Study of Achievement Both Noise and Vibration and Thermal Efficiency by Using the Excitation Force of a Piston System

Hironao Sato･Masahiro Oba･Toshiyuki Sonobe･Noriaki Sekine (SUBARU)･Yasuo Moriyoshi･Koji Morikawa･Tatsuya Kuboyama (Chiba University)

Rapid combustion is being attempted as a means of improving thermal efficiency of internal combustion engines, but rapid combustion deteriorates the vehicle interior noise level. This study focused on the piston system as a phenomenon of noise and vibration caused by combustion. The results of a study to achieve both noise and vibration and thermal efficiency at various engine speed and load by using the excitation force indicators of the piston system are reported.

High-Speed Imaging and Numerical Analysis of Spark Discharge for Combustion Prediction in Spark-Ignition Engines

Ryo Muto (SUBARU)･Tatsuya Kuboyama･Yasuo Moriyoshi (Chiba University)

Accurate analysis of spark discharge behavior, which influences initial flame formation, is essential for improving the prediction accuracy of spark-ignition engine combustion. In this study, high-speed imaging of the spark discharge process was conducted using a fan-equipped constant-volume chamber to investigate the effects of ambient pressure and flow velocity. Furthermore, the method for setting model constants based on experimental data was examined, and the validity of numerical analysis was evaluated.

Effects of RON on Interaction between Main Combustion and Unburned-Zone Autoignition Reactions

Taisei Shimizu･yuki Imagawa･shota Okuyama･Kazunari Kuwahara (Osaka Institute of Technology)

When intense knock occurs in spark-ignition engines, the main combustion decelerates immediately before unburned-zone autoignition. It has been considered that the deceleration is caused by the interaction between flame propagation and intermediate products of unburned-zone autoignition reactions as formaldehyde. In the present study, knock-limit engine operation is carried out using different fuels with the RON's of 85 to 120, and autoignition process is computed for the fuels using a detailed reaction mechanism. The essential of the interaction and the effect of the deceleration on the relationship between combustion phase and knock intensity are discussed.

Proposal and feasibility study of new combustion analysis method based on heat release rate prediction using machine learning

Shojun Rachi･Kaname Naganuma (Kanazawa Institute of Technology)

The original combustion analysis concept previously proposed by the authors is reconstructed using recent machine learning techniques. Machine learning–based algorithms are applied to optimize the Wiebe function coefficients to fit the experimentally derived heat release rate, enabling the prediction of in-cylinder pressure profiles and demonstrating the potential for highly accurate engine performance prediction.