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Retrieval Temperature And Soot Concentration Of Methane Flames Based On Spectral Experiments

Posted on:2019-06-09Degree:MasterType:Thesis
Country:ChinaCandidate:J Y HanFull Text:PDF
GTID:2322330542991138Subject:Thermal Engineering
Abstract/Summary:PDF Full Text Request
Natural gas is the most realistic energy source for optimizing and adjusting the energy structure.Under the condition of a large increase in natural gas consumption,studying natural gas combustion characteristics and optimizing natural gas combustion will support energy conservation and emission reduction policies.In many industrial applications,the combustion of natural gas is jet-fired.It is of great academic value and engineering application to study the radiation characteristics of natural gas jet combustion and to reconstruct the temperature field based on the inverse of the radiation propagation in the flame direction and to reconstruct the radiative Soot distribution.In this paper,the combustion and radiation characteristics of methane coaxial jet flame studied respectively by numerical simulation and experimental methods.In order to predict more accurately the results of the flame flow field,the temperature field and the component concentration field in the flame numerical simulation,the eddy dissipation concept(EDC)model combined with the methane combustion to simplify the reaction mechanism.The use of simplified reaction mechanism can improve the computational efficiency basised of better simulation accuracy of component concentration field.Path Flux Analysis(PFA)can comprehensively consider the direct and indirect relationship of the combination or elementary reaction in the simplification of the chemical reaction mechanism,and keep the main components and elementary reactions to ensure the simulation accuracy.In this paper,the detailed reaction mechanism of methane combustion simplified by PFA method,and the simplified mechanism sp24 obtained.The simulation results of homogeneous combustion model coincide well with the simulation results of the detailed mechanism,and the ignition delay of the two under different conditions.Ignition delay time is not much difference,the error values are less than 5%.Using FLUENT software to simulate the co-axial jet methane flame Sandia flame D,respectively,using the probability density function(PDF)model and EDC model combined with simplified mechanism sp24 simulation.The comparison between the calculated results and the experimental data shows that there is not much difference between the PDF model and the EDC model for predicting the temperature field of the flame,while the EDC model predicts the concentration field of the flame component closer to the experimental data.In the aspect of experimental research,an experiment system of methane coaxial jet flame spectrometry was set up,and the reconstruction of the temperature distribution and the strong radiation concentration distribution of soot based on inverse flame propagation was realized.An adaptive particle swarm optimization(APSO)algorithm based on diversity feedback combined with coaxial jeted flame radiation model established to optimize the distribution of flame temperature and soot concentration by flame emission spectroscopy.Then,the experimental data of the literature(temperature and soot concentration)substituted into the model for simulation calculation,the validity of the intelligent optimization algorithm verified,and then the influence of parameter selection on calculation efficiency and accuracy discussed.Under the condition of ensuring the accuracy of calculation,the parameters with higher computational efficiency selected.Then,the information of the flame emission spectra of the experimental methane coaxial jet substituted into the algorithm for calculation.Comparing the calculated temperature and soot concentration distribution with the numerical simulation results at four flame cross sections,the relative errors are about 10%.
Keywords/Search Tags:Methane jet flame, Path flux analysis method, Combustion numerical simulation, Inversion radiative problem, Swarm intelligence optimization algorithm
PDF Full Text Request
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