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
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
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