• 한국산학기술학회논문지
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• 제12권5호
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• pp.2054-2060
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• 2011

본 연구의 목적은 경제적이고, 친환경적인 가솔린 직접 분사 인젝터의 분무특성을 연구하는 것에 있다. 분무도달거리, 분무각 그리고 혼합기 형성과 같은 인젝터의 특성을 가시화 실험을 통하여 측정하였다. 특히 분무압력과 분위기압력이 분무 침투거리와 분무각에 미치는 영향을 분석하였다. 가시화 실험을 위하여 정적 연소실과 연료 공급장치를 제작하였다. 초고속 카메라와 LED광원을 이용하여 분무형상을 촬영하였고, 촬영된 영상으로 분무 특성을 분석하였다. 연소실내의 분위기압력이 감소하고, 연료의 분무압력이 증가할수록 도달거리는 증가하였다. 분위기압력과 분무압력에 대해 분무각의 변화는 미소하지만, 분위기압력이 분무각에 더 큰 영향을 미치고 있다.

• 한국자동차공학회논문집
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• 제20권2호
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• pp.116-122
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• 2012

The purpose of this study is to investigate the overall spray behavior characteristics for various injection conditions in a gasoline direct injection(GDI) injector with multi-hole. The spray characteristics, such as the spray penetration, the spray angle, and the injection quantity, were studied through the change of the injection pressure, the ambient pressure, and the energizing duration in a high-pressure chamber with a constant volume. The n-heptane with 99.5% purity was used as the test fuel. In a constant volume chamber, the injected spray was visualized by the spray visualization system, which consisted of the high-speed camera, the metal-halide lamp, the injector control device, and the image analysis system with the image processing program. It was revealed that the injection quantity was mainly affected by the difference between the injection pressure and the ambient pressure. For low injection pressure conditions, the injection quantity was decreased by the increase of the ambient pressure, while it nearly maintained regardless of the ambient pressure at high injection pressure. According to the increase of the ambient pressure in the constant volume chamber, the spray development became slow, consequently, the spray tip penetration decreased, and the spray area increased. In additions, the circular cone area decreased, and the vortex area increased.

• 한국분무공학회지
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• 제16권4호
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• pp.201-209
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• 2011

Optimum engine performance is obtained when the spray characteristics is well matched to the geometry of a combustion chamber. Among many parameters governing the combustion performance in internal combustion engine, fuel supply characteristics and atomization are important performance factors. Therefore, spray characteristics of high pressure multi-hole injector has been studied experimentally. An experimental test system has been made to operate high pressure injection system and to visualize spray behavior. Spray visualization has been performed to analyze spray formation, spray cone angle, bent angle and penetration length. Spray interaction with piston has been analyzed with various injector installation angle, injection pressure and ambient pressure. Test results show that penetration length is greatly influenced by the injection pressure. Penetration length is decreased as ambient pressure increased. Spray cone angle is increased as injection pressure and ambient pressure increased. However, bent angle is not influenced by the change of injection pressure and ambient pressure. Spray cone angle distribution map is plotted using the experimental data. Fuel movement around the spark-plug has been enforced as increasing injector installation angle.

• 한국분무공학회지
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• 제27권2호
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• pp.57-65
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• 2022

The purpose of this study is to use machine learning to build a model capable of predicting the flash boiling spray characteristics. In this study, the flash boiling spray was visualized using Shadowgraph visualization technology, and then the spray image was processed with MATLAB to obtain quantitative data of spray characteristics. The experimental conditions were used as input, and the spray characteristics were used as output to train the machine learning model. For the machine learning model, the XGB (extreme gradient boosting) algorithm was used. Finally, the performance of machine learning model was evaluated using R² and RMSE (root mean square error). In order to have enough data to train the machine learning model, this study used 12 injectors with different design parameters, and set various fuel temperatures and ambient pressures, resulting in about 12,000 data. By comparing the performance of the model with different amounts of training data, it was found that the number of training data must reach at least 7,000 before the model can show optimal performance. The model showed different prediction performances for different spray characteristics. Compared with the upstream spray angle and the downstream spray angle, the model had the best prediction performance for the spray tip penetration. In addition, the prediction performance of the model showed a relatively poor trend in the initial stage of injection and the final stage of injection. The model performance is expired to be further enhanced by optimizing the hyper-parameters input into the model.