期刊
ENERGY
卷 250, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2022.123717
关键词
Aviation compression ignition engine; Synthetic FT fuel; RP-3 kerosene; Pentanol; Combustion and emissions; RSM prediction
资金
- China Scholarship Council [201905064]
- National Natural Science Foundation of China [51922019, 51920105009]
- National Engineering Laboratory for Mobile Source Emission Control Technology [NELMS2018A02]
- Open Foundation of Beijing Key Laboratory of Occupational Safety and Health
This study analyzed the combustion and emission characteristics of an aviation compression ignition engine burning different fuels and found that blending pentanol with FT fuel can significantly reduce nitrogen oxides and particulate matter emissions while increasing thermal efficiency.
General aviation aircraft driven by aviation piston engines (APE) have gained a broad range of applica-tions. Aviation fuels blended with long-chain alcohols is a promising means for APE to mitigate its de-pendency on fossil fuel. Herein, the combustion and emission characteristics of an aviation compression ignition engine burning a baseline diesel, the RP-3 kerosene, and a synthetic Fischer-Tropsch (FT) fuel were analyzed. The engine tests were carried out under different conditions via varying pentanol ad-ditive ratio (PAR), fuel injection timing and engine load variables. The Response Surface Method (RSM) was utilized to quantify the effectiveness of independent variables on the target responses of indicated thermal efficiency (ITE), nitrogen oxides (NOx) and particulate matter (PM) emissions. Compared to the baseline diesel, burning the pentanol-FT blends (40% PAR) significantly reduces NOx by 81% and PM by 75% with a prominent increase of ITE by 7.2%. Based on the analysis of variance, the RSM-derived model demonstrated that the fuel type predominantly determines ITE and NOx, while PAR primarily alters PM emissions. The binary effects of independent variables on the target responses were further resolved quantitatively. Moreover, the RSM was well validated to implement effective prediction on the engine performance/emission characteristics.(c) 2022 Elsevier Ltd. All rights reserved.
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