Quantitative Method of the Structural Damage Identification of Gas Explosion Based on Case Study:The Shanxi “11. 23” Explosion Investigation

In order to present a retrospective analysis of exposition accidents using input data from investigation processes,data from a specific accident was examined,in which we analyzed possible involved gas species（ liquefied petroleum gas; nature gas） and computed their concentrations and distributions based on the interactions between the structures and the effects of the explosion. In this study,5 scenarios were created to analyze the impact effect. Moreover,a coupling algorithm was put into practice,with a practical outflow boundary and joint strength are applied. Finally,the damage effects of each scenario were simulated. Our experimental results showed significant differences in the 5 scenarios concerning the damage effects on the building structures. The results from scenario 3 agree with the accident characteristics,demonstrating the effectiveness of our proposed modeling method. Our proposed method reflects gas properties,species and the concentration and distribution,and the simulated results validates the root cause,process,and consequences of accidental explosions. Furthermore,this method describes the evolution process of explosions in different building structures. Significantly,our model demonstrates the quantatative explosion effect of factors like gas species,gas volumes,and distributions of gases on explosion results. In this study,a feasible,effective,and quantitative method for structure safety is defined,which is helpful to accelerate the development of safer site regulations.

Large eddy simulation of spray combustion using flamelet generated manifolds combined with artificial neural networks

In the present work,artificial neural networks(ANN)technique combined with flamelet generated manifolds(FGM)is proposed to mitigate the memory issue of FGM models.A set of ANN models is firstly trained using a 68-species mass fractions in mixture fraction-progress variable space.The ANN prediction accuracy is examined in large eddy simulation(LES)and Reynolds averaged Navier-Stokes(RANS)simulations of spray combustion.It is shown that the present ANN models can properly replicate the FGM table for most of the species mass fractions.The network models with relative error less than 5%are considered in RANS and LES to simulate the Engine Combustion Network(ECN)Spray H flames.Validation of the method is firstly conducted in the framework of RANS.Both non-reacting and reacting cases show the present method predicts very well the trend of spray and combustion process under different ambient temperatures.The results show that FGM-ANN can replicate the ignition delay time(IDT)and lift-off length(LOL)precisely as the conventional FGM method,and the results agree very well with the experiments.With the help of ANN,it is possible to achieve high efficiency and accuracy,with a significantly reduced memory requirement of the FGM models.LES with FGM-ANN is then applied to explore the detailed spray combustion process.Chemical explosive mode analysis(CEMA)approach is used to identify the local combustion modes.It is found that before the spray flame is developed to the steady-state,the high CH_(2)O zone is always associated with ignition mode.However,high CH_(2)O zone together with high OH zone is dominated by the burned mode after the steady-state.The lift-off position is dominated mainly by the diffusion mode.