RP-3 Aviation Kerosene Surrogate Fuel and the Chemical Reaction Kinetic Model

Zheng, Dong; Yu, Weiming; Zhong, Benhe

doi:10.3866/pku.whxb201501231

Cited by 76 publications

(15 citation statements)

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“…These results indicate that this method is reliable to obtain the absorption index of liquid samples, while there exist deviations for the refractive index obtained 32,33 No. 3 92.1 0 7.9 12-15 Diesel 30,34 No.…”

Section: Verificationmentioning

confidence: 86%

Near infrared spectral radiation properties of different liquid hydrocarbon fuels

Sun

et al. 2017

Journal of Near Infrared Spectroscopy

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A double-thickness transmission model for a three-layer structure combined with a genetic algorithm was adopted to determine the spectral radiation properties of liquids. The reliability of this model was illustrated by the good agreement between the optical constants measured and that from the references. Then the spectral radiation properties of three typical liquid hydrocarbon fuels (diesel, gasoline, and jet fuel) available in China were measured in the wavelength range of 1000-2100 nm from room temperature to 423 K. Results indicate that the optical constants of these fuels are related to the fuel composition, wavelength, and temperature. At room temperature, the absorption index of the low carbon fuel (gasoline) was smaller than that for the high carbon fuels (diesel and jet fuel) in the wavelength range of 1100-1450 nm and 1700-2000 nm and larger in other bands. From room temperature to 423 K, the absorption index increased with temperature by a maximum rate of 50% per degree for the low carbon fuel, while it presents different trends for the high carbon fuel. The absorption index of the diesel decreased with temperature by a maximum rate of 40% per degree, while there exists a turning point for that of jet fuel at 1685 nm. For all these fuels, the temperature influence on the refractive index was not obvious, as the changing rate only reaches up to 3% per degree.

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Section: Verificationmentioning

confidence: 86%

Near infrared spectral radiation properties of different liquid hydrocarbon fuels

Sun

et al. 2017

Journal of Near Infrared Spectroscopy

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“…As shown in Figure , a set of derived surrogate models were formed by randomly changing the composition of a given parent surrogate model. Thirty derived surrogate models were created for each parent RP-3 surrogate fuel used in this work by randomly changing the composition while keeping the component chemical structure fixed as the parent RP-3 surrogate fuel. − A detailed composition of these derived surrogate models is shown in Table S1 of the Supporting Information.…”

Section: Methodsmentioning

confidence: 99%

“…Surrogate models of Chinese RP-3 fuel are widely used in studying RP-3 combustion behavior. Some of the representative RP-3 surrogate fuel models with typical component numbers of 2, 3, or 4 can well describe the specific combustion properties of real RP-3 fuels. − However, simple surrogate models of RP-3 fuel may not be able to well reproduce the combustion chemistry of real complicated RP-3 fuel because of the limited components, of which the chemical structures and composition are usually determined mainly based on some of the specific macroscopic properties of real fuel. The combustion chemistry of a surrogate depends on the chemical reactivity of component chemical structures and their blending ratio of the surrogate and combustion conditions.…”

Section: Introductionmentioning

confidence: 99%

“…This work attempts to develop a computational approach to refine the composition of surrogate RP-3 fuel models reported in the literature − by fitting a structure–chemical reactivity relationship model between the chemical reactivity of a chemical RP-3 surrogate and its component fraction as well as chemical structure with machine learning. The surrogate chemical reactivity data are described by representative RP-3 oxidation reactions in terms of species amount data with time (dynamic species concentration) generated from ReaxFF MD simulations.…”

Section: Introductionmentioning

confidence: 99%

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

Han

Guo

et al. 2020

Energy Fuels

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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 threecomponent 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.

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“…As an important characteristic parameter of fuel, laminar flame propagation speed can be used to characterize the chemical reaction kinetics of fuel [30,31].…”

Section: Verification Of Laminar Flame Propagation Speedmentioning

confidence: 99%

Mechanism Reduction and Bunsen Burner Flame Verification of Methane

Liu

Wang

et al. 2018

Energies

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Based on directed relation graph with error propagation methods, 39 species and 231 reactions skeletal mechanism were obtained from Mech_56.54 (113 species and 710 reactions) mechanism of methane. The ignition delay times, laminar flame propagation speed, and important species were calculated using the simplified mechanism at different pressures and equivalence ratios. The simulation results were in good agreement with that of detailed mechanisms and experimental data. The numerical simulation of the Bunsen burner jet flame was carried out using the simplified methane mechanism, and the simulation results well reproduced the temperature, flow fields and distribution of important species at flame zone. The compact methane reduced mechanism can not only correctly respond to its dynamic characteristics, but also can be well used for numerical simulation, which is of great significance in engineering applications.

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RP-3 Aviation Kerosene Surrogate Fuel and the Chemical Reaction Kinetic Model

Cited by 76 publications

References 0 publications

Near infrared spectral radiation properties of different liquid hydrocarbon fuels

Near infrared spectral radiation properties of different liquid hydrocarbon fuels

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

Mechanism Reduction and Bunsen Burner Flame Verification of Methane

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