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A model has been developed for estimating the oil vaporization rate from the cylinder liner of a reciprocating engine. The model uses input from an external cycle simulator and an external liner oil film thickness model. It allows for the change in oil composition and the change in oil film thickness due to vaporization. It also estimates how the passage of the compression and scraper rings combine with the vaporization to influence the steady-state composition of the oil layer in the upper ring pack. Computer model results are presented for a compression-ignition engine using a range of liner temperatures, several engine speeds, and two different oils. Vaporization is found to be highly dependent on liner temperature and steady-state oil composition. The steady-state oil composition near the top of the cylinder is found to be significantly different than the composition of the oil near the bottom of the cylinder.

Advances in avionics and display technology are significantly changing the cockpit environment in current transport aircraft. The MIT Aeronautical Systems Lab (ASL) has developed a part-task flight simulator specifically to study the effects of these new technologies on flight crew situational awareness and performance. The simulator is based on a commercially-available graphics workstation, and can be rapidly reconfigured to meet the varying demands of experimental studies. The simulator has been successfully used to evaluate graphical microburst alerting displays, electronic instrument approach plates, terrain awareness and alerting displays, and ATC routing amendment delivery through digital datalinks.

Diesel engines are far more efficient than gasoline engines of comparable size, and emit less greenhouse gases that have been implicated in global warming. In 2000, the US EPA proposed very stringent emissions standards to be introduced in 2007 along with low sulfur (< 15 ppm) diesel fuel. The California Air Resource Board (CARB) has also established the principle that future diesel fueled vehicles should meet the same low emissions standards as gasoline fueled vehicles and the EPA followed suit with its Tier II emissions regulation. Achieving such low emissions cannot be done through engine development and fuel reformulation alone, and requires application of NOx and particulate matter (PM) aftertreatment control devices. There is a widespread consensus that NOx adsorbers and particulate filter are required in order for diesel engines to meet the 2007 emissions regulations for NOx and PM. In this paper, the key exhaust characteristics from an advanced diesel engine are reviewed.

A quasi-dimensional model for a direct injection diesel engine was developed based on experiments at Sandia National Laboratory. The Sandia researchers obtained images describing diesel spray evolution, spray mixing, premixed combustion, mixing controlled combustion, soot formation, and NOx formation. Dec [1] combined all of the available images to develop a conceptual diesel combustion model to describe diesel combustion from the start of injection up to the quasi-steady form of the jet. The end of injection behavior was left undescribed in this conceptual model because no clear image was available due to the chaotic behavior of diesel combustion. A conceptual end-of-injection diesel combustion behavior model was developed to capture diesel combustion throughout its life span. The compression, expansion, and gas exchange stages are modeled via zero-dimensional single zone calculations.

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.

A method for detecting drivers' intentions is essential to facilitate operating mode transitions between driver and driver assistance systems. We propose a driver behavior recognition method using Hidden Markov Models (HMMs) to characterize and detect driving maneuvers and place it in the framework of a cognitive model of human behavior. HMM-based steering behavior models for emergency and normal lane changes as well as for lane keeping were developed using a moving base driving simulator. Analysis of these models after training and recognition tests showed that driver behavior modeling and recognition of different types of lane changes is possible using HMMs.

Comparisons have been made between dual-fuel (80% port-injection gasoline and 20% direct-injection diesel by mass) Highly Premixed Charge Combustion (HPCC) and blended-fuel (80% gasoline and 20% diesel) Low Temperature Combustion (LTC) modes on a 1-L single-cylinder test engine. In the HPCC mode, both early-injection (E-HPCC) and late-injection (L-HPCC) of diesel have been used. The comparisons have been conducted with a fixed fuel injection rate of 50 mg/cycle at 1500 rpm, and with the combustion phasing fixed (by adjusting the injection timing) so that the 50% heat release point (CA50) is at 8° ATDC. The rapid heat release process of LTC leads to the highest maximum pressure rise rate (MPRR). A two-peak heat release process is observed in L-HPCC, resulting in a lower MPRR. The heat release rate and MPRR values for the E-HPCC are comparable to the L-HPCC values. The EHPCC mode provides the lowest NOX emission. The soot emissions for all three modes are low.

This paper quantifies the potential of electric propulsion systems to reduce petroleum use and greenhouse gas (GHG) emissions in the 2030 U.S. light-duty vehicle fleet. The propulsion systems under consideration include gasoline hybrid-electric vehicles (HEVs), plug-in hybrid vehicles (PHEVs), fuel-cell hybrid vehicles (FCVs), and battery-electric vehicles (BEVs). The performance and cost of key enabling technologies were extrapolated over a 25-30 year time horizon. These results were integrated with software simulations to model vehicle performance and tank-to-wheel energy consumption. Well-to-wheel energy and GHG emissions of future vehicle technologies were estimated by integrating the vehicle technology evaluation with assessments of different fuel pathways. The results show that, if vehicle size and performance remain constant at present-day levels, these electric propulsion systems can reduce or eliminate the transport sector's reliance on petroleum.