Considering the influence of hydrogen gas generated during electrochemical machining on the conductivity of electrolyte, a two-phase turbulent flow model is presented to describe the gas bubbles distribution.The k-e t...Considering the influence of hydrogen gas generated during electrochemical machining on the conductivity of electrolyte, a two-phase turbulent flow model is presented to describe the gas bubbles distribution.The k-e turbulent model is used to describe the electrolyte flow field.The Euler–Euler model based on viscous drag and pressure force is used to calculate the twodimensional distribution of gas volume fraction.A multi-physics coupling model of electric field,two-phase flow field and temperature field is established and solved by weak coupling iteration method.The numerical simulation results of gas volume fraction, temperature and conductivity in equilibrium state are discussed.The distributions of machining gap at different time are analyzed.The predicted results of the machining gap are consistent with the experimental results, and the maximum deviation between them is less than 50 lm.展开更多
The prediction of excitation band edge wavelength(EBEW)and peak emission wavelength(PEW)for Eu^(2+)-activated phosphors is intricate in practice,although a theoretical interpretation has been well established.A data-d...The prediction of excitation band edge wavelength(EBEW)and peak emission wavelength(PEW)for Eu^(2+)-activated phosphors is intricate in practice,although a theoretical interpretation has been well established.A data-driven approach could be of great help for EBEW and PEW prediction.We collected 91 Eu^(2+)-activated phosphors,the host structures of which exhibit a single activator site and the EBEW and PEW of which are available at the critical activator concentration.We extracted 29 descriptors(input features)that implicate the elemental and structural traits of phosphor hosts,and set up an integrated machine-learning(ML)platform consisting of 18 ML algorithms that allowed prediction of the EBEW and PEW as well as the DFT-calculated band gap(Eg).The acquired dataset involving 91 phosphors was insufficient for the 29-input-feature problem and the real-world data collected from the literature have a so-called dirty nature due to inaccurate,unstandardized experiments.Despite an unavoidable paucity of data and the dirty-data problems of real-world data-based ML implementation,we obtained acceptable holdout dataset test results for PEW predications such as R^(2)>0.6,MSE<0.02,and test_R^(2)/training_R^(2)>0.77 for four ML algorithms.The EBEW and E_(g)predictions returned slightly better test results than these PEW examples.展开更多
基金funded by the National Natural Science Foundation of China(Nos.51775161 and 51775158)。
文摘Considering the influence of hydrogen gas generated during electrochemical machining on the conductivity of electrolyte, a two-phase turbulent flow model is presented to describe the gas bubbles distribution.The k-e turbulent model is used to describe the electrolyte flow field.The Euler–Euler model based on viscous drag and pressure force is used to calculate the twodimensional distribution of gas volume fraction.A multi-physics coupling model of electric field,two-phase flow field and temperature field is established and solved by weak coupling iteration method.The numerical simulation results of gas volume fraction, temperature and conductivity in equilibrium state are discussed.The distributions of machining gap at different time are analyzed.The predicted results of the machining gap are consistent with the experimental results, and the maximum deviation between them is less than 50 lm.
基金supported by the Creative Materials Discovery Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT,and Future Planning(2015M3D1A1069705),(2021R1A2C1011642)and(2021R1A2C1009144)partly by the Alchemist Project(20012196)Digital manufacturing platform(N0002598)funded by MOTIE,Korea.
文摘The prediction of excitation band edge wavelength(EBEW)and peak emission wavelength(PEW)for Eu^(2+)-activated phosphors is intricate in practice,although a theoretical interpretation has been well established.A data-driven approach could be of great help for EBEW and PEW prediction.We collected 91 Eu^(2+)-activated phosphors,the host structures of which exhibit a single activator site and the EBEW and PEW of which are available at the critical activator concentration.We extracted 29 descriptors(input features)that implicate the elemental and structural traits of phosphor hosts,and set up an integrated machine-learning(ML)platform consisting of 18 ML algorithms that allowed prediction of the EBEW and PEW as well as the DFT-calculated band gap(Eg).The acquired dataset involving 91 phosphors was insufficient for the 29-input-feature problem and the real-world data collected from the literature have a so-called dirty nature due to inaccurate,unstandardized experiments.Despite an unavoidable paucity of data and the dirty-data problems of real-world data-based ML implementation,we obtained acceptable holdout dataset test results for PEW predications such as R^(2)>0.6,MSE<0.02,and test_R^(2)/training_R^(2)>0.77 for four ML algorithms.The EBEW and E_(g)predictions returned slightly better test results than these PEW examples.
