Mixing Rules for an Exact Determination of the Dielectric Properties of Engine Soot Using the Microwave Cavity Perturbation Method and Its Application in Gasoline Particulate Filters.

dielectric properties finite element method (FEM) gasoline particulate filter (GPF) microwave cavity perturbation mixing rule radio-frequency (RF) soot mass determination

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
26 Apr 2022
Historique:
received: 29 03 2022
revised: 20 04 2022
accepted: 21 04 2022
entrez: 20 5 2022
pubmed: 21 5 2022
medline: 24 5 2022
Statut: epublish

Résumé

In recent years, particulate filters have become mandatory in almost all gasoline-powered vehicles to comply with emission standards regarding particulate number. In contrast to diesel applications, monitoring gasoline particulate filters (GPFs) by differential pressure sensors is challenging due to lower soot masses to be deposited in the GPFs. A different approach to determine the soot loading of GPFs is a radio frequency-based sensor (RF sensor). To facilitate sensor development, in previous work, a simulation model was created to determine the RF signal at arbitrary engine operating points. To ensure accuracy, the exact dielectric properties of the soot need to be known. This work has shown how small samples of soot-loaded filter are sufficient to determine the dielectric properties of soot itself using the microwave cavity perturbation method. For this purpose, mixing rules were determined through simulation and measurement, allowing the air and substrate fraction of the sample to be considered. Due to the different geometry of filter substrates compared to crushed soot samples, a different mixing rule had to be derived to calculate the effective filter properties required for the simulation model. The accuracy of the determined mixing rules and the underlying simulation model could be verified by comparative measurements on an engine test bench.

Identifiants

pubmed: 35591000
pii: s22093311
doi: 10.3390/s22093311
pmc: PMC9101221
pii:
doi:

Substances chimiques

Air Pollutants 0
Gasoline 0
Particulate Matter 0
Soot 0
Vehicle Emissions 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Bavarian Research Foundation
ID : AZ-1288-17

Références

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pubmed: 31444115
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pubmed: 26340629
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pubmed: 33114027
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pubmed: 25211199
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Auteurs

Stefanie Walter (S)

Bayreuth Engine Research Center (BERC), Department of Functional Materials, University of Bayreuth, 95447 Bayreuth, Germany.

Peter Schwanzer (P)

Ostbayerische Technische Hochschule Regensburg, 93053 Regensburg, Germany.

Carsten Steiner (C)

Bayreuth Engine Research Center (BERC), Department of Functional Materials, University of Bayreuth, 95447 Bayreuth, Germany.

Gunter Hagen (G)

Bayreuth Engine Research Center (BERC), Department of Functional Materials, University of Bayreuth, 95447 Bayreuth, Germany.

Hans-Peter Rabl (HP)

Ostbayerische Technische Hochschule Regensburg, 93053 Regensburg, Germany.

Markus Dietrich (M)

Vitesco Technologies, 93055 Regensburg, Germany.

Ralf Moos (R)

Bayreuth Engine Research Center (BERC), Department of Functional Materials, University of Bayreuth, 95447 Bayreuth, Germany.

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Classifications MeSH