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Modelling the Influence of Different Soot Types on the Radio-Frequency-Based Load Detection of Gasoline Particulate Filters
Gasoline particulate filters (GPFs) are an appropriate means to meet today’s emission standards. As for diesel applications, GPFs can be monitored via differential pressure sensors or using a radio-frequency approach (RF sensor). Due to largely differing soot properties and engine operating modes of...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248741/ https://www.ncbi.nlm.nih.gov/pubmed/32384796 http://dx.doi.org/10.3390/s20092659 |
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author | Walter, Stefanie Schwanzer, Peter Hagen, Gunter Haft, Gerhard Rabl, Hans-Peter Dietrich, Markus Moos, Ralf |
author_facet | Walter, Stefanie Schwanzer, Peter Hagen, Gunter Haft, Gerhard Rabl, Hans-Peter Dietrich, Markus Moos, Ralf |
author_sort | Walter, Stefanie |
collection | PubMed |
description | Gasoline particulate filters (GPFs) are an appropriate means to meet today’s emission standards. As for diesel applications, GPFs can be monitored via differential pressure sensors or using a radio-frequency approach (RF sensor). Due to largely differing soot properties and engine operating modes of gasoline compared to diesel engines (e.g., the possibility of incomplete regenerations), the behavior of both sensor systems must be investigated in detail. For this purpose, extensive measurements on engine test benches are usually required. To simplify the sensor development, a simulation model was developed using COMSOL Multiphysics(®) that not only allowed for calculating the loading and regeneration process of GPFs under different engine operating conditions but also determined the impact on both sensor systems. To simulate the regeneration behavior of gasoline soot accurately, an oxidation model was developed. To identify the influence of different engine operating points on the sensor behavior, various samples generated at an engine test bench were examined regarding their kinetic parameters using thermogravimetric analysis. Thus, this compared the accuracy of soot mass determination using the RF sensor with the differential pressure method. By simulating a typical driving condition with incomplete regenerations, the effects of the soot kinetics on sensor accuracy was demonstrated exemplarily. Thereby, the RF sensor showed an overall smaller mass determination error, as well as a lower dependence on the soot kinetics. |
format | Online Article Text |
id | pubmed-7248741 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-72487412020-08-13 Modelling the Influence of Different Soot Types on the Radio-Frequency-Based Load Detection of Gasoline Particulate Filters Walter, Stefanie Schwanzer, Peter Hagen, Gunter Haft, Gerhard Rabl, Hans-Peter Dietrich, Markus Moos, Ralf Sensors (Basel) Article Gasoline particulate filters (GPFs) are an appropriate means to meet today’s emission standards. As for diesel applications, GPFs can be monitored via differential pressure sensors or using a radio-frequency approach (RF sensor). Due to largely differing soot properties and engine operating modes of gasoline compared to diesel engines (e.g., the possibility of incomplete regenerations), the behavior of both sensor systems must be investigated in detail. For this purpose, extensive measurements on engine test benches are usually required. To simplify the sensor development, a simulation model was developed using COMSOL Multiphysics(®) that not only allowed for calculating the loading and regeneration process of GPFs under different engine operating conditions but also determined the impact on both sensor systems. To simulate the regeneration behavior of gasoline soot accurately, an oxidation model was developed. To identify the influence of different engine operating points on the sensor behavior, various samples generated at an engine test bench were examined regarding their kinetic parameters using thermogravimetric analysis. Thus, this compared the accuracy of soot mass determination using the RF sensor with the differential pressure method. By simulating a typical driving condition with incomplete regenerations, the effects of the soot kinetics on sensor accuracy was demonstrated exemplarily. Thereby, the RF sensor showed an overall smaller mass determination error, as well as a lower dependence on the soot kinetics. MDPI 2020-05-06 /pmc/articles/PMC7248741/ /pubmed/32384796 http://dx.doi.org/10.3390/s20092659 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Walter, Stefanie Schwanzer, Peter Hagen, Gunter Haft, Gerhard Rabl, Hans-Peter Dietrich, Markus Moos, Ralf Modelling the Influence of Different Soot Types on the Radio-Frequency-Based Load Detection of Gasoline Particulate Filters |
title | Modelling the Influence of Different Soot Types on the Radio-Frequency-Based Load Detection of Gasoline Particulate Filters |
title_full | Modelling the Influence of Different Soot Types on the Radio-Frequency-Based Load Detection of Gasoline Particulate Filters |
title_fullStr | Modelling the Influence of Different Soot Types on the Radio-Frequency-Based Load Detection of Gasoline Particulate Filters |
title_full_unstemmed | Modelling the Influence of Different Soot Types on the Radio-Frequency-Based Load Detection of Gasoline Particulate Filters |
title_short | Modelling the Influence of Different Soot Types on the Radio-Frequency-Based Load Detection of Gasoline Particulate Filters |
title_sort | modelling the influence of different soot types on the radio-frequency-based load detection of gasoline particulate filters |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248741/ https://www.ncbi.nlm.nih.gov/pubmed/32384796 http://dx.doi.org/10.3390/s20092659 |
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