| 柴油机SCR催化器载体结构参数优化 |
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| 引用本文： | 龚金科,张福杰,鄂加强,刘恒语,杜佳,陈韬,江俊豪.柴油机SCR催化器载体结构参数优化[J].环境工程学报,2012,6(12):4573-4578. |
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| 作者姓名： | 龚金科 张福杰 鄂加强 刘恒语 杜佳 陈韬 江俊豪 |
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| 作者单位： | 湖南大学汽车车身先进设计制造国家重点实验室,长沙,410082 |
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| 基金项目： | 国家自然科学基金资助项目(51276056,51176045);湖南省自然科学基金资助项目(10JJ6080);湖南大学汽车车身先进设计制造国家重点实验室自主课题(61075002) |
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| 摘 要： | 提出一种柴油机选择催化还原（SCR）催化器载体结构参数优化的设计方法，根据车用柴油机排量，将SCR催化器载体分为4类，选取载体体积、长度、目数、壁厚和涂层厚5个结构参数为设计变量，以高NO。转化率及低压力损失为优化目标，利用拉丁超立方实验设计选择样本点进行数值模拟，在构建的Kriging近似模型基础上，对载体结构参数采用改进的非支配排序遗传算法NSGA—II（non—dominatedsortinggeneticalgorithm—II）进行优化设计。结果表明：Kriging近似模型的拟合精度较高，结合NSGA—II算法对SCR催化器载体结构参数进行优化是可行的、有效的，不同排量下的优化结果均能够较好地满足设计要求。
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| 关 键 词： | 柴油机SCR 催化器载体 结构参数优化 Kriging模型 NSGA—II算法 |
| 修稿时间： | 2/9/2012 12:00:00 AM |
Optimization on structural parameters of diesel SCR catalytic reactor |
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| Gong Jinke,Zhang Fujie,E Jiaqiang,Liu Hengyu,Du Ji,Chen Tao and Jiang Junhao.Optimization on structural parameters of diesel SCR catalytic reactor[J].Techniques and Equipment for Environmental Pollution Control,2012,6(12):4573-4578. |
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| Authors: | Gong Jinke Zhang Fujie E Jiaqiang Liu Hengyu Du Ji Chen Tao and Jiang Junhao |
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| Institution: | State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,Hunan University,Changsha 410082, China;State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,Hunan University,Changsha 410082, China;State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,Hunan University,Changsha 410082, China;State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,Hunan University,Changsha 410082, China;State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,Hunan University,Changsha 410082, China;State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,Hunan University,Changsha 410082, China;State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,Hunan University,Changsha 410082, China |
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| Abstract: | |
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| Keywords: | diesel selective catalytic reduction catalytic reactor optimization of structural parameters Kriging model NSGA-Ⅱ algorithm |
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