Missing values in radionuclide diffusion datasets can undermine the predictive accuracy and robustness of the machine learning(ML)models.In this study,regression-based missing data imputation method using a light grad...Missing values in radionuclide diffusion datasets can undermine the predictive accuracy and robustness of the machine learning(ML)models.In this study,regression-based missing data imputation method using a light gradient boosting machine(LGBM)algorithm was employed to impute more than 60%of the missing data,establishing a radionuclide diffusion dataset containing 16 input features and 813 instances.The effective diffusion coefficient(D_(e))was predicted using ten ML models.The predictive accuracy of the ensemble meta-models,namely LGBM-extreme gradient boosting(XGB)and LGBM-categorical boosting(CatB),surpassed that of the other ML models,with R^(2)values of 0.94.The models were applied to predict the D_(e)values of EuEDTA^(−)and HCrO_(4)^(−)in saturated compacted bentonites at compactions ranging from 1200 to 1800 kg/m^(3),which were measured using a through-diffusion method.The generalization ability of the LGBM-XGB model surpassed that of LGB-CatB in predicting the D_(e)of HCrO_(4)^(−).Shapley additive explanations identified total porosity as the most significant influencing factor.Additionally,the partial dependence plot analysis technique yielded clearer results in the univariate correlation analysis.This study provides a regression imputation technique to refine radionuclide diffusion datasets,offering deeper insights into analyzing the diffusion mechanism of radionuclides and supporting the safety assessment of the geological disposal of high-level radioactive waste.展开更多
A severe accident in a marine nuclear reactor leads to radionuclide leakage,which causes hidden dangers to workers and has adverse effects of environmental pollution.It is necessary to propose a novel approach to radi...A severe accident in a marine nuclear reactor leads to radionuclide leakage,which causes hidden dangers to workers and has adverse effects of environmental pollution.It is necessary to propose a novel approach to radionuclide diffusion in a confined environment after a severe accident in a marine nuclear reactor.Therefore,this study proposes a new method for the severe accident analysis program MELCOR coupled with computational fluid dynamics scSTREAM to study radioactive diffusion in severe accidents.The radionuclide release fraction and temperature calculated by MELCOR were combined with the scSTREAM calculations to study the radionuclide diffusion behavior and the phenomenon of radionuclide diffusion in different space environments of the reactor under the conditions of varying wind velocities of the ventilation system and diffusion speed.The results show that the wind velocity of the ventilation system is very small or zero,and the turbulent diffusion of radionuclides is not obvious and diffuses slowly in the form of condensation sedimentation and gravity settlement.When the wind speed of the ventilation system increases,the flow of radionuclides meets the wall and forms eddy currents,affecting the time variation of radionuclides diffusing into chamber 2.The wind velocity of the ventilation system and the diffusion speed has opposite effects on the time variation trend of radionuclide diffusion into the four chambers.展开更多
It needs long time to predict radioactive contaminant diffusion in receiving water under accident condition by using computational fluid dynamics （CFD） model. In order to shorten the computation time, a hybrid model...It needs long time to predict radioactive contaminant diffusion in receiving water under accident condition by using computational fluid dynamics （CFD） model. In order to shorten the computation time, a hybrid model based on CFD and time series neural network （TSNN） is proposed in this paper. The concentration change of radioactive contamination in an inland reservoir after a postulated accident is studied as a case. The result shows that this hybrid model can predict the contaminant diffusion trend and shorten at least 50% of iteration time. Priori knowledge integrated into the neural network model is able to reduce the mean square error of network output to 9.66×10 8 , which makes neural network output more close to the simulated contaminant concentration.展开更多
基金supported by the National Natural Science Foundation of China(No.12475340 and 12375350)Special Branch project of South Taihu Lakethe Scientific Research Fund of Zhejiang Provincial Education Department(No.Y202456326).
文摘Missing values in radionuclide diffusion datasets can undermine the predictive accuracy and robustness of the machine learning(ML)models.In this study,regression-based missing data imputation method using a light gradient boosting machine(LGBM)algorithm was employed to impute more than 60%of the missing data,establishing a radionuclide diffusion dataset containing 16 input features and 813 instances.The effective diffusion coefficient(D_(e))was predicted using ten ML models.The predictive accuracy of the ensemble meta-models,namely LGBM-extreme gradient boosting(XGB)and LGBM-categorical boosting(CatB),surpassed that of the other ML models,with R^(2)values of 0.94.The models were applied to predict the D_(e)values of EuEDTA^(−)and HCrO_(4)^(−)in saturated compacted bentonites at compactions ranging from 1200 to 1800 kg/m^(3),which were measured using a through-diffusion method.The generalization ability of the LGBM-XGB model surpassed that of LGB-CatB in predicting the D_(e)of HCrO_(4)^(−).Shapley additive explanations identified total porosity as the most significant influencing factor.Additionally,the partial dependence plot analysis technique yielded clearer results in the univariate correlation analysis.This study provides a regression imputation technique to refine radionuclide diffusion datasets,offering deeper insights into analyzing the diffusion mechanism of radionuclides and supporting the safety assessment of the geological disposal of high-level radioactive waste.
基金supported by the Postgraduate Scientific Research Innovation Project of Hunan Province (No. CX20210922)
文摘A severe accident in a marine nuclear reactor leads to radionuclide leakage,which causes hidden dangers to workers and has adverse effects of environmental pollution.It is necessary to propose a novel approach to radionuclide diffusion in a confined environment after a severe accident in a marine nuclear reactor.Therefore,this study proposes a new method for the severe accident analysis program MELCOR coupled with computational fluid dynamics scSTREAM to study radioactive diffusion in severe accidents.The radionuclide release fraction and temperature calculated by MELCOR were combined with the scSTREAM calculations to study the radionuclide diffusion behavior and the phenomenon of radionuclide diffusion in different space environments of the reactor under the conditions of varying wind velocities of the ventilation system and diffusion speed.The results show that the wind velocity of the ventilation system is very small or zero,and the turbulent diffusion of radionuclides is not obvious and diffuses slowly in the form of condensation sedimentation and gravity settlement.When the wind speed of the ventilation system increases,the flow of radionuclides meets the wall and forms eddy currents,affecting the time variation of radionuclides diffusing into chamber 2.The wind velocity of the ventilation system and the diffusion speed has opposite effects on the time variation trend of radionuclide diffusion into the four chambers.
基金Supported by the National Natural Science Foundation of China(51339004,71171151)
文摘It needs long time to predict radioactive contaminant diffusion in receiving water under accident condition by using computational fluid dynamics （CFD） model. In order to shorten the computation time, a hybrid model based on CFD and time series neural network （TSNN） is proposed in this paper. The concentration change of radioactive contamination in an inland reservoir after a postulated accident is studied as a case. The result shows that this hybrid model can predict the contaminant diffusion trend and shorten at least 50% of iteration time. Priori knowledge integrated into the neural network model is able to reduce the mean square error of network output to 9.66×10 8 , which makes neural network output more close to the simulated contaminant concentration.
