| DOI | Resolve DOI: https://doi.org/10.1016/j.icheatmasstransfer.2021.105640 |
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| Author | Search for: Sun, Yujia; Search for: Zheng, Shu; Search for: Liu, Fengshan1 |
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| Affiliation | - National Research Council of Canada. Metrology Research Centre
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| Funder | Search for: Nanjing University of Information Science and Technology; Search for: National Natural Science Foundation of China; Search for: Natural Science Foundation of Anhui Province |
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| Format | Text, Article |
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| Subject | gas radiation; particle; non-gray radiation; soot |
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| Abstract | Thermal radiation is an important heat transfer mode in flames and combustion systems. Non-gray gas and soot radiation is one of the most important heat feedback mechanisms to the preheating of fuel particles. The Discrete Ordinates Method (DOM) is used to solve the radiative transfer equation and the statistical Narrow Band k-distribution (SNBCK) method is used to model the non-gray radiation to investigate the radiative heat flux imposed on a spherical fuel particle immersed in high-temperature media. A range of gas temperature, pressure, gas volume size, mole fraction of radiating gas species and soot volume fraction are considered. Based on the datasets from the DOM-SNBCK model, two predictive models are developed by function fitting and neural networks. The function fitting model explicitly accounts for the dependence of radiative heat flux on the fitting parameters, but suffers from noticeable errors. The neural network results are in great agreement with the DOM-SNBCK results and can be used to estimate the non-gray gas and soot radiation heat transfer to spherical fuel particles. |
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| Publication date | 2021-10-07 |
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| Publisher | Elsevier |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Identifier | S0735193321005339 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 79f54675-fd8c-4cb7-a662-c98e49705e21 |
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| Record created | 2023-02-20 |
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| Record modified | 2023-02-20 |
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