Refining Fuel Composition of RP-3 Chemical Surrogate Models by Reactive Molecular Dynamics and Machine Learning
文献类型：期刊论文
| 作者 | Han, Song1,3,4; Li, Xiaoxia1,3,4; Guo, Li1,3,4; Sun, Haiyun2; Zheng, Mo1,3,4; Ge, Wei1,3,4 |
| 刊名 | ENERGY & FUELS
 |
| 出版日期 | 2020-09-17 |
| 卷号 | 34期号:9页码:11381-11394 |
| ISSN号 | 0887-0624 |
| DOI | 10.1021/acs.energyfuels.0c01491 |
| 英文摘要 | A simple chemical surrogate fuel model may not be able to fully reproduce the chemical behavior in real fuel combustion. A structure-chemical reactivity relationship at the molecular level is believed to be useful in tuning the chemical composition of reported surrogate fuel models. This work proposes an approach to predict a component fraction of a RP-3 surrogate fuel model with a combined method of ReaxFF molecular dynamics (MD) simulations and machine learning. There are four major steps to get a refined surrogate fuel model on the basis of parent RP-3 fuel models with two, three, or four components. Step 1 helps to prepare chemical reactivity data as the input of machine learning. The chemical reactivity data are described by the oxidation reactions in terms of dynamic species concentration with time that were prepared with ReaxFF MD simulations for each derived fuel model generated from the RP-3 parent fuel model by randomly changing its component fraction. Step 2 fits a component fraction prediction model between a single surrogate component fraction and its chemical reactivity data among the common reaction space in oxidation of possible RP-3 surrogates and a 45-component RP-3 fuel with the machine-learning method. The best performance model of LightGBM was selected among the machine-learning models of linear regression, support vector regression, and LightGBM based on the training error evaluation. Each refined component fraction of RP-3 surrogate models was predicted one by one using the trained LightGBM model with the input of dynamic species concentration data of the 45-component RP-3 fuel model better representing real RP-3 fuel. Step 3 helps to select one refined surrogate model among the predicted surrogate models with two, three, or four components by comparing the chemical reactivity deviation of important species with additional ReaxFF MD simulations under the conditions of heat-up, isothermal oxidation, and isothermal pyrolysis. The optimal model of the refined three-component RP-3 surrogate is validated in step 4 where its predicted ignition delay time is found to be closer to the reported experimental data of the real RP-3 fuel than to its parent surrogate fuel model. This work suggests that the proposed computational approach by combining ReaxFF MD simulations and machine learning should be potentially useful in search for refined RP-3 chemical surrogate fuel models directly on the basis of chemical reactivity data in oxidation at the molecular level. |
| WOS关键词 | FORCE-FIELD ; AVIATION KEROSENE ; HIGH-TEMPERATURE ; THERMOPHYSICAL PROPERTIES ; INITIAL REACTIVITY ; SHOCK-TUBE ; REAXFF ; PYROLYSIS ; OXIDATION ; COMBUSTION |
| 资助项目 | China Postdoctoral Science Foundation[BX20180310] ; National Science and Technology Major Project[2017-III-0007-0032] ; National Natural Science Foundation of China[91641102] |
| WOS研究方向 | Energy & Fuels ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000574904900086 |
| 出版者 | AMER CHEMICAL SOC |
| 资助机构 | China Postdoctoral Science Foundation ; National Science and Technology Major Project ; National Natural Science Foundation of China |
| 源URL | [http://ir.ipe.ac.cn/handle/122111/42298]  |
| 专题 | 中国科学院过程工程研究所 |
| 通讯作者 | Li, Xiaoxia; Ge, Wei |
| 作者单位 | 1.Chinese Acad Sci, Inst Proc Engn, State Key Lab Multiphase Complex Syst, Beijing 100190, Peoples R China 2.Beijing Inst Aerosp Testing Technol, Beijing Key Lab Res & Applicat Aerosp Green Prope, Beijing 100074, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Innovat Acad Green Manufacture, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Han, Song,Li, Xiaoxia,Guo, Li,et al. Refining Fuel Composition of RP-3 Chemical Surrogate Models by Reactive Molecular Dynamics and Machine Learning[J]. ENERGY & FUELS,2020,34(9):11381-11394. |
| APA | Han, Song,Li, Xiaoxia,Guo, Li,Sun, Haiyun,Zheng, Mo,&Ge, Wei.(2020).Refining Fuel Composition of RP-3 Chemical Surrogate Models by Reactive Molecular Dynamics and Machine Learning.ENERGY & FUELS,34(9),11381-11394. |
| MLA | Han, Song,et al."Refining Fuel Composition of RP-3 Chemical Surrogate Models by Reactive Molecular Dynamics and Machine Learning".ENERGY & FUELS 34.9(2020):11381-11394. |
入库方式： OAI收割
来源：过程工程研究所
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