Refining Fuel Composition of RP-3 Chemical Surrogate Models by Reactive Molecular Dynamics and Machine Learning,Energy & Fuels

当前位置： X-MOL 学术 › Energy Fuels › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Refining Fuel Composition of RP-3 Chemical Surrogate Models by Reactive Molecular Dynamics and Machine Learning
Energy & Fuels ( IF 5.2 ) Pub Date : 2020-08-25 , DOI: 10.1021/acs.energyfuels.0c01491
Song Han _{1,

2,

3} , Xiaoxia Li _{1,

2,

3} , Li Guo _{1,

2,

3} , Haiyun Sun ₄ , Mo Zheng _{1,

2,

3} , Wei Ge _{1,

2,

3}

Affiliation

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.

中文翻译：

通过反应分子动力学和机器学习精炼RP-3化学替代模型的燃料成分

一个简单的化学替代燃料模型可能无法完全再现真实燃料燃烧中的化学行为。据信在分子水平上的结构-化学反应性关系可用于调节已报道的替代燃料模型的化学组成。这项工作提出了一种通过ReaxFF分子动力学（MD）模拟和机器学习相结合的方法来预测RP-3替代燃料模型的组成部分的方法。在具有两个，三个或四个组成部分的母体RP-3燃料模型的基础上，有四个主要步骤来获得精确的替代燃料模型。步骤1帮助准备化学反应性数据作为机器学习的输入。化学反应性数据是通过ReaxFF MD模拟针对随RP-3母体燃料模型生成的每个衍生燃料模型通过随机改变其组分分数而用动态物种浓度随时间变化的氧化反应来描述的。第2步采用机器学习方法，在单个替代组分分数与其在可能的RP-3替代物和45组分RP-3燃料的氧化反应中的公共反应空间中的化学反应性数据之间拟合了组分分数预测模型。根据训练误差评估，从线性回归，支持向量回归和LightGBM的机器学习模型中选择了LightGBM的最佳性能模型。使用训练有素的LightGBM模型逐一预测RP-3替代模型的每个精炼组分分数，并输入能更好地代表实际RP-3燃料的45组分RP-3燃料模型的动态物质浓度数据。步骤3通过在加热，等温氧化和等温热解条件下将重要物种的化学反应性偏差与其他ReaxFF MD模拟进行比较，从而帮助您在具有两个，三个或四个成分的预测替代模型中选择一个精细的替代模型。。在第4步中验证了精制三组分RP-3替代物的最佳模型，在该模型中，预测的点火延迟时间比其父代替代燃料模型更接近于报告的实际RP-3燃料实验数据。

更新日期：2020-09-18

点击分享查看原文

点击收藏

阅读更多本刊新发论文本刊介绍/投稿指南