Analysis of controlled auto-ignition /HCCI combustion in a direct injection gasoline engine with single and split fuel injections

A multi-cycle three-dimensional CFD engine simulation programme has been developed and applied to analyze the Controlled autoignition (CAI) combustion, also known as homogeneous charge compression ignition (HCCI), in a direct injection gasoline engine. CAI operation was achieved through the negative valve overlap method by means of a set of low lift camshafts. In the first part of the paper, the effect of single injection timing on combustion phasing and underlying physical and chemical processes involved was examined through a series of analytical studies using the multi-cycle 3D engine simulation programme. The analyses showed that early injection into the trapped burned gases of a lean-burn mixture during the negative valve overlap period had a large effect on combustion phasing, due to localized heat release and the production of chemically reactive species. As the injection was retarded to the intake stroke, the charge cooling effect tended to slow down the autoignition process. However, further retard of fuel injection to the compression stroke caused the earlier start of main combustion as fuel stratification was produced in the cylinder. In order to optimize the engine performance and engine-out emissions, double injection was investigated by injecting part of the fuel first in the negative valve overlap period and the rest of fuel during the intake or compression strokes. By varying the fueling of each injection, the best engine performance was obtained with the 50/50 fuel injection split ratio, while the lowest total NOx and soot emissions were seen with the optimal split injection ratio of 10/90

Concentration for unknown atomic entangled states via cavity decay

We present a physical scheme for entanglement concentration of unknown atomic entangled states via cavity decay. In the scheme, the atomic state is used as stationary qubit and photonic state as flying qubit, and a close maximally entangled state can be obtained from pairs of partially entangled states probabilistically.Comment: Three pages, Two figure

A novel automated approach of multi-modality retinal image registration and fusion

Biomedical image registration and fusion are usually scene dependent, and require intensive computational effort. A novel automated approach of feature-based control point detection and area-based registration and fusion of retinal images has been successfully designed and developed. The new algorithm, which is reliable and time-efficient, has an automatic adaptation from frame to frame with few tunable threshold parameters. The reference and the to-be-registered images are from two different modalities, i.e. angiogram grayscale images and fundus color images. The relative study of retinal images enhances the information on the fundus image by superimposing information contained in the angiogram image. Through the thesis research, two new contributions have been made to the biomedical image registration and fusion area. The first contribution is the automatic control point detection at the global direction change pixels using adaptive exploratory algorithm. Shape similarity criteria are employed to match the control points. The second contribution is the heuristic optimization algorithm that maximizes Mutual-Pixel-Count (MPC) objective function. The initially selected control points are adjusted during the optimization at the sub-pixel level. A global maxima equivalent result is achieved by calculating MPC local maxima with an efficient computation cost. The iteration stops either when MPC reaches the maximum value, or when the maximum allowable loop count is reached. To our knowledge, it is the first time that the MPC concept has been introduced into biomedical image fusion area as the measurement criteria for fusion accuracy. The fusion image is generated based on the current control point coordinates when the iteration stops. The comparative study of the presented automatic registration and fusion scheme against Centerline Control Point Detection Algorithm, Genetic Algorithm, RMSE objective function, and other existing data fusion approaches has shown the advantage of the new approach in terms of accuracy, efficiency, and novelty