کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1732689 | 1521481 | 2014 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Parametric study and optimization of a RCCI (reactivity controlled compression ignition) engine fueled with methanol and diesel
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی (عمومی)
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چکیده انگلیسی
By integrating an updated multi-dimensional model and the NSGA-II (non-dominated sorting genetic algorithm II), the combustion of a RCCI (reactivity controlled compression ignition) engine fueled with methanol/diesel was optimized. Based on the optimization results, parametric study was performed by varying energy fraction of methanol, EGR (exhaust gas recirculation rate, initial in-cylinder pressure at IVC (intake valve closing), initial in-cylinder temperature at IVC, and SOI (start of injection). Furthermore, the sensitivities of these five parameters on fuel efficiency, emissions, ignition timing and RI (ringing intensity) were analyzed. The results indicated that initial temperature and EGR rate exhibited the most significant effect on engine performance and emissions for their obvious effect on combustion temperature. By varying the local fuel-rich and high-temperature regions, methanol fraction and SOI could dramatically affect NOx (nitrogen oxide) emission. Overall, the RCCI combustion with high methanol fraction and advanced SOI exhibited higher fuel efficiency and lower emissions. Moreover, it was found that both decreasing EGR rate and increasing initial temperature led to the monotonously increased RI. While decreasing methanol fraction and increasing initial pressure demonstrated the negligible effect on RI at CA50 earlier than 4.3 °CA ATDC, which was contributed to their obvious effect on fuel spatial distributions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy - Volume 65, 1 February 2014, Pages 319-332
Journal: Energy - Volume 65, 1 February 2014, Pages 319-332
نویسندگان
Yaopeng Li, Ming Jia, Yachao Chang, Yaodong Liu, Maozhao Xie, Tianyou Wang, Lei Zhou,