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RISS 인기검색어
- 검색키워드
- 저자 : Kusaka Jin (검색결과 3 건)
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A Study on LPG Spray and Combustion Characteristics
이성욱(Lee Seang-wock),Nagashima Naoki,Kusaka Jin,Daisho Yasuhiro 한국자동차공학회 2002 한국자동차공학회 춘 추계 학술대회 논문집 Vol.2002 No.5_1
It has been recognized that alternative fuels such as Liquid Petroleum Gas (LPG) less polluting combustion characteristics than diesel fuel. Furthermore, engine performance is expected to be nearly equal to that of the diesel engine both in thermal efficiency and power output. However the properties of LPG are quite different from those of diesel fuel. Understanding the characteristics of LPG spray and evaporating processes are very important for developing efficient and low emission LPG engines optimized in fuel injection control and combustion processes. In this study, the LPG spray characteristics and evaporating processes were investigated using the Schlieren optical system and single-hole injectors in a constant volume chamber at room temperature. Also the characteristics of combustion were investigated using simultaneous visualization of OH radical and flames. The results show that the mixture moves along the impingement wall that reproduced the piston bowl and reaches in ignition spark plug. Also, OH fluorescence rises sharply and then decreases gradually when the combustion is carried out actively.
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MACHINE LEARNING APPLICATION TO PREDICT COMBUSTION PHASE OF A DIRECT INJECTION SPARK IGNITION ENGINE
Rio Asakawa,Keisuke Yokota,Iku Tanabe,Kyohei Yamaguchi,Ratnak Sok,Hiroyuki Ishii,Jin Kusaka 한국자동차공학회 2022 International journal of automotive technology Vol.23 No.1
Lean-diluted combustion can enhance thermal efficiency and reduce exhaust gas emissions from spark-ignited (SI) gasoline engines. However, excessive lean mixture with external dilution leads to combustion instability due to high cycle-to-cycle variations (CCV). The CCV should be controlled as low as possible to achieve stable combustion, high engine performance, and low emissions. Therefore, a stable combustion control function is required to predict the combustion phase with a low calculation load. A machine learning-based function is developed in this work to predict the 50 % mass fraction burn location (MFB50). Input parameters to the machine learning model consist of 1-, 2-, 3-, and 4-cycle from a three-cylinder production-based gasoline engine operated under stoichiometric to the lean-burn mixture. The results show that the MFB50 prediction model achieves high accuracy when 2-cycle data are used relative to 1-cycle data, which implies that the previous cycle data affects the predicted MFB50 of the next cycle. As a result, the neural network model can predict the cyclic MFB50 error within ± 3 oCA CCV and ± 5 oCA CCV with 70 % and 90 % accuracy, respectively. However, an increasing number of cycle data worsens the prediction accuracy due to model over-learning.
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Kei Yoshimura,Hajime Yahata,Waseda University,Kyohei Yamaguchi,Ratnak Sok,Jin Kusaka,Satoshi Tokuhara 한국자동차공학회 2022 International journal of automotive technology Vol.23 No.1
The purpose of this study was to develop a one-dimensional (1-D) model for predicting the amount of unburned hydrocarbons (UHC) due to flame extinction by quenching close to the combustion chamber wall in a gasoline engine. The local Reynolds number was used to predict the thickness of the thermal boundary layer developed by in-cylinder flow caused by high speed charge passing through the intake valves. The effect of different intake port geometries, including moderate- and high-tumble types, on the thickness of the thermal boundary layer was examined. The flame extinction model was integrated into a 1-D gasoline engine model. The amount of UHC predicted by the model was compared with experimental results by using a single-cylinder gasoline engine under various engine operating conditions. The numerical values were found to be in reasonable agreement with the measured data. A methodology for controlling UHC was also proposed in the final section.
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