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Diesel engine exhaust aftertreatment components, especially the diesel particulate filter (DPF), are subject to various modes of degradation over their lifetimes. One particular adverse effect on the DPF is the significant rise in pressure drop due to the accumulation of engine lubricant-derived ash which coats the inlet channel walls effectively decreasing the permeability of the filter. The decreased permeability due to ash in the DPF can result in increased filter pressure drop and decreased fuel economy. A unique two-step approach, consisting of experimental measurements and direct numerical simulations using ultra-high resolution 3D imaging data, has been utilized in this study to better understand the effects of ash accumulation on engine aftertreatment component functionality.

The Engine-Out Particulate Matter (EOPM) was collected from a spark ignition engine operating in steady state using a heated quartz fiber filter. The samples were weighted to obtain an EOPMindex and were analyzed using Scanning Electron Microscopy. The EOP Mindex was not sensitive to the engine rpm and load. When the mixture is very rich (air equivalence ratio λ less than ∼ 0.7), the EOPM comprise mostly of soot particles from fuel combustion. In the lean to slightly rich region (0.8 < λ < 1.2), however, the EOPM are dominated by particles derived from the lubrication oil.

The presence of liquid fuel inside the engine cylinder is believed to be a strong contributor to the high levels of hydrocarbon emissions from spark ignition (SI) engines during the warm-up period. Quantifying and determining the fate of the liquid fuel that enters the cylinder is the first step in understanding the process of emissions formation. This work uses planar laser induced fluorescence (PLIF) to visualize the liquid fuel present in the cylinder. The fluorescing compounds in indolene, and mixtures of iso-octane with dopants of different boiling points (acetone and 3-pentanone) were used to trace the behavior of different volatility components. Images were taken of three different planes through the engine intersecting the intake valve region. A closed valve fuel injection strategy was used, as this is the strategy most commonly used in practice. Background subtraction and masking were both performed to reduce the effect of any spurious fluorescence.

Computer simulation of extravehicular activity (EVA) is increasingly being used in planning and training for EVA. A space suit model is an important, but often overlooked, component of an EVA simulation. Because of the inherent difficulties in collecting angle and torque data for space suit joints in realistic conditions, little data exists on the torques that a space suit’s wearer must provide in order to move in the space suit. A joint angle and torque database was compiled on the Extravehicular Maneuvering Unit (EMU), with a novel measurement technique that used both human test subjects and an instrumented robot. Using data collected in the experiment, a hysteresis modeling technique was used to predict EMU joint torques from joint angular positions. The hysteresis model was then applied to EVA operations by mapping out the reach and work envelopes for the EMU.

The challenge of developing a robust, real-time driver gaze classification system is that it has to handle difficult edge cases that arise in real-world driving conditions: extreme lighting variations, eyeglass reflections, sunglasses and other occlusions. We propose a single-camera end-toend framework for classifying driver gaze into a discrete set of regions. This framework includes data collection, semi-automated annotation, offline classifier training, and an online real-time image processing pipeline that classifies the gaze region of the driver. We evaluate an implementation of each component on various subsets of a large onroad dataset. The key insight of our work is that robust driver gaze classification in real-world conditions is best approached by leveraging the power of supervised learning to generalize over the edge cases present in large annotated on-road datasets.

The fuel injection process in the port of a firing 4-valve SI engine at part load and 25°C head temperature was observed by a high speed video camera. Fuel was injected when the valve was closed. The reverse blow-down flow when the intake valve opens has been identified as an important factor in the mixture preparation process because it not only alters the thermal environment of the intake port, but also strip-atomizes the liquid film at the vicinity of the intake valve and carries the droplets away from the engine. In a series of “fuel-on” experiments, the fuel injected in the current cycle was observed to influence the fuel delivery to the engine in the subsequent cycles.

A Lagrangian random vortex-boundary element method has been developed for the simulation of unsteady incompressible flow inside three-dimensional domains with time-dependent boundaries, similar to IC engines. The solution method is entirely grid-free in the fluid domain and eliminates the difficult task of volumetric meshing of the complex engine geometry. Furthermore, due to the Lagrangian evaluation of the convective processes, numerical viscosity is virtually removed; thus permitting the direct simulation of flow at high Reynolds numbers. In this paper, a brief description of the numerical methodology is given, followed by an example of induction flow in an off-centered port-cylinder assembly with a harmonically driven piston and a valve seat situated directly below the port. The predicted flow is shown to resemble the flow visualization results of a laboratory experiment, despite the crude approximation used to represent the geometry.

One of the goals of designing engineering systems is to maximize the system's reliability. A reliable system must satisfy its functional requirements without failure throughout its intended lifecycle. The typical means to achieve a desirable level of reliability is through preventive maintenance of a system; however, this involves cost. A more fundamental approach to the problem is to maximize the system's reliability by preventing failures from occurring. A key question is to find mechanisms (and the means to implement them into a system) that will prevent its system range from going out of the design range. Functional periodicity is a means to achieve this goal. Three examples are discussed to illustrate the concept. In the new electrical connector design, it is the geometric functional periodicity provided by the woven wire structure. In the case of integrated manufacturing systems, it is the periodicity in scheduling of the robot motion.

This paper presents an Axiomatic Design framework for manufacturing system design and illustrates how lean cellular manufacturing can achieve volume flexibility. Axiomatic Design creates a design framework by mapping the functional requirements of a system to specific design parameters. Volume flexibility is often neglected as a requirement of manufacturing systems. Very few industries are fortunate enough to experience stable or predictable product demand. In reality, demand is often volatile and uncertain. It is important that manufacturing system designers are aware of manufacturing system types which can accommodate volume flexibility and follow a structured design methodology that assures that all requirements are met by the system.

A design framework based on the principles of lean manufacturing and axiomatic design was used as a guideline for designing an automotive component manufacturing system. A brief overview of this design decomposition is given to review its structure and usefulness. Examples are examined to demonstrate how this design framework was applied to the design of a gear manufacturing system. These examples demonstrate the impact that low-level design decisions can have on high-level system objectives and the need for a systems-thinking approach in manufacturing system design. Results are presented to show the estimated performance improvements resulting from the new system design.

This session keynote paper presents a framework for designing and deploying production systems. The framework enables the communication and determination of objectives and design solutions from the highest level to the lowest level of a manufacturing enterprise. The design methodology ensures that the physical implementation, called Design Parameters (DPs), meets the objectives or Functional Requirements (FRs) of the production system design. This paper presents a revolutionary approach to determine the objectives and the implementation of a “lean” production system design for a manufacturing business as guided by the design axiom of independence.

The linked cell system gives both reduced cost and volume flexibility. The characteristics of the linked cell system are a consequence of decoupling the operators from the machines, using standard work in process between the cells and by integrating the information system with the cell and system design. By decoupling the operators from the machines the capacity can be increased/decreased in small increments by using more or fewer operators in the cell. The information system is integrated with the linked cell design by the use of a Heijunka box. The Heijunka is used to level production and to initiate the pace of production as a result of pulling withdrawal kanban at a standard time interval. This standard time interval is called the pitch of production. The kanban cards give information about what to produce, when to produce, when to make changeovers but they also give information to control the material replenishment.

Lean production has greatly influenced the way manufacturing systems should be designed. One important aspect of lean production is takt time. Takt time relates customer demand to available production time and is used to pace the production. This paper applies the manufacturing system design and deployment framework to describe the impact of takt time on both the design and the operation of a manufacturing system. The goal of this paper is to illustrate the relevant relationships of takt time to overall system design.

Optimal design of modern direct injection spark-ignition engines depends heavily on the characteristics and distribution of the fuel spray. This study was designed to compliment imaging experiments of changes in the spray structure due to fuel volatility and operating conditions. Use of phase-Doppler particle analysis (PDPA) allows quantitative point measurements of droplet diameter and velocity. In agreement with imaging experiments, the results show that the spray structure changes not only with ambient gas density, which is often measured, but also with fuel temperature and volatility. The mean droplet diameter was found to decrease substantially with increasing fuel temperature and decreasing ambient density. Under conditions of low potential for vaporization, the observed trends in mean droplet sizes agree with published correlations for pressure-swirl atomizers.

Optimal design of modern direct injection spark-ignition engines depends heavily on the characteristics and distribution of the fuel spray. This study was designed to investigate changes in the spray properties due to fuel volatility and operating conditions using a firing optically-accessible engine with planar laser-induced fluorescence (PLIF) imaging. The results show that the spray structure changes not only with ambient gas density, which is often measured, but also with fuel temperature and volatility. As ambient pressure decreases and fuel temperature increases, the volatile ends of multi-component fuels evaporate quickly, disrupting the spray structure and producing a vapor core along the axis of the spray. Beyond a certain point, evaporation is rapid enough to expand the initial cone angle of the spray while causing a decrease in the overall spray width.

Catalytic and non-catalytic engine aftertreatment components, such as the diesel oxidation catalyst (DOC), selective catalytic reduction on filter (SCRF), the gasoline particulate filter (GPF) and the diesel particulate filter (DPF) are complex, multifunctional emissions control technologies that are robustly designed for extended use in harsh automotive exhaust environments. Over the useful component lifetime, lubricant-derived inorganic and incombustible ash accumulates in and/or on the surface of the aforementioned aftertreatment components, resulting in degraded performance and other potential problems. In order to better understand effects of ash in such components, a multiscale analytical approach is necessary, requiring a variety of experimental tools.

Three-piece oil control rings (TPOCR) are widely used in the majority of modern gasoline engines and they are critical for lubricant regulation and friction reduction. Despite their omnipresence, the TPOCRs’ motion and sealing mechanisms are not well studied. With stricter emission standards, gasoline engines are required to maintain lower oil consumption limits, since particulate emissions are strongly correlated with lubricant oil emissions. This piqued our interest in building a numerical model coupling TPOCR dynamics and oil transport to explain the physical mechanisms. In this work, a 2D dynamics model of all three pieces of the ring is built as the main frame. Oil transport in different zones are coupled into the dynamics model. Specifically, two mass-conserved fluid sub-models predict the oil movement between rail liner interface and rail groove clearance to capture the potential oil leakage through TPOCR. The model is applied on a 2D laser induced fluorescence (2D-LIF) engine.