cooling becomes crucial, thus ensuring a significant reduction of the power losses. The CFD analysis of the real component is performed to predict the efficiency of the system in terms of both the time needed to replenish the discs’ chamber when brake is actuated, and the hydraulic torque exerted by the splashing of the oil. The entire three-dimensional geometry of the domain is accurately discretized, and the multi-phase flow nature is addressed by means of the volume of fluid approach.

Knock is one of the main limitations on Spark-Ignited (SI) Internal Combustion Engine (ICE) performance and efficiency and so has been the object of study for over one hundred years. Great strides have been made in terms of understanding in that time, but certain rather elementary practical problems remain. One of these is how to interpret if a running engine is knocking and how likely this is to result in damage. Knocking in a development environment is typically quantified based on numerical descriptions of the high frequency content of a cylinder pressure signal. Certain key frequencies are observed, which Draper [1] explained with fundamental acoustic theory back in 1935. Since then, a number of approaches of varying complexity have been employed to correlate what is happening within the chamber with what is measured by a pressure transducer.

In recent years, internal combustion engine (ICE) downsizing coupled with turbocharging was considered the most effective path to improve engine efficiency at low load, without penalizing rated power/torque performance at full load. On the other side, issues related to knocking combustion and excessive exhaust gas temperatures obliged adopting countermeasures that highly affect the efficiency, such as fuel enrichment and delayed combustion. Powertrain electrification allows operating the ICE mostly at medium/high loads, shifting design needs and constraints towards targeting high efficiency under those operating conditions. Conversely, engine efficiency at low loads becomes a less important issue. In this track, the aim of this work is the investigation of the potential of the oversizing of a small Variable Valve ActuationSpark Ignition gasoline engine towards efficiency increase and tailpipe emission reduction.

The ever-increasing quest for sustainable mobility is pushing the automotive sector towards electric-based technologies, allowing the reduction of localized emission sources in highly populated urban areas. Among the many possible solutions, Proton Exchange Membrane Fuel Cells (PEMFC) have the potential to de-carbonise the automotive sector without the range anxiety of present and future batteries. The interaction between physical and chemical processes in PEMFC is crucial to their maximum attainable efficiency, albeit the complexity of such interplay still limits a complete understanding of the governing processes. In this paper a canonical PEMFC from literature is simulated using 3D-CFD, and results are compared against experiments. A Eulerian multi-phase/multi-physics non-isothermal framework is used to account for both fluid (gas channels, porous gas diffusion layers) and solid (bi-polar plates, membrane), as well as for electrochemical and sorption reactions.

The increasing limitations in engine emissions and fuel consumption have led researchers to the need to accurately predict combustion and related events in gasoline engines. In particular, knock is one of the most limiting factors for modern SI units, severely hindering thermal efficiency improvements. Modern CFD simulations are becoming an affordable instrument to support experimental practice from the early design to the detailed calibration stage. To this aim, combustion and knock models in RANS formalism provide good time-to-solution trade-off allowing to simulate mean flame front propagation and flame brush geometry, as well as “ensemble average” knock tendency in end-gases. Still, the level of confidence in the use of CFD tools strongly relies on the possibility to validate models and methodologies against experimental measurements.

With the aim of identifying technical solutions to lower the particulate matter emissions, the engine research community made a consistent effort to investigate the root causes leading to soot formation. Nowadays, the computational power increase allows the use of advanced soot emissions models in 3D-CFD turbulent reacting flows simulations. However, the adaptation of soot models originally developed for Diesel applications to gasoline direct injection engines is still an ongoing process. A limited number of studies in literature attempted to model soot produced by gasoline direct injection engines, obtaining a qualitative agreement with the experiments. To the authors’ best knowledge, none of the previous studies provided a methodology to quantitatively match particulate matter, particulate number and particle size distribution function measured at the exhaust without a case-by-case soot model tuning.

Studies have shown that there are a significant number of chemical species present in engine exhaust particulate matter emissions. Additionally, the majority of current world-wide regulatory methods for measuring engine particulate emissions are gravimetrically based. As modern engines considerably reduce particulate mass emissions, these methods become less stable and begin to display higher levels of measurement uncertainty. In this study, a characterization of mass emissions from three heavy-duty diesel engines, with a range of particulate emission levels, was made in order to gain a better understanding of the variability and uncertainty associated with common mass measurement methods, as well as how well these methods compare with each other. Two gravimetric mass measurement methods and a reconstructed mass method were analyzed as part of the present study.