Gradient boosting decision tree(GBDT)machine learning(ML)method was adopted for the first time to automatically recognize and conduct quantitative statistical analysis of boundaries in bainitic microstructure using el...Gradient boosting decision tree(GBDT)machine learning(ML)method was adopted for the first time to automatically recognize and conduct quantitative statistical analysis of boundaries in bainitic microstructure using electron back-scatter diffraction(EBSD)data.In spite of lack of large sets of EBSD data,we were successful in achieving the desired accuracy and accomplishing the objective of recognizing the boundaries.Compared with a low model accuracy of<50%as using Euler angles or axis-angle pair as characteristic features,the accuracy of the model was significantly enhanced to about 88%when the Euler angle was converted to overall misorientation angle(OMA)and specific misorientation angle(SMA)and considered as important features.In this model,the recall score of prior austenite grain(PAG)boundary was~93%,high angle packet boundary(OMA>40°)was~97%,and block boundary was~96%.The derived outcomes of ML were used to obtain insights into the ductile-to-brittle transition(DBTT)behavior.Interestingly,ML modeling approach suggested that DBTT was not determined by the density of high angle grain boundaries,but significantly influenced by the density of PAG and packet boundaries.The study underscores that ML has a great potential in detailed recognition of complex multi-hierarchical microstructure such as bainite and martensite and relates to material performance.展开更多
A general numerical method is presented for calculating steady three-dimensional and two-phase turbulent reactive flows with a nonstaggered body-fitted coordinate system in an annular gas turbine combustor with the du...A general numerical method is presented for calculating steady three-dimensional and two-phase turbulent reactive flows with a nonstaggered body-fitted coordinate system in an annular gas turbine combustor with the dump diffuser. The modified two-equation model and the EDC turbulent combustion model are used for the gas phase. The liquid phase equations are solved in a Lagrangian frame of reference by PSIC algorithm. The effect of different velocity profiles at the entry of the prediffuser on combustor flow characteristics is calculated.展开更多
基金financially supported by the National Key Research and Development Program of China(No.2017YFB0304900)。
文摘Gradient boosting decision tree(GBDT)machine learning(ML)method was adopted for the first time to automatically recognize and conduct quantitative statistical analysis of boundaries in bainitic microstructure using electron back-scatter diffraction(EBSD)data.In spite of lack of large sets of EBSD data,we were successful in achieving the desired accuracy and accomplishing the objective of recognizing the boundaries.Compared with a low model accuracy of<50%as using Euler angles or axis-angle pair as characteristic features,the accuracy of the model was significantly enhanced to about 88%when the Euler angle was converted to overall misorientation angle(OMA)and specific misorientation angle(SMA)and considered as important features.In this model,the recall score of prior austenite grain(PAG)boundary was~93%,high angle packet boundary(OMA>40°)was~97%,and block boundary was~96%.The derived outcomes of ML were used to obtain insights into the ductile-to-brittle transition(DBTT)behavior.Interestingly,ML modeling approach suggested that DBTT was not determined by the density of high angle grain boundaries,but significantly influenced by the density of PAG and packet boundaries.The study underscores that ML has a great potential in detailed recognition of complex multi-hierarchical microstructure such as bainite and martensite and relates to material performance.
文摘A general numerical method is presented for calculating steady three-dimensional and two-phase turbulent reactive flows with a nonstaggered body-fitted coordinate system in an annular gas turbine combustor with the dump diffuser. The modified two-equation model and the EDC turbulent combustion model are used for the gas phase. The liquid phase equations are solved in a Lagrangian frame of reference by PSIC algorithm. The effect of different velocity profiles at the entry of the prediffuser on combustor flow characteristics is calculated.
