A joint moment projection method and maximum entropy approach for simulation of soot formation and oxidation in diesel engines

A joint moment projection method and maximum entropy approach for treating the soot population balance equations is developed and presented in this work. The moment projection method is used to solve the population balance equations and generate moments that are supplied to the maximum entropy appro...

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Main Authors: Wu, Shaohua, Lao, Chung Ting, Akroyd, Jethro, Mosbach, Sebastian, Yang, Wenming, Kraft, Markus
Other Authors: School of Chemical and Biomedical Engineering
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/152275
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1522752023-12-29T06:53:50Z A joint moment projection method and maximum entropy approach for simulation of soot formation and oxidation in diesel engines Wu, Shaohua Lao, Chung Ting Akroyd, Jethro Mosbach, Sebastian Yang, Wenming Kraft, Markus School of Chemical and Biomedical Engineering Engineering::Chemical engineering Soot Moment Projection Method A joint moment projection method and maximum entropy approach for treating the soot population balance equations is developed and presented in this work. The moment projection method is used to solve the population balance equations and generate moments that are supplied to the maximum entropy approach as a post-processing technique to reconstruct the soot particle size distribution. The particle size range required by the maximum entropy for particle size distribution reconstruction is determined based on the weighted particles generated in the moment projection method. The performance of the joint approach is first evaluated by solving a set of simplified population balance equations in MatLab, then it is implemented into a Stochastic Reactor Model engine code to simulate the formation and oxidation of soot particles in a single-cylinder direct injection diesel engine. Results suggest that the joint approach has the advantages of ease of implementation, high accuracy and low computational cost. It enables a detailed analysis on the soot formation and oxidation processes in diesel engines. Complete information on the soot particle size distribution can be provided with little CPU cost induced. National Research Foundation (NRF) Accepted version This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its CREATE programme. 2021-07-28T07:44:41Z 2021-07-28T07:44:41Z 2020 Journal Article Wu, S., Lao, C. T., Akroyd, J., Mosbach, S., Yang, W. & Kraft, M. (2020). A joint moment projection method and maximum entropy approach for simulation of soot formation and oxidation in diesel engines. Applied Energy, 258, 114083-. https://dx.doi.org/10.1016/j.apenergy.2019.114083 0306-2619 https://hdl.handle.net/10356/152275 10.1016/j.apenergy.2019.114083 2-s2.0-85075732542 258 114083 en Applied Energy © 2019 Elsevier Ltd. All rights reserved. This paper was published in Applied Energy and is made available with permission of Elsevier Ltd. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Chemical engineering
Soot
Moment Projection Method
spellingShingle Engineering::Chemical engineering
Soot
Moment Projection Method
Wu, Shaohua
Lao, Chung Ting
Akroyd, Jethro
Mosbach, Sebastian
Yang, Wenming
Kraft, Markus
A joint moment projection method and maximum entropy approach for simulation of soot formation and oxidation in diesel engines
description A joint moment projection method and maximum entropy approach for treating the soot population balance equations is developed and presented in this work. The moment projection method is used to solve the population balance equations and generate moments that are supplied to the maximum entropy approach as a post-processing technique to reconstruct the soot particle size distribution. The particle size range required by the maximum entropy for particle size distribution reconstruction is determined based on the weighted particles generated in the moment projection method. The performance of the joint approach is first evaluated by solving a set of simplified population balance equations in MatLab, then it is implemented into a Stochastic Reactor Model engine code to simulate the formation and oxidation of soot particles in a single-cylinder direct injection diesel engine. Results suggest that the joint approach has the advantages of ease of implementation, high accuracy and low computational cost. It enables a detailed analysis on the soot formation and oxidation processes in diesel engines. Complete information on the soot particle size distribution can be provided with little CPU cost induced.
author2 School of Chemical and Biomedical Engineering
author_facet School of Chemical and Biomedical Engineering
Wu, Shaohua
Lao, Chung Ting
Akroyd, Jethro
Mosbach, Sebastian
Yang, Wenming
Kraft, Markus
format Article
author Wu, Shaohua
Lao, Chung Ting
Akroyd, Jethro
Mosbach, Sebastian
Yang, Wenming
Kraft, Markus
author_sort Wu, Shaohua
title A joint moment projection method and maximum entropy approach for simulation of soot formation and oxidation in diesel engines
title_short A joint moment projection method and maximum entropy approach for simulation of soot formation and oxidation in diesel engines
title_full A joint moment projection method and maximum entropy approach for simulation of soot formation and oxidation in diesel engines
title_fullStr A joint moment projection method and maximum entropy approach for simulation of soot formation and oxidation in diesel engines
title_full_unstemmed A joint moment projection method and maximum entropy approach for simulation of soot formation and oxidation in diesel engines
title_sort joint moment projection method and maximum entropy approach for simulation of soot formation and oxidation in diesel engines
publishDate 2021
url https://hdl.handle.net/10356/152275
_version_ 1787136803440951296