本文基于某高压共轨发动机，利用AVL-Fire软件建立缸内燃烧模型，在发动机75%负荷工况掺烧5%二代生物柴油情况下开展燃油喷射控制参数仿真优化。为提升发动机掺烧第二代生物柴油后的的综合性能，通过燃油喷射控制参数正交试验设计，利用仿真计算结果，建立基于燃油喷射控制参数的NOx质量分数和$\bar bi$为优化目标的回归模型。利用所拟合的回归方程在多目标粒子群算法中优化，获得帕累托最优边界。根据帕累托前端中NOx排放与$ {\bar b}i $关系，利用TOPSIS方法综合分析，确定发动机掺烧二代生物柴油优化后的喷射控制参数（HI5-优化方案）为：预喷正时28.87°CABTDC、预喷油量2.37 mg、主喷正时7.59°CABTDC、后喷油量1 mg。采用HI5-优化方案相较于HI5方案，燃油消耗率降低3.04 g/kWh，且NOx排放可满足Tier II排放限值要求。
Using AVL-Fire software, a combustion model was established for an in-cylinder combustion engine based on high-pressure common rail technology. The fuel injection control parameters were simulated and optimized for burning 5% second-generation biodiesel at 75% engine load. To enhance the overall performance of engines blended with second-generation biodiesel, we established a regression model based on the NOx mass fraction of the fuel injection control parameters, with $\bar bi$ as the optimization objective. We used orthogonal test design of the fuel injection control parameters and the results of simulation calculations. The fitted regression equation is optimized in a multi-objective particle swarm algorithm using the fitted regression equation to obtain a Pareto optimal boundary. Based on the relationship between NOx emission and $\bar bi$in the Pareto front-end, the optimized injection control parameters of the engine blended with second-generation biodiesel (HI5-optimized) were determined using the TOPSIS method of comprehensive analysis as follows: pre-injection timing 28.87°CABTDC, pre-injection fuel quantity 2.37 mg, main injection timing 7.59°CABTDC, post The engine consumes 3.04 g/kWh less fuel than the HI5-optimised solution and meets the Tier II emission limits for NOx, resulting in improved overall performance.
2024,46(21): 87-92 收稿日期：2023-12-26
DOI：10.3404/j.issn.1672-7649.2024.21.015
分类号：U664.1
基金项目：福建省自然科学基金资助项目（2021J01846，2022J01807）
作者简介：王波(1995-),男,硕士研究生,研究方向为轮机工程
参考文献：
[1] 贺林林, 焦钰褀, 贾瑞, 等. 绿色港口建设中港区大气污染物排放研究综述[J]. 重庆交通大学学报(自然科学版), 2021, 40(8): 78-87.
HE Linlin, JIAO Yuqi, JIA Rui, et al. Riview on the research status of air pollutant emission in port area in the development of green port[J]. Journal of Chongqing Jiaotong University (Natural Science), 2021, 40(8): 78-87.
[2] 张爽. IMO 2023年船舶温室气体减排战略全面引领新未来[J]. 中国船检, 2023(8): 11-15.
[3] 蔡跃胜, 杨春华, 李岩. 现有船能效指数和营运碳强度指标对船舶的影响[J]. 船海工程, 2022, 51(4): 25-28+33.
BAI Juli, LI Xiaorong, WANG Jian. Manufacturing and installation technology for complex inlet duct of water-jet propulsion[J]. Ship and Ocean Engineerin, 2022, 51(4): 25-28+33.
[4] 唐铭沂, 毛进, 杨捷波, 等. 掺烧生物柴油结合相继增压技术对柴油机性能影响试验研究[J]. 舰船科学技术, 2023, 45(2): 129-134.
TANG Mingyi, MAO Jin, YANG Jiebo, et al. Experimental study on the effect of blended biodiesel combined with successive pressurization on the performance of diesel engine[J]. Ship Science and Technology, 2023, 45(2): 129-134.
[5] 高占斌, 杨捷波, 蔡浩, 等. 掺烧生物柴油结合米勒循环对船用柴油机燃烧及排放性能的影响[J]. 船舶工程, 2022, 44(6): 65-70+133.
GAO Zhanbin, YANG Jiebo, CAI Hao, et al. Effects of miller cycle on combustion and emission performance of dual fuel engines[J]. Ship and Ocean Engineering, 2022, 44(6): 65-70+133.
[6] 于世永, 李晓飞. 不同喷油策略对燃生物柴油船用柴油机燃烧和排放的影响[J]. 舰船科学技术, 2020, 42(15): 145-151.
YU Shiyong, LI Xiaofei. Effect of different injection strategies on combustion and emission characteristics of a marine diesel engine fueled with biodiesel[J]. Ship Science and Technology, 2020, 42(15): 145-151.
[7] 曹礼轩, 王谦, 钟汶君, 等. 冷态下喷油策略对双燃料发动机燃烧特性影响的可视化研究[J]. 重庆理工大学学报(自然科学), 2023, 37(3): 138-146.
CAO Lixuan, WANG Qian, ZHONG Wenjun, et al. Visual study on the effect of fuel injection strategies on combustion characteristics of dual-fuel engines under cold conditions[J]. Journal of Chongqing University of Technology (Natural Science, 2023, 37(3): 138-146.
[8] 焦昱翔, 赵庆军, 任三群, 等. 基于NSGA-Ⅱ算法的ATR发动机PI控制器多目标优化方法研究[J]. 推进技术, 2024, 45(2): 188-196.
JIAO Yuxiang, ZHAO Qingjun, REN Sanqun, et al. Multi-objective optimization method of ATR engine PI controller based on NSGA-Ⅱ algorithm[J]. Journal of Propulsion Technology, 2024, 45(2): 188-196.
[9] SAKTHIVEL G, SENTHIL Kumar S, ILANGKUMARAN M. A genetic algorithm-based artificial neural network model with TOPSIS approach to optimize the engine performance[J]. Biofuels, 2019, 10(6): 693-717.
