Journal
FUEL
Volume 300, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.fuel.2021.120997
Keywords
Coal-based naphtha; HCCI combustion characteristics; Knock recognition; Feature extraction; Classification
Categories
Funding
- National Natural Science Foundation of China [51806020]
- Innovation Capability Support Program of Shaanxi [2021TD28]
- Special Fund for Basic Scientific Research of Central Colleges, Chang'an University [300102221512]
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This study investigates the combustion characteristics and knock recognition of HCCI engines fueled with coal-based naphtha by analyzing in-cylinder pressure. The results show significant differences in in-cylinder pressure and temperature between normal combustion and knock combustion.
The purpose of this study is to investigate combustion characteristics and knock recognition of HCCI engines fueled with coal-based naphtha by analyzing in-cylinder pressure. The in-cylinder pressures of knock and normal combustion at two different intake temperatures (Tin) of 60 degrees C and 90 degrees C were collected on a modified diesel engine, characteristic vector was constructed from the in-cylinder pressure signal based on empirical mode decomposition (EMD) and sample entropy (SampEn), and a support vector machine (SVM) classifier was used to recognize knock. The results show that in-cylinder pressure, heat release rate (HRR), in-cylinder temperature, pressure rise rate (PRR) and pressure rise acceleration (PRA) change steadily in normal combustion. With occurrence of knock, they have a sharp rise and huge fluctuations. HRRmax, PRRmax, and PRAmax are greatly increased, and the corresponding phase is significantly advanced, especially in high-temperature stage. When Tin is 90 degrees C, PRAmax reaches 1.174 MPa/degrees CA2, which is an increase of 854.47% compared to normal combustion. However, the coefficient of variation for peak pressure (COVPmax) of two Tins show completely opposite results. At Tin of 60 degrees C, COVPmax increases from 3.95% to 4.71%, while at Tin of 90 degrees C, COVPmax reduces from 3.08% to 1.52%. Moreover, knock recognition achieves the best classification result at Tin of 60 degrees C. The average classification accuracy reaches 91.35%, and the best classification accuracy reaches 99.67%. This shows that feature extraction method based on EMD and SampEn has excellent classification performance for knock recognition of coal-based naphtha HCCI engines.
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