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Modeling of the Soot Oxidation in Gasoline Particulate Filters

机译:汽油颗粒过滤器中烟尘氧化的建模

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摘要

The share of gasoline engines based on direct injection (DI) technology is rapidly growing, to a large extend driven by their improved efficiency and potential to lower CO_2 emissions. One downside of these advanced engines are their significantly higher particulate emissions compared to engines based on port fuel injection technologies [1]. Gasoline particulate filters (GPF) are one potential technology path to address the EU6 particulate number regulation for vehicles powered by gasoline DI engines. For the robust design and operation of GPFs it is essential to understand the mechanisms of soot accumulation and oxidation under typical operating conditions. In this paper we will first discuss the use of detailed numerical simulation to describe the soot oxidation in particulate filters under typical gasoline engine operating conditions. Laboratory experiments are used to establish a robust set of soot oxidation kinetics. The filter model and oxidation kinetics are validated using a large set of experimental data from laboratory reactor as well as vehicle experiments. With the validated model and parameters the impact of key operating parameters will be discussed, with special focus on passive soot oxidation mechanisms under normal vehicle operating conditions. Results show that short oxygen pulses during fuel cuts are the dominant passive regeneration mechanism. Results also provide insight into potential "worst case" conditions. Based on the insight from the detailed model a reduced OD model is developed and described, which could be used for real time soot estimation.
机译:基于直喷(DI)技术的汽油发动机的份额正在迅速增长,很大程度上是由于其提高的效率和降低CO_2排放的潜力。这些先进发动机的缺点之一是,与基于港口燃料喷射技术的发动机相比,它们的微粒排放量明显更高[1]。汽油微粒过滤器(GPF)是解决由汽油DI发动机驱动的车辆的EU6微粒数量法规的一种潜在技术途径。对于GPF的稳健设计和操作,必须了解典型操作条件下烟灰累积和氧化的机理。在本文中,我们将首先讨论使用详细的数值模拟来描述典型汽油机运行条件下颗粒过滤器中烟灰的氧化。实验室实验用于建立一套可靠的烟ot氧化动力学。过滤器模型和氧化动力学使用来自实验室反应堆的大量实验数据以及车辆实验进行了验证。使用经过验证的模型和参数,将讨论关键操作参数的影响,并将重点放在正常车辆操作条件下的被动碳烟氧化机理上。结果表明,在燃油切断期间短的氧气脉冲是主要的被动再生机理。结果还提供了对潜在“最坏情况”条件的洞察力。基于对详细模型的了解,开发并描述了减少的OD模型,该模型可用于实时烟灰估计。

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