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The increasingly stringent regulations for fuel economy and emissions require better optimization and control of oil consumption. One of the primary mechanisms of oil consumption is vaporization from the liner; we consider this as the “minimum oil consumption (MOC).” This paper presents a physical-mathematical cycle model for predicting the MOC. The numerical simulations suggest that the MOC is markedly sensitive to oil volatility, liner temperature, engine load and speed but less sensitive to oil film thickness. A one-line correlation is proposed for quick MOC estimations. It is shown to have <15% error compared to the cycle MOC computation. In the “dry region” (between top ring and OCR at the TDC), oil is depleted due to high heat and continual exposure to the combustion chamber.

In internal combustion engines, a portion of liquid fuel spray may directly land on the liner and mix with oil (lubricant), forming a fuel-oil film (~10μm) that is much thicker than the original oil film (~0.1μm). When the piston retracts in the compression stroke, the fuel-oil mixture may have not been fully vaporized and can be scraped by the top ring into the 1st land crevice and eventually enter the combustion chamber in the format of droplets. Studies have shown that this mechanism is possibly a leading cause for low-speed pre-ignition (LSPI) as the droplets contain oil that has a much lower self-ignition temperature than pure fuel. In this interest, this work aims to study the oil-fuel interactions on the liner during an engine cycle, addressing molecular diffusion (in the liquid film) and vaporization (at the liquid-gas interface) to quantify the amount of fuel and oil that are subject to scraping by the top ring, thereby exploring their implications on LSPI and friction.

This paper presents a programmable E/H control valve consisting of five individually proportional flow control valves. With a hybrid control algorithm, this valve has programmable valve characteristics, such as adjustable valve deadband and flow control gain, and programmable valve functions, such as different center functions. System analyses and experimental evaluations indicate that this programmable valve is capable of replacing conventional E/H control valves in practical applications.

The use of substances other than petroleum based fuels for power sources is not a new concept. Prior to the advent of petroleum fueled vehicles numerous other substances were used to create mobile sources of power. As the world's petroleum supply dwindles, alternative fuel sources are sought after to replace petroleum fuels. Many industries are particularly interested in the development of renewable fuel sources, or biologically derived fuel sources, which includes ethanol. The use of No. 2 diesel as well as many alternative fuels in compression ignition engines result in injector coking. Injector coking can severely limit engine performance by limiting the amount of fuel delivered to the combustion chamber and altering the spray pattern. Injector tip coking is also one of the most sensitive measures of diesel fuel quality [1]. A machine vision system was implemented to quantify injector coking accumulation when a compression ignition engine was fueled with oxydiesel.

Machine operators rely on intuition and experience to evaluate vehicle performance. As we increasingly turn to automation, it is important to automatically evaluate sensor data and system performance. Fuzzy logic allows us to take advantage of domain knowledge to evaluate data and to describe a system linguistically. In this paper, two automated fuzzy evaluation systems are described. In the first, a fuzzy quality module evaluates output from a simulated noisy sensor. In the second system, a fuzzy quality module evaluates the output from a machine vision system. Results from both systems indicate that fuzzy logic was able to accurately categorize the output in support of machinery decision making for automated control.

This paper presents system identification methods of using both frequency and time domain analyses to estimate system parameters for a nonlinear electrohydraulic (E/H) tractor steering system. The frequency domain method identified parameters of the system using a linear model with a nonlinear gain function. The time domain method identified those parameters using a discrete time expression. A method of multiple models was used to represent the nonlinear system for both frequency and time domain analyses. Simulation and test results showed that the modified identification method could satisfactorily identify the parameters of the nonlinear E/H tractor steering system.

The development of automated guidance for agricultural tractors has addressed several basic and applied issues of agricultural equipment automation. Basic analyses have included the dynamics of steering systems and posture sensors for guidance. Applied issues have evaluated the potential of several commercial sensing systems and a commercial mechanical guidance system. A research platform has been developed based on a Case 7220 Magnum1 2-wheel drive agricultural tractor. An electrohydraulic steering system was used and characterized in support of automated guidance control. Posture sensing methods were developed using GPS, geomagnetic direction sensors (GDS), inertial, and machine vision sensing systems. Sensor fusion of GPS-inertial-machine vision and GPS-GDS-machine vision provided the most flexible and accurate guidance and capable for operation under dynamically changing field conditions.