• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.4
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• pp.40-47
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• 2010

This study deals with the investigation about the effect of the pilot and split injection strategies on the spray-atomization characteristics of biodiesel fuel derived from a soybean oil. Experimental results were compared with the calculation results obtained from the numerical analysis. Fuel properties of biodiesel according to the variation of the fuel temperature were inserted to the fuel library in the KIVA code. The amount of fuel injection is divided into equal mass for each split and main injection. In this work, the pilot injection strategy can be achieved by the amount of fuel injection shortly before the start of the main injection. A spray tip penetration, radial distance and spray area were measured for the analysis of macroscopic spray characteristics. In addition, the local and overall droplet size distribution were calculated by using KIVA-3V code to study the effect of split and pilot injection on the atomization performance under high ambient pressure. From these studies, the experimental results showed the multiple injection induced the decrease of the spray tip penetration due to the reduction and division of the spray momentum compared to single injection. In the atomization performance, the droplet size increased in the case of the multiple injection a little. Moreover, the SMD slightly increased as the fuel droplets goes through the axial direction. The spray behavior of numerical results were well predicted the experimental multiple spray characteristics of biodiesel fuel.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.399-406
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• 2009

In this study, Polynomial Radial Basis Function Neural Network(pRBFNN) based on Fuzzy Inference System is designed and its parameters such as learning rate, momentum coefficient, and distributed weight (width of RBF) are optimized by means of Particle Swarm Optimization. The proposed model can be expressed as three functional module that consists of condition part, conclusion part, and inference part in the viewpoint of fuzzy rule formed in 'If-then'. In the condition part of pRBFNN as a fuzzy rule, input space is partitioned by defining kernel functions (RBFs). Here, the structure of kernel functions, namely, RBF is generated from HCM clustering algorithm. We use Gaussian type and Inverse multiquadratic type as a RBF. Besides these types of RBF, Conic RBF is also proposed and used as a kernel function. Also, in order to reflect the characteristic of dataset when partitioning input space, we consider the width of RBF defined by standard deviation of dataset. In the conclusion part, the connection weights of pRBFNN are represented as a polynomial which is the extended structure of the general RBF neural network with constant as a connection weights. Finally, the output of model is decided by the fuzzy inference of the inference part of pRBFNN. In order to evaluate the proposed model, nonlinear function with 2 inputs, waster water dataset and gas furnace time series dataset are used and the results of pRBFNN are compared with some previous models. Approximation as well as generalization abilities are discussed with these results.

• Journal of the Korean Society of Propulsion Engineers
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• v.20 no.2
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• pp.39-45
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• 2016

In a coaxial porous injector, a gas propellant is injected through the porous cylinder surface to the liquid jet which is encircled by a porous cylinder. In this study, to observe the differences in disintegration mechanisms between a shear coaxial injector and a coaxial porous injector, cold-flow tests and 2-D axisymmetric numerical analysis have been carried out. The shadowgraph images and Sauter mean diameters were compared in similar experimental conditions, and the effects of velocity distributions at the inner injector region on the disintegration of liquid jet were investigated through the numerical calculations. As a result, in high air mass flow rate condition, the disintegration performance of coaxial porous injector is better than shear coaxial injector, in spite of a lower velocity at the inner injector region.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.970-976
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• 2007

In this study, Polynomial Network Pattern Classifier(PNC) based on Fuzzy Inference Mechanism is designed and its parameters such as learning rate, momentum coefficient and fuzzification coefficient are optimized by means of Particle Swarm Optimization. The proposed PNC employes a partition function created by Fuzzy C-means(FCM) clustering as an activation function in hidden layer and polynomials weights between hidden layer and output layer. Using polynomials weights can help to improve the characteristic of the linear classification of basic neural networks classifier. In the viewpoint of linguistic analysis, the proposed classifier is expressed as a collection of "If-then" fuzzy rules. Namely, architecture of networks is constructed by three functional modules that are condition part, conclusion part and inference part. The condition part relates to the partition function of input space using FCM clustering. In the conclusion part, a polynomial function caries out the presentation of a partitioned local space. Lastly, the output of networks is gotten by fuzzy inference in the inference part. The proposed PNC generates a nonlinear discernment function in the output space and has the better performance of pattern classification as a classifier, because of the characteristic of polynomial based fuzzy inference of PNC.

• Journal of Astronomy and Space Sciences
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• v.26 no.2
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• pp.141-156
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• 2009

Through the photometric observations of the near-contact binary, XZ CMi, new BV light curves were secured and seven times of minimum light were determined. An intensive period study with all published timings, including ours, confirms that the period of XZ CMi has varied in a cyclic period variation superposed on a secular period decrease over last 70 years. Assuming the cyclic change of period to occur by a light-time effect due to a third-body, the light-time orbit with a semi-amplitude of 0.0056d, a period of 29y and an eccentricity of 0.71 was calculated. The observed secular period decrease of $-5.26{\times}10^{-11}d/P$ was interpreted as a result of simultaneous occurrence of both a period decrease of $-8.20{\times}10^{-11}d/P$ by angular momentum loss (AML) due to a magnetic braking stellar wind and a period increase of $2.94{\times}10^{-11}d/P$ by a mass transfer from the less massive secondary to the primary components in the system. In this line the decreasing rate of period due to AML is about 3 times larger than the increasing one by a mass transfer in their absolute values. The latter implies a mass transfer of $\dot{M}_s=3.21{\times}10^{-8}M_{\odot}y^{-1}$ from the less massive secondary to the primary. The BV light curves with the latest Wilson-Devinney binary code were analyzed for two separate models of 8200K and 7000K as the photospheric temperature of the primary component. Both models confirm that XZ CMi is truly a near-contact binary with a less massive secondary completely filling Roche lobe and a primary inside the inner Roche lobe and there is a third-light corresponding to about 15-17% of the total system light. However, the third-light source can not be the same as the third-body suggested from the period study. At the present, however, we can not determine which one between two models is better fitted to the observations because of a negligible difference of $\sum(O-C)^2$ between them. The diversity of mass ratios, with which previous investigators were in disagreement, still remains to be one of unsolved problems in XZ CMi system. Spectroscopic observations for a radial velocity curve and high-resolution spectra as well as a high-precision photometry are needed to resolve some of remaining problems.