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Design of High Performance Coated GPF with 2D/3D Structure Analysis
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 02, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
In recent years along with stringent the regulations, vehicles equipped with gasoline particulate filter (GPF) have started to launch. Compared to bare GPF, coated GPF (cGPF) requires not only PN filtration efficiency, low pressure drop, but also purification performance. In the wall flow type cGPF having a complicated the pore shape, the pore structure further irregularly changes depending on the coated state of the catalyst, so it is difficult to understand the matter of in-wall. In order to advance of cGPF function, it was researched that revealing the relevance between pore structure change in the wall and GPF function. Therefore, to understand the catalyst coated state difference, cGPF of several coating methods were prepared, and their properties were evaluated by various analyses, and performance was tested. First of all, as a result of Mercury porosimetry analysis revealed that the pore diameter of the filter wall of GPF is a key factor for the pressure drop and the Particulate Number filtration efficiency. Next, as a result of analyzing the 3D model created by the μX-ray computed tomography image, it was found that the uniformity of the catalyst coat is a key factor for the purification performance. On the other hand, from images showing catalyst coated state of cGPF using electron probe micro analyzer, it was possible to quantify and evaluated uniform of catalyst coated state, by 2D digital image analysis. From the above those studies, cGPF could become to be designed with the best balance of low pressure drop, high purification performance and high PN filtration efficiency.
CitationSeki, C., Watanabe, T., Mori, T., Furukawa, A. et al., "Design of High Performance Coated GPF with 2D/3D Structure Analysis," SAE Technical Paper 2019-01-0977, 2019, https://doi.org/10.4271/2019-01-0977.
Data Sets - Support Documents
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