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Predictive 3D-CFD Model for the Analysis of the Development of Soot Deposition Layer on Sensor Surfaces R and D CFD Srl

SAE Technical Papers (1906-current) Available online

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Format:
Book
Conference/Event
Author/Creator:
D'Orrico, Fabrizio, author.
Contributor:
Breda, Sebastiano
Cicalese, Giuseppe
Cozza, Ivan
Fontanesi, Stefano
Gopalakrishnan, Venkatesh
Tosi, Sergio
Conference Name:
16th International Conference on Engines & Vehicles (2023-09-10 : Capri, Italy)
Language:
English
Physical Description:
1 online resource cm
Place of Publication:
Warrendale, PA SAE International 2023
Summary:
After-treatment sensors are used in the ECU feedback control to calibrate the engine operating parameters. Due to their contact with exhaust gases, especially NOx sensors are prone to soot deposition with a consequent decay of their performance. Several phenomena occur at the same time leading to sensor contamination: thermophoresis, unburnt hydrocarbons condensation and eddy diffusion of submicron particles. Conversely, soot combustion and shear forces may act in reducing soot deposition. This study proposes a predictive 3D-CFD model for the analysis of the development of soot deposition layer on the sensor surfaces. Alongside with the implementation of deposit and removal mechanisms, the effects on both thermal properties and shape of the surfaces are taken in account. The latter leads to obtain a more accurate and complete modelling of the phenomenon influencing the sensor overall performance. The evolution of the fouling thickness is evaluated by means of the implementation of a morphing and remesh procedure based on the local conditions of both the flow and the pollutant concentration. The proposed model was tested on actual sensors by means of accelerated contamination cycles. The sensor behavior was correlated to the experimental response time to account for the decay of performance due to fouling accumulation. The response time is calculated both in the middle of the contamination cycle and at its end. Comparing the experimental data with the CFD results an error lower than the 9% is obtained
Notes:
Vendor supplied data
Publisher Number:
2023-24-0012
Access Restriction:
Restricted for use by site license

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