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Modeling of soot fragmentation that proceeds in a catalyzed diesel particulate filter of a diesel engine

机译:柴油发动机催化柴油颗粒过滤器中进行的烟灰片段的建模

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Catalyzed diesel particulate filters (CDPFs) are crucial technologies to control the particulate emissions from diesel engines. While obvious drops in filtration efficiencies (FEs) for particle number (PN) occur during the passive regeneration of CDPFs, which can lead to the deterioration of transient emissions. Therefore, in this study, a quasi-2-D model, including a fluid dynamic model, a back-diffusion model, a global kinetic model and a basic filtration model, was established for describing the PN-filtration-behavior of a CDPF. An extended filtration model was developed based on the soot fragmentation hypothesis, considering the effects of the particle diameter, soot load, exhaust gas temperature and flow velocity. An 8.8L, heavy duty diesel engine equipped with a commercial DOC and a CDPF was employed. The particle number and size distributions before and after the CDPF were determined with an engine exhaust particle sizer (EEPS) under eight operating conditions. The extended filtration model was calibrated with the test data, and the model results aligned with those of experiments with a maximal error less than 5% and an average error less than 1%. Then, the undetermined parameters in the extended filtration model were evaluated under the eight working conditions, and distinct regularities were observed among these parameters. The time-dependent PN-filtration-behavior of the tested CDPF was also explored based on the model. According to the model predictions, it was found that the PN-filtration-behavior of a CDPF was different from that of a DPF, and the PN-FE of a CDPF might be still unsatisfactory under the high-soot-load conditions.
机译:催化的柴油颗粒过滤器(CDPF)是控制柴油发动机的微粒排放的重要技术。虽然在CDPF的被动再生期间发生粒子数(PN)的过滤效率(FES)中的明显下降,这可能导致瞬态排放的恶化。因此,在本研究中,建立了一种用于描述CDPF的PN过滤行为的基准2-D模型,包括流体动态模型,反扩散模型,全局动力学模型和基本过滤模型。考虑到粒径,烟灰荷载,废气温度和流速的影响,基于烟灰碎片假设开发了扩展过滤模型。采用8.8L,配备商业DOC和CDPF的重型柴油发动机。在八个操作条件下用发动机排气粒子Sizer(EEPS)测定CDPF之前和之后的粒子数和尺寸分布。使用测试数据校准扩展过滤模型,模型结果与具有小于5%的最大误差和小于1%的平均误差对齐的模型结果。然后,在八个工作条件下评估延伸过滤模型中未确定的参数,并且在这些参数之间观察到不同的规则。还基于模型探讨了测试的CDPF的时间依赖性PN过滤行为。根据模型预测,发现CDPF的PN过滤行为与DPF的PN过滤行为不同,并且CDPF的PN-FE可能在高烟尘载荷条件下仍然不令人满意。

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