• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.5
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• pp.2054-2060
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• 2011

The purpose of this paper is to investigate spray characteristics of GDI injector that is economic and environment-friendly. Injector characteristics such as penetration length, spray angle and mixture formation were measured using experimental visualization technique. Especially, it has been analyzed that the influences of ambient pressure and injection pressure on penetration length and spray angle. To visualize the spray, a constant volume combustion chamber and fuel supply system have been manufactured. A high-speed camera and LED light source have been applied to obtain spray images. The experimental and visualization result shows that the penetration length is increased as decreasing ambient pressure and/or increasing injection pressure. Also, ambient pressure and injection pressure have minor effect on the spray angle variation.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.2
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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.

• Journal of ILASS-Korea
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• v.16 no.4
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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.

• Journal of ILASS-Korea
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• v.27 no.2
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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.