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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.
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.