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Author |
Sun, S.R.; Wang, H.X.; Bogaerts, A. |
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Title |
Chemistry reduction of complex CO2chemical kinetics: application to a gliding arc plasma |
Type |
A1 Journal article |
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Year  |
2020 |
Publication |
Plasma Sources Science & Technology |
Abbreviated Journal |
Plasma Sources Sci T |
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Volume |
29 |
Issue |
2 |
Pages |
025012 |
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Keywords |
A1 Journal article; Plasma Lab for Applications in Sustainability and Medicine – Antwerp (PLASMANT) |
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Abstract |
A gliding arc (GA) plasma has great potential for CO2 conversion into value-added chemicals, because of its high energy efficiency. To improve the application, a 2D/3D fluid model is needed to investigate the CO2 conversion mechanisms in the actual discharge geometry. Therefore, the complex CO2 chemical kinetics description must be reduced due to the huge computational cost associated with 2D/3D models. This paper presents a chemistry reduction method for CO2 plasmas, based on the so-called directed relation graph method. Depending on the defined threshold values, some marginal species are identified. By means of a sensitivity analysis, we can further reduce the chemistry set by removing one by one the marginal species. Based on the socalled flux-sensitivity coupling, we obtain a reduced CO2 kinetics model, consisting of 36 or 15 species (depending on whether the 21 asymmetric mode vibrational states of CO2 are explicitly included or lumped into one group), which is applied to a GA discharge. The results are compared with those predicted with the full chemistry set, and very good agreement is reached. Moreover, the range of validity of the reduced CO2 chemistry set is checked, telling us that this reduced set is suitable for low power GA discharges. Finally, the time and spatial evolution of the CO2 plasma characteristics are presented, based on a 2D model with the reduced kinetics. |
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Wos |
000525600600001 |
Publication Date |
2020-02-11 |
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ISSN |
1361-6595 |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
3.8 |
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Open Access |
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Notes |
We acknowledge financial support from the Fund for Scientific Research Flanders (FWO; Grant No. G.0383.16 N). The calculations were performed using the Turing HPC infrastructure at the CalcUA core facility of the Universiteit Antwerpen (UAntwerpen), a division of the Flemish Supercomputer Center VSC, funded by the Hercules Foundation, the Flemish Government (department EWI) and the UAntwerpen. This work was also supported by the National Natural Science Foundation of China. (Grant Nos. 11735004, 11575019). SR Sun thanks the financial support from the National Postdoctoral Program for Innovative Talents (BX20180029). |
Approved |
Most recent IF: 3.8; 2020 IF: 3.302 |
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Call Number |
PLASMANT @ plasmant @c:irua:167135 |
Serial |
6338 |
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