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タイトル: Numerical simulation of bi-component fuel droplet evaporation using Level Set method
著者: Muramatsu, Hiroki
Pillai, Abhishek L.
Kitada, Kenya
Kurose, Ryoichi  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0003-0377-6480 (unconfirmed)
著者名の別形: 村松, 宏起
北田, 絢也
黒瀬, 良一
キーワード: Bi-component fuel droplet evaporation
Eulerian framework
Level Set method
発行日: Jun-2022
出版者: Elsevier BV
誌名: Fuel
巻: 318
論文番号: 123331
抄録: Fuel droplets consisting of multiple components are known to exhibit complex evaporation behaviour. The Eulerian framework employed in Computational Fluid Dynamics (CFD) which treats the droplets as a continuum, is effective for simulating the complex evaporation behaviour of such multicomponent droplets. In this study, the evaporation of binary-component or bi-component fuel droplets (which consist of two chemical species) in air, is simulated using the Eulerian framework with an extended evaporation model and its validity is examined. Spatio-temporal evolution of the gas-liquid interface is captured using the Level Set method, and a Ghost Fluid method is incorporated to consider the jump conditions at the interface. The computed evaporation speed of a fuel droplet consisting of a mixture of n-heptane and n-decane is compared with that of a previous experiment. Results show that n-heptane evaporates preferentially and the total evaporation speed is in an overall good agreement with that in the experiment. This confirms that the numerical framework used in the present simulation is capable of capturing the evaporation phenomenon of a bi-component fuel droplet. Additionally, several parametric simulations with different initial compositions (i.e., mass ratio of the constituent chemical species) of the bi-component fuel droplet and different initial ambient temperatures are performed, to investigate their influences on the evaporation characteristics of bi-component fuel droplet. The applicability of the proposed extended evaporation model and the numerical framework under these various simulation conditions are verified.
著作権等: © 2022. This manuscript version is made available under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license.
The full-text file will be made open to the public on 15 June 2024 in accordance with publisher's 'Terms and Conditions for Self-Archiving'.
This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。
URI: http://hdl.handle.net/2433/269398
DOI(出版社版): 10.1016/j.fuel.2022.123331
出現コレクション:学術雑誌掲載論文等

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