Artificial intelligence for simulation of soot distribution inside porous filter walls

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/60461373:22340/24:43930900 RIV/68407700:21220/24:00381503

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Artificial intelligence for simulation of soot distribution inside porous filter walls

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Artificial intelligence for simulation of soot distribution inside porous filter walls

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

<a href="/cs/project/GA22-12227S" target="_blank" >GA22-12227S: Počítačový návrh katalytických filtrů zohledňující vliv zachycených částic</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Topical Problems of Fluid Mechanics

ISBN

978-80-87012-88-8

ISSN

2336-5781

e-ISSN

—

Počet stran výsledku

8

Strana od-do

85-92

Název nakladatele

Institute of Thermomechanics AS CR, v. v. i.

Místo vydání

Prague

Místo konání akce

Prague

Datum konání akce

21. 2. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001242655400012