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( Quangkhai Pham ),( Mengzhao Chang ),( Byungchul Choi ),( Suhan Park ) 한국분무공학회 2022 한국액체미립화학회지 Vol.27 No.1
In this study, an experimental investigation on the effects of the pressure ratio on the wall-impingement spray characteristics of nitrogen gas using a compressed natural gas (CNG) injector was conducted. The transient development of the impingement spray was recorded by a high speed camera with Z-type Schlieren visualization method. The spray behavior under various pressure ratio conditions were analyzed. The experimental results showed that the pressure ratio has positive effect on the development of spray wall-impingement. The effects of the above factor were evaluated in a constant volume chamber at atmospheric conditions. The data from test showed that, with the increase of the pressure ratio, the spray tip penetration (STP) quickly increases before the impingement and gradually increases after the impingement. Additionally, the spray velocity first increases and then sharply decreases on regardless of the injection pressure level. As the spray spreading angle increases, spray area and volume increases rapidly with the increase in STP at the beginning of injection, and finally entered a stable range, has a great correlation with the increase of pressure ratios.
Quangkhai Pham,Huijun Kim,최병철,박수한 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.9
The aim of this study is to investigate the influence of the change in the intake manifold geometry on the flow behavior and air-fuel mixing formation within a fuel injector port in a single-cylinder engine with a dual-fuel model engine. In an optimization study supported by the commercial computational code ANSYS-FLUENT, various injector angles and entry angles of an intake manifold are established to analyze their effects on the mixing behavior that causes mixture formation. The injector angles (α) are being changed from 20° to 30° to 40° for an intake manifold with entry angle (β) levels of 20°, 25°, 30°, 35°. The total pressure recovery, pressure loss, mean of velocity distribution, turbulent kinetic energy, helicity, and mass fraction of methane (CH 4 ) are analyzed to evaluate the performance of flow behavior for mixing enhancement. Based on the validated results, the numerical results provide a highly accurate solution for geometry problems.
머신러닝을 이용한 다공형 GDI 인젝터의 플래시 보일링 분무 예측 모델 개발
상몽소,신달호,Quangkhai Pham,박수한 한국분무공학회 2022 한국액체미립화학회지 Vol.27 No.2
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.