Modeling the thermal and soot oxidation dynamics inside a ceria-coated gasoline particulate filter
Modeling the thermal and soot oxidation dynamics inside a ceria-coated gasoline particulate filter
复制标题
DOI：
10.1016/j.conengprac.2019.104199
复制
发表时间：
2020-01-01
影响因子：
4.9
通讯作者：
Onori, Simona
中科院分区：
文献类型：
--
作者：
Arunachalam, Harikesh;Pozzato, Gabriele;Onori, Simona
汽油微粒过滤器（GPFs）是一种实用的装置，可以减少汽油直燃发动机车辆的颗粒物排放。本文提出了一种新开发的面向控制的模型来表征铈涂层GPF中的热氧化动力学和烟尘氧化动力学。该模型利用GPF入口废气温度、废气质量流量、GPF初始烟尘负荷密度和空燃比来预测再生过程中GPF内部温度和烟尘氧化量。模型中包含的反应动力学包括氧和铈引发的烟灰氧化反应的速率。根据涂层GPF的几何信息计算体积模型参数，而空气燃料比用于确定废气成分的体积分数。废气特性利用体积分数和热力学表进行评估，而堇青石比热容则利用干净的实验数据集进行确定。用热化学表计算再生反应的焓。因此，通过限制从拟合中获得的参数数量，仅限那些不能从实验中直接测量的参数，所提出的模型的物理见解得到了增强。采用粒子群优化算法和同时预测烟灰氧化和热动力学的代价函数对模型参数进行识别。参数识别和模型验证使用独立的数据集从实验室实验进行了铈涂层GPF。研究表明，该模型可以成功地用于预测不同烟尘负荷和温度条件下氧化铈涂层GPF的动力学。
Gasoline particulate filters (GPFs) are practically adoptable devices to mitigate particulate matter emissions from vehicles using gasoline direct ignition engines. This paper presents a newly developed control-oriented model to characterize the thermal and soot oxidation dynamics in a ceria-coated GPF. The model utilizes the GPF inlet exhaust gas temperature, exhaust gas mass flow rate, the initial GPF soot loading density, and air- fuel ratio to predict the internal GPF temperature and the amount of soot oxidized during regeneration events. The reaction kinetics incorporated in the model involve the rates of both oxygen- and ceria-initiated soot oxidation reactions. Volumetric model parameters are calculated from the geometric information of the coated GPF, while the air-fuel ratio is used to determine the volume fractions of the exhaust gas constituents. The exhaust gas properties are evaluated using the volume fractions and thermodynamic tables, while the cordierite specific heat capacity is identified using a clean experimental data set. The enthalpies of the regeneration reactions are calculated using thermochemical tables. Physical insights from the proposed model are thus enhanced by limiting the number of parameters obtained from fitting to only those which cannot be directly measured from experiments. The parameters of the model are identified using the particle swarm optimization algorithm and a cost function designed to simultaneously predict both thermal and soot oxidation dynamics. Parameter identification and model validation are performed using independent data sets from laboratory experiments conducted on a ceria-coated GPF. This work demonstrates that the proposed model can be successfully implemented to predict ceria-coated GPF dynamics under different soot loading and temperature conditions.