Artificial intelligence for simulation of soot distribution inside porous filter walls
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388998%3A_____%2F24%3A00584646" target="_blank" >RIV/61388998:_____/24:00584646 - isvavai.cz</a>
Alternative codes found
RIV/60461373:22340/24:43930900 RIV/68407700:21220/24:00381503
Result on the web
<a href="http://www2.it.cas.cz/fm/im/im/proceeding/2024/12" target="_blank" >http://www2.it.cas.cz/fm/im/im/proceeding/2024/12</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14311/TPFM.2024.012" target="_blank" >10.14311/TPFM.2024.012</a>
Alternative languages
Result language
angličtina
Original language name
Artificial intelligence for simulation of soot distribution inside porous filter walls
Original language description
The ability to estimate the influence of the accumulated solid matter on the performance of catalytic filters (CFs) in automotive exhaust gas aftertreatment leads to the ability to estimate the required filter regeneration frequency. The ability to perform such estimates fast and only from CFs microstructural data would allow for CFs microstructure optimization. In this work, we present an approach to estimate the distribution of soot deposited in the porous structure of CFs walls. The approach leverages methods of artificial intelligence (AI) and is based on convolutional autoencoders and deep neural networks. The resulting method is trained and tested on an artificial dataset that corresponds to a single pore in the CF wall. The dataset is prepared using our previously developed transient pore-scale model of particle deposit formation in the 3D microstructure of the catalytic filter wall. The developed AI model yields good results in terms of total amount of accumulated soot, but is less accurate in its distribution. As a result, using the estimated particle deposits to calculate the pressure drop and filtration efficiency of the artificial pore allows to estimate these two CF performancenindicators with 33.6 % accuracy.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
<a href="/en/project/GA22-12227S" target="_blank" >GA22-12227S: Computer-aided design of catalytic filters considering the impact of trapped particulate matter</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Topical Problems of Fluid Mechanics
ISBN
978-80-87012-88-8
ISSN
2336-5781
e-ISSN
—
Number of pages
8
Pages from-to
85-92
Publisher name
Institute of Thermomechanics AS CR, v. v. i.
Place of publication
Prague
Event location
Prague
Event date
Feb 21, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001242655400012