Tungsten,a leading candidate for plasma-facing materials(PFM) in future fusion devices,will be exposed to high-flux low-energy helium plasma under the anticipated fusion operation conditions.In the past two decades,ex...Tungsten,a leading candidate for plasma-facing materials(PFM) in future fusion devices,will be exposed to high-flux low-energy helium plasma under the anticipated fusion operation conditions.In the past two decades,experiments have revealed that exposure to helium plasma strongly modifies the surface morphology and hence the sputtering,thermal and other properties of tungsten,posing a serious danger to the performance and lifetime of tungsten and the steadystate operation of plasma.In this article,we provide a review of modeling and simulation efforts on the long-term evolution of helium bubbles,surface morphology,and property changes of tungsten exposed to low-energy helium plasma.The current gap and outstanding challenges to establish a predictive modeling capability for dynamic evolution of PFM are discussed.展开更多
Thermal conductivity of material is one of the basic physical properties and plays an important role in manipu-lating thermal energy.In order to accelerate the prediction of material structure with desired thermal pro...Thermal conductivity of material is one of the basic physical properties and plays an important role in manipu-lating thermal energy.In order to accelerate the prediction of material structure with desired thermal property,machine learning algorithm has been widely adopted.However,in the optimization of multivariable material structure such as different lengths or proportions,the machine learning algorithm may be required to be recon-ducted again and again for each variable,which will consume a lot of computing resources.Recently,it has been found that the thermal conductivity of aperiodic superlattices is closely related to the degree of the structural ran-domness,which can also be reflected in their local pattern structures.Inspired by these,we propose a new pattern analysis method,in which machine learning only needs to be carried out for one time,and through which the optimal structure of different variables with low thermal conductivity can be obtained.To verify the method,we compare the thermal conductivities of the structure obtained by pattern analysis,conventional machine learning,and previous literature,respectively.The pattern analysis method is validated to greatly reduce the prediction time of multivariable structure with high enough accuracy and may promote further development of material informatics.展开更多
基金supported by National Natural Science Foundation of China(No.11905071)the National MCF Energy R&D Program(No.2018YFE0308103)
文摘Tungsten,a leading candidate for plasma-facing materials(PFM) in future fusion devices,will be exposed to high-flux low-energy helium plasma under the anticipated fusion operation conditions.In the past two decades,experiments have revealed that exposure to helium plasma strongly modifies the surface morphology and hence the sputtering,thermal and other properties of tungsten,posing a serious danger to the performance and lifetime of tungsten and the steadystate operation of plasma.In this article,we provide a review of modeling and simulation efforts on the long-term evolution of helium bubbles,surface morphology,and property changes of tungsten exposed to low-energy helium plasma.The current gap and outstanding challenges to establish a predictive modeling capability for dynamic evolution of PFM are discussed.
基金This work was supported by National Natural Science Foundation of China(52076087)the Ministry of Science and Technology of the People’s Republic of China(2017YFE0100600)Wuhan City Science and Technology Program(2020010601012197).
文摘Thermal conductivity of material is one of the basic physical properties and plays an important role in manipu-lating thermal energy.In order to accelerate the prediction of material structure with desired thermal property,machine learning algorithm has been widely adopted.However,in the optimization of multivariable material structure such as different lengths or proportions,the machine learning algorithm may be required to be recon-ducted again and again for each variable,which will consume a lot of computing resources.Recently,it has been found that the thermal conductivity of aperiodic superlattices is closely related to the degree of the structural ran-domness,which can also be reflected in their local pattern structures.Inspired by these,we propose a new pattern analysis method,in which machine learning only needs to be carried out for one time,and through which the optimal structure of different variables with low thermal conductivity can be obtained.To verify the method,we compare the thermal conductivities of the structure obtained by pattern analysis,conventional machine learning,and previous literature,respectively.The pattern analysis method is validated to greatly reduce the prediction time of multivariable structure with high enough accuracy and may promote further development of material informatics.
