Development of a semi-empirical physical model for transient NOx emissions prediction from a high-speed diesel engine
With emissions regulations becoming increasingly restrictive and the advent of real driving emissions limits, control of engine-out NOx emissions remains an important research topic for diesel engines. Progress in experimental engine development and computational modelling has led to the generation...
| Main Authors: | , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
SAGE Publications
2024
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| _version_ | 1824458736137142272 |
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| author | Bajwa, A Zou, G Zhong, F Fang, X Leach, F Davy, M |
| author_facet | Bajwa, A Zou, G Zhong, F Fang, X Leach, F Davy, M |
| author_sort | Bajwa, A |
| collection | OXFORD |
| description | With emissions regulations becoming increasingly restrictive and the advent of real driving emissions limits, control of
engine-out NOx emissions remains an important research topic for diesel engines. Progress in experimental engine
development and computational modelling has led to the generation of a large amount of high-fidelity emissions and
in-cylinder data, making it attractive to use data-driven emissions prediction and control models. While pure datadriven methods have shown robustness in NOx prediction during steady-state engine operation, deficiencies are found
under transient operation and at engine conditions far outside the training range. Therefore, physics-based, mean
value models that capture cyclic-level changes in in-cylinder thermo-chemical properties appear as an attractive option
for transient NOx emissions modelling. Previous experimental studies have highlighted the existence of a very strong
correlation between peak cylinder pressure and cyclic NOx emissions. In this study, a cyclic peak pressure-based
semi-empirical NOx prediction model is developed. The model is calibrated using high-speed NO and NO2 emissions
measurements during transient engine operation and then tested under different transient operating conditions. The
transient performance of the physical model is compared to that of a previously developed data-driven (artificial neural
network) model, and is found to be superior, with a better dynamic response and low (<10%) errors. The results shown
in this study are encouraging for the use of such models as virtual sensors for real-time emissions monitoring and as
complimentary models for future physics-guided neural network development. |
| first_indexed | 2024-09-25T04:11:08Z |
| format | Journal article |
| id | oxford-uuid:ced1b1aa-89a9-44c5-8a74-2d96c83dd9d3 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2025-02-19T04:30:37Z |
| publishDate | 2024 |
| publisher | SAGE Publications |
| record_format | dspace |
| spelling | oxford-uuid:ced1b1aa-89a9-44c5-8a74-2d96c83dd9d32024-12-19T08:58:39ZDevelopment of a semi-empirical physical model for transient NOx emissions prediction from a high-speed diesel engineJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:ced1b1aa-89a9-44c5-8a74-2d96c83dd9d3EnglishSymplectic ElementsSAGE Publications2024Bajwa, AZou, GZhong, FFang, XLeach, FDavy, MWith emissions regulations becoming increasingly restrictive and the advent of real driving emissions limits, control of engine-out NOx emissions remains an important research topic for diesel engines. Progress in experimental engine development and computational modelling has led to the generation of a large amount of high-fidelity emissions and in-cylinder data, making it attractive to use data-driven emissions prediction and control models. While pure datadriven methods have shown robustness in NOx prediction during steady-state engine operation, deficiencies are found under transient operation and at engine conditions far outside the training range. Therefore, physics-based, mean value models that capture cyclic-level changes in in-cylinder thermo-chemical properties appear as an attractive option for transient NOx emissions modelling. Previous experimental studies have highlighted the existence of a very strong correlation between peak cylinder pressure and cyclic NOx emissions. In this study, a cyclic peak pressure-based semi-empirical NOx prediction model is developed. The model is calibrated using high-speed NO and NO2 emissions measurements during transient engine operation and then tested under different transient operating conditions. The transient performance of the physical model is compared to that of a previously developed data-driven (artificial neural network) model, and is found to be superior, with a better dynamic response and low (<10%) errors. The results shown in this study are encouraging for the use of such models as virtual sensors for real-time emissions monitoring and as complimentary models for future physics-guided neural network development. |
| spellingShingle | Bajwa, A Zou, G Zhong, F Fang, X Leach, F Davy, M Development of a semi-empirical physical model for transient NOx emissions prediction from a high-speed diesel engine |
| title | Development of a semi-empirical physical model for transient NOx emissions prediction from a high-speed diesel engine |
| title_full | Development of a semi-empirical physical model for transient NOx emissions prediction from a high-speed diesel engine |
| title_fullStr | Development of a semi-empirical physical model for transient NOx emissions prediction from a high-speed diesel engine |
| title_full_unstemmed | Development of a semi-empirical physical model for transient NOx emissions prediction from a high-speed diesel engine |
| title_short | Development of a semi-empirical physical model for transient NOx emissions prediction from a high-speed diesel engine |
| title_sort | development of a semi empirical physical model for transient nox emissions prediction from a high speed diesel engine |
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