In the Large Helical Device(LHD),diborane(B2H6)is used as a standard boron source for boronization,which is assisted by helium glow discharges.In 2019,a new Impurity Powder Dropper(IPD)system was installed and is unde...In the Large Helical Device(LHD),diborane(B2H6)is used as a standard boron source for boronization,which is assisted by helium glow discharges.In 2019,a new Impurity Powder Dropper(IPD)system was installed and is under evaluation as a real-time wall conditioning technique.In the LHD,which is a large-sized heliotron device,an additional helium(He)glow discharge cleaning(GDC)after boronization was operated for a reduction in hydrogen recycling from the coated boron layers.This operational time of 3 h was determined by spectroscopic data during glow discharges.A flat hydrogen profile is obtained on the top surface of the coated boron on the specimen exposed to boronization.The results suggest a reduction in hydrogen at the top surface by He-GDC.Trapped oxygen in coated boron was obtained by boronization,and the coated boron,which has boron-oxide,on the first wall by B-IPD was also shown.Considering the difference in coating areas between B2H6 boronization and B-IPD operation,it would be most effective to use the IPD and B2H6 boronization coating together for optimized wall conditioning.展开更多
Achieving the detachment of divertor can help to alleviate excessive heat load and sputtering problems on the target plates,thereby extending the lifetime of divertor components for fusion devices.In order to provide ...Achieving the detachment of divertor can help to alleviate excessive heat load and sputtering problems on the target plates,thereby extending the lifetime of divertor components for fusion devices.In order to provide a fast but relatively reliable prediction of plasma parameters along the flux tube for future device design,a one-dimensional(1D)modeling code for the operating point of impurity seeded detached divertor is developed based on Python language,which is a fluid model based on previous work(Plasma Phys.Control.Fusion 58045013(2016)).The experimental observation of the onset of divertor detachment by neon(Ne)and argon(Ar)seeding in EAST is well reproduced by using the 1D modeling code.The comparison between the 1D modeling and two-dimensional(2D)simulation by the SOLPS-ITER code for CFETR detachment operation with Ne and Ar seeding also shows that they are in good agreement.We also predict the radiative power loss and corresponding impurity concentration requirement for achieving divertor detachment via different impurity seeding under high heating power conditions in EAST and CFETR phase II by using the 1D model.Based on the predictions,the optimized parameter space for divertor detachment operation on EAST and CFETR is also determined.Such a simple but reliable 1D model can provide a reasonable parameter input for a detailed and accurate analysis by 2D or three-dimensional(3D)modeling tools through rapid parameter scanning.展开更多
By using the numerical renormalization group(NRG)method,we construct a large dataset with about one million spectral functions of the Anderson quantum impurity model.The dataset contains the density of states(DOS)of t...By using the numerical renormalization group(NRG)method,we construct a large dataset with about one million spectral functions of the Anderson quantum impurity model.The dataset contains the density of states(DOS)of the host material,the strength of Coulomb interaction between on-site electrons(U),and the hybridization between the host material and the impurity site(Γ).The continued DOS and spectral functions are stored with Chebyshev coefficients and wavelet functions,respectively.From this dataset,we build seven different machine learning networks to predict the spectral function from the input data,DOS,U,andΓ.Three different evaluation indexes,mean absolute error(MAE),relative error(RE)and root mean square error(RMSE),are used to analyze the prediction abilities of different network models.Detailed analysis shows that,for the two kinds of widely used recurrent neural networks(RNNs),gate recurrent unit(GRU)has better performance than the long short term memory(LSTM)network.A combination of bidirectional GRU(BiGRU)and GRU has the best performance among GRU,BiGRU,LSTM,and BiLSTM.The MAE peak of BiGRU+GRU reaches 0.00037.We have also tested a one-dimensional convolutional neural network(1DCNN)with 20 hidden layers and a residual neural network(ResNet),we find that the 1DCNN has almost the same performance of the BiGRU+GRU network for the original dataset,while the robustness testing seems to be a little weak than BiGRU+GRU when we test all these models on two other independent datasets.The ResNet has the worst performance among all the seven network models.The datasets presented in this paper,including the large data set of the spectral function of Anderson quantum impurity model,are openly available at https://doi.org/10.57760/sciencedb.j00113.00192.展开更多
The effects of impurities on ion temperature gradient(ITG)driven turbulence transport in tokamak core plasmas are investigated numerically via global simulations of microturbulence with carbon impurities and adiabatic...The effects of impurities on ion temperature gradient(ITG)driven turbulence transport in tokamak core plasmas are investigated numerically via global simulations of microturbulence with carbon impurities and adiabatic electrons.The simulations use an extended fluid code(ExFC)based on a four-field gyro-Landau-fluid(GLF)model.The multispecies form of the normalized GLF equations is presented,which guarantees the self-consistent evolution of both bulk ions and impurities.With parametric profiles of the cyclone base case,well-benchmarked ExFC is employed to perform simulations focusing on different impurity density profiles.For a fixed temperature profile,it is found that the turbulent heat diffusivity of bulk ions in a quasi-steady state is usually lower than that without impurities,which is contrary to the linear and quasilinear predictions.The evolutions of the temperature gradient and heat diffusivity exhibit a fast relaxation process,indicating that the destabilization of the outwardly peaked impurity profile is a transient state response.Furthermore,the impurity effects from different profiles can obviously influence the nonlinear critical temperature gradient,which is likely to be dominated by linear effects.These results suggest that the improvement in plasma confinement could be attributed to the impurities,most likely through adjusting both heat diffusivity and the critical temperature gradient.展开更多
基金supported by NIFS budgets,KOBF031,ULFF004,KUHR032partly supported by JSPS KAKENHI 18K04999+2 种基金JSPS-CAS Bilateral Joint Research Projects,“Control of wall recycling on metallic plasma-facing materials in fusion reactor”2019-2022,(No.GJHZ201984)the Chinese Academy of Sciences President’s International Fellowship Initiative Grant No.2024VMB0003 in FY2023the U.S.Department Of Energy under Contract No.DE-AC02-09CH11466 with Princeton University。
文摘In the Large Helical Device(LHD),diborane(B2H6)is used as a standard boron source for boronization,which is assisted by helium glow discharges.In 2019,a new Impurity Powder Dropper(IPD)system was installed and is under evaluation as a real-time wall conditioning technique.In the LHD,which is a large-sized heliotron device,an additional helium(He)glow discharge cleaning(GDC)after boronization was operated for a reduction in hydrogen recycling from the coated boron layers.This operational time of 3 h was determined by spectroscopic data during glow discharges.A flat hydrogen profile is obtained on the top surface of the coated boron on the specimen exposed to boronization.The results suggest a reduction in hydrogen at the top surface by He-GDC.Trapped oxygen in coated boron was obtained by boronization,and the coated boron,which has boron-oxide,on the first wall by B-IPD was also shown.Considering the difference in coating areas between B2H6 boronization and B-IPD operation,it would be most effective to use the IPD and B2H6 boronization coating together for optimized wall conditioning.
基金Project supported by the National Key Research and Development Program of China (Grant No.2022YFE03030001)the National Natural Science Foundation of China (Grant No.12075283)。
文摘Achieving the detachment of divertor can help to alleviate excessive heat load and sputtering problems on the target plates,thereby extending the lifetime of divertor components for fusion devices.In order to provide a fast but relatively reliable prediction of plasma parameters along the flux tube for future device design,a one-dimensional(1D)modeling code for the operating point of impurity seeded detached divertor is developed based on Python language,which is a fluid model based on previous work(Plasma Phys.Control.Fusion 58045013(2016)).The experimental observation of the onset of divertor detachment by neon(Ne)and argon(Ar)seeding in EAST is well reproduced by using the 1D modeling code.The comparison between the 1D modeling and two-dimensional(2D)simulation by the SOLPS-ITER code for CFETR detachment operation with Ne and Ar seeding also shows that they are in good agreement.We also predict the radiative power loss and corresponding impurity concentration requirement for achieving divertor detachment via different impurity seeding under high heating power conditions in EAST and CFETR phase II by using the 1D model.Based on the predictions,the optimized parameter space for divertor detachment operation on EAST and CFETR is also determined.Such a simple but reliable 1D model can provide a reasonable parameter input for a detailed and accurate analysis by 2D or three-dimensional(3D)modeling tools through rapid parameter scanning.
基金Project supported by the National Natural Science Foundation of China(Grant No.12174101)the Fundamental Research Funds for the Central Universities(Grant No.2022MS051)。
文摘By using the numerical renormalization group(NRG)method,we construct a large dataset with about one million spectral functions of the Anderson quantum impurity model.The dataset contains the density of states(DOS)of the host material,the strength of Coulomb interaction between on-site electrons(U),and the hybridization between the host material and the impurity site(Γ).The continued DOS and spectral functions are stored with Chebyshev coefficients and wavelet functions,respectively.From this dataset,we build seven different machine learning networks to predict the spectral function from the input data,DOS,U,andΓ.Three different evaluation indexes,mean absolute error(MAE),relative error(RE)and root mean square error(RMSE),are used to analyze the prediction abilities of different network models.Detailed analysis shows that,for the two kinds of widely used recurrent neural networks(RNNs),gate recurrent unit(GRU)has better performance than the long short term memory(LSTM)network.A combination of bidirectional GRU(BiGRU)and GRU has the best performance among GRU,BiGRU,LSTM,and BiLSTM.The MAE peak of BiGRU+GRU reaches 0.00037.We have also tested a one-dimensional convolutional neural network(1DCNN)with 20 hidden layers and a residual neural network(ResNet),we find that the 1DCNN has almost the same performance of the BiGRU+GRU network for the original dataset,while the robustness testing seems to be a little weak than BiGRU+GRU when we test all these models on two other independent datasets.The ResNet has the worst performance among all the seven network models.The datasets presented in this paper,including the large data set of the spectral function of Anderson quantum impurity model,are openly available at https://doi.org/10.57760/sciencedb.j00113.00192.
基金supported by National Natural Science Foundation of China(Nos.U1967206 and 12275071)National Key R&D Program of China(No.2017YFE0301201)。
文摘The effects of impurities on ion temperature gradient(ITG)driven turbulence transport in tokamak core plasmas are investigated numerically via global simulations of microturbulence with carbon impurities and adiabatic electrons.The simulations use an extended fluid code(ExFC)based on a four-field gyro-Landau-fluid(GLF)model.The multispecies form of the normalized GLF equations is presented,which guarantees the self-consistent evolution of both bulk ions and impurities.With parametric profiles of the cyclone base case,well-benchmarked ExFC is employed to perform simulations focusing on different impurity density profiles.For a fixed temperature profile,it is found that the turbulent heat diffusivity of bulk ions in a quasi-steady state is usually lower than that without impurities,which is contrary to the linear and quasilinear predictions.The evolutions of the temperature gradient and heat diffusivity exhibit a fast relaxation process,indicating that the destabilization of the outwardly peaked impurity profile is a transient state response.Furthermore,the impurity effects from different profiles can obviously influence the nonlinear critical temperature gradient,which is likely to be dominated by linear effects.These results suggest that the improvement in plasma confinement could be attributed to the impurities,most likely through adjusting both heat diffusivity and the critical temperature gradient.
文摘工程结构在制造工艺过程中或使用期间会产生裂纹,对结构断裂路径的预测和研究是防治工程安全问题发生的重要手段。在考虑裂纹尖端应力场常数项T应力的基础上对传统的最大周向应力准则(Maximum tangential stress criterion,MTS)和最小应变能密度因子准则(Minimum strain energy density criterion,SED)进行修正,采用Python语言对ABAQUS的前、后处理和有限元计算模块进行二次开发,通过计算最优解的粒子群算法(Particle swarm optimization,PSO)将修正后的准则编入裂纹自动扩展程序脚本中。利用上述二次开发程序对初始纯Ⅰ型裂纹的扩展路径进行模拟,结果表明:采用ABAQUS脚本程序模拟结果与相关文献实验结果吻合,表明了程序的有效性,进而实现考虑T应力的多种断裂准则对裂纹扩展路径的预测;当T应力值处于一定范围内时,修正的MTS准则无法预测裂纹发生的偏转现象,扩展路径呈直线,此时可采用修正的SED准则进行预测。