Název: Data-driven approach to estimating soot distribution inside catalytic filters in utomotive exhaust gas aftertreatment
Autoři: Khýr, Matyáš ; Plachá, M. ; Hlavatý, Tomáš ; Isoz, Martin
Typ dokumentu: Příspěvky z konference
Konference/Akce: Engineering mechanics 2024 /30./, Milovy (CZ), 20240514
Rok: 2024
Jazyk: eng
Abstrakt: The performance and the necessary regeneration frequency of catalytic filters (CFs) used in the treatment of automotive exhaust gases depend strongly on the solid matter accumulated within their porous walls. Reliable predictions of solid matter (soot) accumulation are crucial in the development and optimisation of CFs. In this contribution, we exploit the tools of artificial intelligence (AI) to estimate the distribution of soot directly in the porous microstructure of CFs. Specifically, our AI model uses deep neural networks (DNNs) and convolutional autoencoders (CAEs) to predict the soot distribution from information about the microstructure and the initial velocity field. To provide the model with training and validation data, we used our previously developed transient numerical model of particle deposition in the CF walls to calculate soot distribution in a dataset of artificial 2D geometries. The results of the developed AI model are in good agreement with simulation regarding the total amount of accumulated soot. However, the accuracy in the spatial distribution of the soot is not optimal, and consequently, using estimated particle deposits to simulate the pressure drop in\nthe artificial microstructure results in 35 % accuracy.
Klíčová slova: AI; catalytic filters; CFD; DNN; OpenFOAM
Číslo projektu: TN02000069
Poskytovatel projektu: GA TA ČR
Zdrojový dokument: Engineering Mechanics 2024, ISBN 978-80-214-6235-9, ISSN 1805-8248
Poznámka: Související webová stránka: https://www.engmech.cz/im/proceedings/show/2024

Instituce: Ústav termomechaniky AV ČR (web)
Informace o dostupnosti dokumentu: Dokument je dostupný v příslušném ústavu Akademie věd ČR.
Původní záznam: https://hdl.handle.net/11104/0361338

Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-669185