Numerical Analysis of Artificial Electron Heating Effects on Polar Mesospheric Winter Echoes

In this paper,an analytical model is used to analyze the modulated polar mesospheric winter echoes(PMWE).The winter parameters were introduced to simulate the effects of different parameters during the artificial electron heating of PMWE.The important role of the charged dust particle in the creation of PMWE is confirmed again.It is found that during the heating of PMWE,the increases of the dust size,dust charge,electron temperature,initial electron density,and ion-neutral collision frequency cause the increase of the electron density irregularity,and hence the PMWE strength.However,with increasing the dust density,the electron density irregularity and the PMWE strength decrease.

On the radar frequency dependence of polar mesosphere summer echoes

Polar mesosphere summer echoes(PMSEs)are very strong radar echoes in the polar mesopause in local summer.Here we present the frequency dependence of the volume reflectivity and the effect of energetic particle precipitation on modulated PMSEs by using PMSEs observations carried out by European Incoherent SCATter(EISCAT)heating equipment simultaneously with very high frequency(VHF)radar and ultra high frequency(UHF)radar on 12 July 2007.According to the experimental observations,the PMSEs occurrence rate at VHF was much higher than that at UHF,and the altitude of the PMSEs maximum observed at VHF was higher than that at UHF.Overlapping regions were observed by VHF radar between high energetic particle precipitation and the PMSEs.In addition,highfrequency heating had a very limited impact on PMSEs when the UHF electron density was enhanced because of energetic particle precipitation.In addition,an updated qualitative method was used to study the relationship between volume reflectivity and frequency.The volume reflectivity was found to be inversely proportional to the fourth power of radar frequency.The theoretical and experimental results provide a definitive data foundation for further analysis and investigation of the physical mechanism of PMSEs.

Modeling effects of alloying elements and heat treatment parameters on mechanical properties of hot die steel with back-propagation artificial neural network

Materials data deep-excavation is very important in materials genome exploration.In order to carry out materials data deep-excavation in hot die steels and obtain the relationships among alloying elements,heat treatment parameters and materials properties,a 11×12×12×4 back-propagation（BP）artificial neural network（ANN）was set up.Alloying element contents,quenching and tempering temperatures were selected as input;hardness,tensile and yield strength were set as output parameters.The ANN shows a high fitting precision.The effects of alloying elements and heat treatment parameters on the properties of hot die steel were studied using this model.The results indicate that high temperature hardness increases with increasing alloying element content of C,Si,Mo,W,Ni,V and Cr to a maximum value and decreases with further increase in alloying element content.The ANN also predicts that the high temperature hardness will decrease with increasing quenching temperature,and possess an optimal value with increasing tempering temperature.This model provides a new tool for novel hot die steel design.